diff --git a/AE.ipynb b/AE.ipynb
index 012fb8a..5a04589 100644
--- a/AE.ipynb
+++ b/AE.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,91 +272,31 @@
       "Trainable params: 218,514\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n",
-      "Epoch 1/40\n",
-      "235/235 [==============================] - 2s 10ms/step - loss: 0.0744 - val_loss: 0.0589\n",
-      "Epoch 2/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0560 - val_loss: 0.0533\n",
-      "Epoch 3/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0515 - val_loss: 0.0494\n",
-      "Epoch 4/40\n",
-      "235/235 [==============================] - 2s 7ms/step - loss: 0.0487 - val_loss: 0.0474\n",
-      "Epoch 5/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0469 - val_loss: 0.0460\n",
-      "Epoch 6/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0458 - val_loss: 0.0452\n",
-      "Epoch 7/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0450 - val_loss: 0.0446\n",
-      "Epoch 8/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0444 - val_loss: 0.0441\n",
-      "Epoch 9/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0439 - val_loss: 0.0437\n",
-      "Epoch 10/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0434 - val_loss: 0.0433\n",
-      "Epoch 11/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0430 - val_loss: 0.0429\n",
-      "Epoch 12/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0427 - val_loss: 0.0426\n",
-      "Epoch 13/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0423 - val_loss: 0.0423\n",
-      "Epoch 14/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0419 - val_loss: 0.0419\n",
-      "Epoch 15/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0416 - val_loss: 0.0416\n",
-      "Epoch 16/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0413 - val_loss: 0.0415\n",
-      "Epoch 17/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0410 - val_loss: 0.0411\n",
-      "Epoch 18/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0408 - val_loss: 0.0411\n",
-      "Epoch 19/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0407 - val_loss: 0.0409\n",
-      "Epoch 20/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0404 - val_loss: 0.0406\n",
-      "Epoch 21/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0403 - val_loss: 0.0406\n",
-      "Epoch 22/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0401 - val_loss: 0.0405\n",
-      "Epoch 23/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0399 - val_loss: 0.0403\n",
-      "Epoch 24/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0397 - val_loss: 0.0403\n",
-      "Epoch 25/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0396 - val_loss: 0.0402\n",
-      "Epoch 26/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0395 - val_loss: 0.0400\n",
-      "Epoch 27/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0393 - val_loss: 0.0399\n",
-      "Epoch 28/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0393 - val_loss: 0.0398\n",
-      "Epoch 29/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0391 - val_loss: 0.0398\n",
-      "Epoch 30/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0390 - val_loss: 0.0397\n",
-      "Epoch 31/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0389 - val_loss: 0.0395\n",
-      "Epoch 32/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0388 - val_loss: 0.0394\n",
-      "Epoch 33/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0387 - val_loss: 0.0393\n",
-      "Epoch 34/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0386 - val_loss: 0.0393\n",
-      "Epoch 35/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0386 - val_loss: 0.0393\n",
-      "Epoch 36/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0385 - val_loss: 0.0392\n",
-      "Epoch 37/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0384 - val_loss: 0.0391\n",
-      "Epoch 38/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0383 - val_loss: 0.0391\n",
-      "Epoch 39/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0382 - val_loss: 0.0392\n",
-      "Epoch 40/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0382 - val_loss: 0.0390\n"
+      "Epoch 1/10\n",
+      "235/235 [==============================] - 1s 6ms/step - loss: 0.0771 - val_loss: 0.0588\n",
+      "Epoch 2/10\n",
+      "235/235 [==============================] - 1s 5ms/step - loss: 0.0561 - val_loss: 0.0535\n",
+      "Epoch 3/10\n",
+      "235/235 [==============================] - 1s 5ms/step - loss: 0.0518 - val_loss: 0.0500\n",
+      "Epoch 4/10\n",
+      "235/235 [==============================] - 1s 4ms/step - loss: 0.0495 - val_loss: 0.0483\n",
+      "Epoch 5/10\n",
+      "235/235 [==============================] - 1s 5ms/step - loss: 0.0479 - val_loss: 0.0470\n",
+      "Epoch 6/10\n",
+      "235/235 [==============================] - 1s 5ms/step - loss: 0.0467 - val_loss: 0.0462\n",
+      "Epoch 7/10\n",
+      "235/235 [==============================] - 1s 5ms/step - loss: 0.0459 - val_loss: 0.0454\n",
+      "Epoch 8/10\n",
+      "235/235 [==============================] - 1s 6ms/step - loss: 0.0452 - val_loss: 0.0449\n",
+      "Epoch 9/10\n",
+      "235/235 [==============================] - 1s 5ms/step - loss: 0.0446 - val_loss: 0.0444\n",
+      "Epoch 10/10\n",
+      "235/235 [==============================] - 1s 6ms/step - loss: 0.0441 - val_loss: 0.0440\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 720x720 with 2 Axes>"
       ]
@@ -366,7 +306,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 720x216 with 12 Axes>"
       ]
@@ -378,7 +318,7 @@
     },
     {
      "data": {
-      "image/png": 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lWDuHMSbZ3HjjjXznO9/h6KOP7pNSRG+kxDPHp0yZovvzIKelG6o57zdvcNcVkzl73JA4RGaMSVYffPABRx55ZKLD6DedHa+ILFDVfa7ttRJHNzpKHFZVZYwxHSxxdKMw27dxWFWVMcZ0sMTRjaxQgMz0NCtxGGNMBEscPSjKDrFjd+fXRxtjTCqyxNGDQuuvyhhj9mKJowdFOSG7j8MYYyLYneM9KMwOsW5HfaLDMMakkO3btzNt2jQANm/eTCAQoP2BdO+++y6hUPc9WcyZM4dQKMQJJ5wQl/gscfSgKCdkjePGmH7VU7fqPZkzZw65ublxSxxWVdWDwuwQNY2ttLSFEx2KMSaFLViwgFNPPZXJkydz9tlns2nTJgDuuOMOxo4dy4QJE5gxYwZr167l97//PbfffjuTJk3i9ddf7/NYrMTRg6Ic1/3wrvoWSvIyeljbGHNQevFm2Lykb/c5ZDx85mcxraqqXH/99TzzzDOUlJTw+OOP873vfY97772Xn/3sZ6xZs4aMjAx27dpFQUEB11xzTa9LKb1hiaMHhRH9VVniMMYkQlNTE0uXLuXMM88EoK2tjaFDhwIwYcIELrvsMi644AIuuOCCfonHEkcPirKth1xjUl6MJYN4UVXGjRvHW2+9tc+y559/nrlz5/Lss8/y4x//mCVL+rhk1Im4tnGIyDki8qGIVIjIzZ0szxCRx/3yd0SkzM+/TEQWRgxhEZnkl00WkSV+mztEJK5PWCq0/qqMMQmWkZFBVVVVR+JoaWlh2bJlhMNh1q9fz+mnn85tt91GdXU1dXV15OXlUVtbG7d44pY4RCQA/Bb4DDAWuFRExkatdhWwU1VHAbcDtwGo6p9UdZKqTgKuANao6kK/ze+ArwGj/XBOvI4B9nR0aP1VGWMSJS0tjSeffJKbbrqJiRMnMmnSJN58803a2tq4/PLLGT9+PEcffTTf+MY3KCgo4Pzzz+fpp58+IBvHjwUqVHU1gIg8BkwHlkesMx2Y6cefBO4UEdG9+3q/FHjM72MokK+qb/vpB4ELgBfjdRAF2a5x3EocxphEmDlzZsf43Llz91n+xhtv7DNvzJgxLF68OG4xxbOqahiwPmK60s/rdB1VbQWqgeKodS4BHo1Yv7KHfQIgIleLyHwRmV9VVbVfBwCQEQyQEwpYf1XGGOMl9X0cIjIVqFfVpb3dVlXvVtUpqjql/Y7L/WX9VRljzB7xTBwbgOER06V+XqfriEgQGABsj1g+gz2ljfb1S3vYZ5+z/qqMSU2p8IRU6P1xxjNxzANGi0i5iIRwSWBW1DqzgCv9+IXA7Pb2DRFJAy7Gt28AqOomoEZEjvNXU30JeCaOxwC4u8etxGFMasnMzGT79u0HffJQVbZv305mZmbM28StcVxVW0XkOuBlIADcq6rLRORWYL6qzgLuAR4SkQpgBy65tDsFWN/euB7h34H7gSxco3jcGsbbFeWEWL2tLt5/xhiTREpLS6msrOSTtJEeKDIzMyktLe15RS+uNwCq6gvAC1HzfhAx3ghc1MW2c4DjOpk/HziqTwPtQWF2iJ3WOG5MSklPT6e8vDzRYSSlpG4cTxZFOenUNbXS1NqW6FCMMSbhLHHEoP3u8V31VuowxhhLHDGw/qqMMWYPSxwxsP6qjDFmD0scMbD+qowxZg9LHDEozLYShzHGtLPEEYP2jg6tvypjjLHEEZP0QBp5mUG7e9wYY7DEETPrr8oYYxxLHDGy/qqMMcaxxBEjK3EYY4xjiSNGhdkhu3PcGGOwxBGzopx0K3EYYwyWOGJWmBOioaWNhmbr6NAYk9osccSovb8qayA3xqQ6Sxwxau+vyqqrjDGpzhJHjNr7q7IShzEm1VniiFGhda1ujDFAnBOHiJwjIh+KSIWI3NzJ8gwRedwvf0dEyiKWTRCRt0RkmYgsEZFMP3+O3+dCPwyK5zG0K7Ku1Y0xBojjM8dFJAD8FjgTqATmicgsVV0esdpVwE5VHSUiM4DbgEtEJAg8DFyhqotEpBiIvIniMv/s8X4zICsdEdhh93IYY1JcPEscxwIVqrpaVZuBx4DpUetMBx7w408C00REgLOAxaq6CEBVt6tqQq+DDaQJA7LSrcRhjEl58Uwcw4D1EdOVfl6n66hqK1ANFANjABWRl0XkPRG5MWq7+3w11S0+0exDRK4WkfkiMr+qqqovjoei7JA9zMkYk/KStXE8CJwEXOZfPy8i0/yyy1R1PHCyH67obAeqereqTlHVKSUlJX0SVGFOyEocxpiUF8/EsQEYHjFd6ud1uo5v1xgAbMeVTuaq6jZVrQdeAI4BUNUN/rUWeARXJdYvXA+51sZhjElt8Uwc84DRIlIuIiFgBjArap1ZwJV+/EJgtqoq8DIwXkSyfUI5FVguIkERGQggIunAecDSOB7DXopyrI3DGGPidlWVqraKyHW4JBAA7lXVZSJyKzBfVWcB9wAPiUgFsAOXXFDVnSLyS1zyUeAFVX1eRHKAl33SCACvAn+I1zFEK8xxbRyqShdNK8YYc9CLW+IAUNUXcNVMkfN+EDHeCFzUxbYP4y7JjZy3G5jc95HGpig7RHNrmPrmNnIy4nrqjDEmaSVr43hSsv6qjDHGEkevWA+5xhhjiaNXrMRhjDGWOHrFesg1xpgYEoeIXC8ihf0RTLIr6ugh1+7lMMakrlhKHINxHRQ+4Xu7TdnrUPMygwTSxO7lMMaktB4Th6p+HxiNu+fiy8BKEfmJiBwW59iSTlqaUJCVbv1VGWNSWkxtHP5u7s1+aAUKgSdF5OdxjC0pWX9VxphU1+NdbCJyA/AlYBvwR+DbqtoiImnASiC659qDWlF2yK6qMsaktFhufy4CvqCqH0fOVNWwiJwXn7CSV2FOOmu31Sc6DGOMSZhYqqpexPUjBYCI5IvIVABV/SBegSWrohx7JocxJrXFkjh+B9RFTNf5eSmpMNu1cbhmH2OMST2xJA7RiG9JVQ0T584Rk1lRTojWsFLb1JroUIwxJiFiSRyrReQbIpLuhxuA1fEOLFkVtvdXZQ3kxpgUFUviuAY4Afe0vkpgKnB1PINKZkXWX5UxJsX1WOWkqlvxD1gyezo6tP6qjDGpKpb7ODKBq4BxQGb7fFX9ahzjSlrWX5UxJtXFUlX1EDAEOBt4DSgFauMZVDIrzEkHrI3DGJO6Ykkco1T1FmC3qj4AfBbXzpGScjOCpAfE7uUwxqSsWBJHe53MLhE5ChgADIpl57433Q9FpEJEbu5keYaIPO6XvyMiZRHLJojIWyKyTESW+CozRGSyn64QkTv6u7deEaEg2/qrMsakrlgSx93+eRzfB2YBy4HbetpIRALAb4HPAGOBS0VkbNRqVwE7VXUUcHv7fkUkCDwMXKOq44DT2JPAfgd8Dddj72jgnBiOoU9Zf1XGmFTWbeLwHRnWqOpOVZ2rqoeq6iBVvSuGfR8LVKjqalVtBh4DpketMx14wI8/CUzzJYizgMWqughAVberapuIDAXyVfVtf1Pig8AFMR5rnynMSWdXvTWOG2NSU7eJw98lvr+93w4D1kdMV/p5na6jqq1ANVAMjAFURF4WkfdE5MaI9St72CcAInK1iMwXkflVVVX7eQids/6qjDGpLJaqqldF5FsiMlxEitqHOMcVBE4CLvOvnxeRab3ZgarerapTVHVKSUlJnwZXaG0cxpgUFkufU5f4169HzFPg0B622wAMj5gu9fM6W6fSt2sMALbjShJzVXUbgIi8AByDa/co7WGfcVeUE2JnfTPhsJKWlrJP0jXGpKhYHh1b3snQU9IAmAeMFpFyEQnh7j6fFbXOLOBKP34hMNu3XbwMjBeRbJ9QTgWWq+omoEZEjvNtIV8CnonpSPtQYXaIsEJNo7VzGGNSTyx3jn+ps/mq+mB326lqq4hch0sCAeBeVV0mIrcC81V1Fu455g+JSAXumR8z/LY7ReSXuOSjwAuq+rzf9b8D9wNZuGeFvNjjUfaxyP6qCvyd5MYYkypiqar6VMR4JjANeA93RVO3VPUF4IWoeT+IGG8ELupi24dxVVPR8+cDR8UQ9yf3+OVQdCiceetes62/KmNMKoulk8PrI6dFpAB3ae3Br7kePnp5n8Rh/VUZY1JZLFdVRdsNlPd1IEmp/GSoWgF1W/eabf1VGWNSWSxtHM/i2hnAJZqxwBPxDCpplJ/iXtfMhfEXdszuaOOwqipjTAqKpY3jFxHjrcDHqlrZ1coHlSETIWMArH19r8SRlR4gI5hmJQ5jTEqKJXGsAzb5hmxEJEtEylR1bVwjSwaBIIw8wZU4IogIhdZflTEmRcXSxvFnIBwx3ebnpYbyU2DHaqjeu5BV6G8CNMaYVBNL4gj6TgoB8OOpc/NCRzvH63vNLspJZ6d1dGiMSUGxJI4qEflc+4SITAe2xS+kJDNoLGQV7VNdZf1VGWNSVSxtHNcAfxKRO/10Ja6rj9SQluYuy10zF1TBPzfKesg1xqSqWG4AXAUcJyK5frou7lElm7KTYfkzsHONu5McV+KobmihtS1MMLA/t8MYY8yBqcdvPBH5iYgUqGqdqtaJSKGI/Kg/gksa5ae614jqqqKcEKpQ3WDtHMaY1BLLT+XPqOqu9glV3QmcG7eIktHA0ZA7ZK/EYf1VGWNSVSyJIyAiGe0TIpIFZHSz/sFHxLdzvO7aObD+qowxqSuWxPEn4O8icpWIXAX8jRh6xj3olJ8Cu7dC1YfAnv6q7CZAY0yqiaVx/DYRWQR82s/6b1V9Ob5hJaHIfqsGHdHRX5VVVRljUk1MlwOp6kuq+i3gv4BBIvJ8T9scdArLYMAIWOvaOQqzQ4hA5c76xMZljDH9LJarqkIi8nkR+TOwCTgD+H3cI0tG5afA2jcgHCYzPcCUkYXMXlGV6KiMMaZfdZk4ROQsEbkPWAN8EdeusUNVv6Kqz/ZXgEml/GRo2AlblgJw1tghfLCphvU7rNRhjEkd3ZU4XgIOBU5S1ct9sgh3s/4+ROQcEflQRCpE5OZOlmeIyON++TsiUubnl4lIg4gs9MPvI7aZ4/fZvmxQb2L6RMpOdq/+styzxw0B4OVlm/stBGOMSbTuEscxwFvAqyLyN39FVSDWHYtIAPgt8Bncw58uFZGxUatdBexU1VHA7cBtEctWqeokP1wTtd1lEcu20l8GDIPiUR2JY0RxNkcMyeOVZVv6LQRjjEm0LhOHqi5U1ZtV9TBco/gkIF1EXhSRq2PY97FAhaqu9j3qPgZMj1pnOvCAH38SmCbiO4NKVmUnw8dvQlsrAGeNG8K8j3ewra4pwYEZY0z/iPWqqjdV9XqgFFcyOC6GzYYB6yOmK/28TtdR1VagGij2y8pF5H0ReU1ETo7a7j5fTXVLV4lGRK4WkfkiMr+qqg8bsMtPgeZa2LQQgLPHDUYV/v6BlTqMMamhV73zqWpYVV9R1a/GKyBvEzBCVY8G/gN4RETy/bLLVHU8cLIfrugi1rtVdYqqTikpKem7yDraOV4DYOzQfEoLs3jZqquMMSkint26bgCGR0yX+nmdriMiQWAAsF1Vm1R1O4CqLgBWAWP89Ab/Wgs8gqsS6z+5Je4ZHf7BTiLCWWOH8MbKbdQ1tfZrKMYYkwjxTBzzgNEiUi4iIWAGMCtqnVnAlX78QmC2qqqIlPjGdUTkUGA0sFpEgiIy0M9PB84DlsbxGDpXfgqsextaXbvG2eMG09wW5rUP7Z4OY8zBr7v7OIq6G3rasW+zuA54GfgAeEJVl4nIrRFPFLwHKBaRClyVVPslu6cAi0VkIa7R/BpV3YHrXPFlEVkMLMSVWP6wH8f9yZSdDK0NUDkfgCllRRTlhOyyXGNMSuiur6oFgAKdNT4r7h6PbqnqC8ALUfN+EDHeCFzUyXZPAU91Mn83MLmnvxt3ZScCAmtfh7ITCaQJnz5yEC8u2Uxza5hQ0B7sZIw5eHV3OW65qh7qX6OHHpPGQS2rEIZO3Ov5HGePG0JtUytvrd6ewMCMMSb+YumrSkTkchG5xU+PEJH+bZBORuUnQ+U8aHbdjZw4aiDZoYBVVxljDnqx1Kn8H3A88C9+uhZ3R3hqKz8V2pph/TsAZKYHOO3wEv62fAvhsCY4OGOMiZ9YEsdUVf060Agdj44NxTWqA8GI4yAQgo/2PJrk7HFDqKpt4v31uxIXlzHGxFksiaPFXxqrACJSQi87OzwoZeTBmLNh6ZMd3Y+cdvgggmnCK8utusoYc/CKJXHcATyNe4DTj4E3gJ/ENaoDxcRLYXcVrJoNwICsdI4/rJhXlm1B1aqrjDEHpx4Th6r+CbgR+CmuK5ALVPXP8Q7sgDDqTMgqgkWPdsw6e9wQ1mzbzcqtdQkMzBhj4iemGwCBrcCjuC4+tsRyA2BKCIZg/IWw4nlo2AXAmWMHA/CKXV1ljDlIdVfiWADM969VwEfASj++IP6hHSAmzoC2Jlj+DACD8zM5ekSBdXpojDlo9XgDIPAqcL6qDlTVYlz/UK/0V4BJ75BjYOAYWPRYx6yzxg5hyYZqNu5qSGBgxhgTH7E0jh/nuw4BQFVfBE6IX0gHGBFX6lj3JuxcC7hOD8Gqq4wxB6dYEsdGEfm+fw54mYh8D9gY78AOKOMvdq+LnwDg0JJcRg/KteoqY8xBKZbEcSlQgrsk92lgkJ9n2hUMdz3mLnoU/GW4Z40bzLtrd7Bzd3OCgzPGmL4Vy+W4O1T1BlxX5yer6g2+i3MTaeKlsGO1678KOHf8UNrCyqPz1iU4MGOM6VuxdHI4XkTexz0waZmILBCRo+If2gFm7OcgmNVxT8e4QwZwxhGDuOu11VQ3tCQ4OGOM6TuxVFXdBfyHqo5U1ZHAfwJ3xzesA1BGHhx5Pix9quPJgP951hiqG1q45/XVCQ7OGGP6TiyJI0dV/9E+oapzgJy4RXQgmzgDGqvho5cAV+r47ISh3PPGGrbXNSU4OGOM6RuxJI7VInJLxFVV3wfsJ3RnDj0NcofAosc7Zv2/T4+hoaWN381Zlbi4jDGmD8WSOL6Ku6rqL34o8fN6JCLniMiHIlIhIjd3sjxDRB73y98RkTI/v0xEGkRkoR9+H7HNZBFZ4re5Q0Q6e7RtYqQFYMLFsPJl2O2eBDhqUC5fOKaUB9/+mE3VdkOgMebAF8tVVTtV9RuqeowfbvDP5OiW74r9t8BngLHApSIyNmq1q4CdqjoKuB24LWLZKlWd5IdrIub/DvgaMNoP5/QUS7+aeCmEW11bh3fDtNGoKr+ZXZHAwIwxpm9018nhrO6GGPZ9LFChqqtVtRl4DJgetc504AE//iQwrbsShIgMBfJV9W11/ZY/CFwQQyz9Z/BYGDJ+rx5zhxdlc+mxI3hi3nrWba9PYHDGGPPJdVfiOB4oBV4HfgH8b9TQk2HA+ojpSj+v03VUtRWoBor9snIReV9EXhORkyPWr+xhnwCIyNUiMl9E5ldVVcUQbh+aeClsfA+qPuyYdd3powgGhF+9+lH/xmKMMX2su8QxBPgucBTwa+BMYJuqvqaqr8U5rk3ACFU9GvgP4BERye/NDlT1blWdoqpTSkpK4hJkl466ECSwV8eHg/IzufL4Mp5euIGPttT2bzzGGNOHuusdt01VX1LVK4HjgApgjohcF+O+NwDDI6ZL/bxO1xGRIDAA2K6qTaq63cexAFgFjPHrl/awz8TLGwyjprm+q8J7nrJ7zamHkRMK8stXrNRhjDlwdds47q96+gLwMPB19jxGNhbzgNEiUi4iIWAGEN02Mgu40o9fCMxWVRWREt+4jogcimsEX62qm4AaETnOt4V8CXgmxnj618QZUFMJH+wJrzAnxL+eXM5LyzazuHJX4mIzxphPoLvG8QeBt4BjgB+q6qdU9b9VNaZf+L7N4jrgZeAD4AlVXSYit4rI5/xq9wDFIlKBq5Jqv2T3FGCxiCzENZpfE9E/1r8Df8SVgFYBL8Z8tP3pyM/B0Enw3H9AzaaO2VedVE5hdjq/sFKHMeYAJep7c91ngUgY2O0nI1cSQFW1V20OiTRlyhSdP39+///hqo/grlNg5PFw2VOQ5vL0Xa+t4qcvruCJfzueY8vtKbzGmOQkIgtUdUr0/O7aONJUNc8P+RFD3oGUNBKqZAyc/WNYNRve3dO915eOL2NQXgb/8/IKukrcxhiTrGK5c9x8ElO+CmPOgb/9ALYsByArFOCbnx7DvLU7+YN1gGiMOcBY4og3EfjcnZCZD3/5WkfPuZceO5xzxw/hZy+u4PWV/XyfiTHGfAKWOPpDbolLHluWwt9vBUBE+J8LJzJ6UB7XP/o+63fYHeXGmAODJY7+cvg5MOUqeOtOWD0HgJyMIHd/aTLhsPK1B+dT39ya2BiNMSYGljj601k/guLR8PS1UO+uLh5ZnMMdlx7Nh1tqufHJxdZYboxJepY4+lMoG774B9i9FZ77f+CTxGmHD+LbZx/Oc4s3cfdcayw3xiQ3Sxz97ZCj4fTvwfK/7tWD7rWnHsZnxw/ltpessdwYk9wscSTCiTfAyBPhhW/D+ncB11j+8wsnMGawNZYbY5KbJY5ESAvAF/8IuYPgwQtg7RuAayy/64rJqGKN5caYpGWJI1HyD4EvvwADSuHhC6Hi78CexvKPttTyzccW0tjSluBAjTFmb5Y4Eil/KHz5eSg+DB6dAR+6/hpPHVPCLeeN5ZXlW/ji7960aitjTFKxxJFouSVw5bMweBw8fjks+ysAXzmxnHuunML6HfWc95s3+MeHWxMbpzHGeJY4kkF2EXzpGRg2BZ78Cix6HIBpRw7m2etP4pCCLL56/zx+/epKwmG7z8MYk1iWOJJF5gC4/Cl3tdXT/wYLHgBcm8dfrj2Bz08axu2vfsS/Pjif6vqWBAdrjEllljiSSUYuXPZn99jZZ78B//w1hNvICgX434sn8t/Tx/H6yirOv/MNlm+sSXS0xpgUZYkj2aRnwYxH4MjzXVfsd50Cq+cgIlxxfBmPXX08Ta1tfOF3/+Shtz+mzaqujDH9zBJHMgpmwMUPwYX3QVMNPDgdHrkEqj5i8shCnrv+ZCaPLOSWvy7ls3e8bneaG2P6VVwTh4icIyIfikiFiNzcyfIMEXncL39HRMqilo8QkToR+VbEvLUiskREFopIAp4H209E4KgvwNfnwad/CB+/Cf93HLzwbUrS6nj4qqn832XHsLu5lSvueZev3PcuK7fUJjpqY0wK6PKZ4594xyIB4CPgTKASmAdcqqrLI9b5d2CCql4jIjOAz6vqJRHLn8Q97/wdVf2Fn7cWmKKq22KNJWHPHO9LdVUw56ew4D4I5cGp34Zjr6aJIA+++TF3zF5JfXMblx47nG9+egwDczMSHbEx5gDX62eO94FjgQpVXa2qzcBjwPSodaYDD/jxJ4FpIiI+4AuANcCyOMZ44MgtgfN+Cde+CcOPhVe+D7ePI+MfP+RrY8O89u3TuXzqCB59dz2n/c8cfjdnFQ3Ndte5MabvxTNxDAPWR0xX+nmdrqOqrUA1UCwiucBNwA872a8Cr4jIAhG5us+jTnaDjoTLn3T3fQyfCm/eCXdOpuiJz/PDsmW8ct2nmFpexG0vreC4n/6dHz+/nI+370501MaYg0gw0QF0YSZwu6rW+QJIpJNUdYOIDAL+JiIrVHVu9Eo+qVwNMGLEiHjH2/8OPc0NtVtg4Z/gvQfh6as5LGMA90y4mCXjL+B3K7K4959r+eMbazh1TAlfOn4kp44ZRCBtn3NqjDExi2cbx/HATFU9209/B0BVfxqxzst+nbdEJAhsBkqAucBwv1oBEAZ+oKp3Rv2NmUBde/tHVw6KNo6ehMPw8T/hvQdg+Sxoa4Khk6g+8l94aPeneOC9HVTVNjG8KIvLp47k4inDKcwJJTpqY0wS66qNI56JI4hrHJ8GbMA1jv+Lqi6LWOfrwPiIxvEvqOrFUfuZiU8OIpIDpKlqrR//G3Crqr7UXSwpkTgi1e+AxU/A+w/BlqUQzCJ85Od4u+BcfrVyEO+u3UkomMa0IwbxuYmHcPoRg8hMDyQ6amNMkukqccStqkpVW0XkOuBlIADcq6rLRORWYL6qzgLuAR4SkQpgBzCjh90OBp721VdB4JGekkZKyi6C466Bqf8GG9+H9x4kbelTnND0OCcUHcrWUy/mwfoTeOyDnby4dDO5GUHOGjuY8ycdwkmjBpIesNt7jDFdi1uJI5mkXImjM831sPwZVwr5+J8gaeiQiWwomMJL9WO4++PBbG0MUpidzmfGD+VzEw/hU2VF1h5iTArr96qqZGKJI8q2CljyZ1jzGlTOh3ALmhakunA87+g4Hq8ayZsto8jLy+eccUM4d/xQji23JGJMqrHEYYmjc827Yf07sOZ1WPs6bHgPtI22tHRWZozn2d1HMLtlPFXZozlnvEsiU8uLLYkYkwIscVjiiE1TLax7G1bPgVWzYau70b86UMSclnHMbh3P8qzJTBk3hlNGl3DCqIEMyEpPbMzGmLiwxGGJY//UbHIJZNVsdNU/kIbtAHygZbzRNpZ3dCwNQ49jyuEjOWXMQCaWFhC0xnVjDgqWOCxxfHLhMGxeBBV/J7x6Dqx7h7RwM22ksTh8KG+Fx7IwOIFQ+fEcd/hwTho1kJHF2XRyE6cx5gBgicMSR99raYTKd2HNXFpXzSFt4/ukaSstBHk/fBjvho9gddYEckedyJQjRnLCYcXW+aIxBxBLHJY44q+pDta9ja55jaZVbxDauog0baMNYVm4jHfDR7BxwDHkjj6JY44cxbHlRWSHkrXXG2OMJQ5LHP2vqQ4q5xFe+0/qK14nc8v7BMNNAKwKD2URo9leMJHcw47j8IlTmTC82NpHjEkiljgscSReaxNsfJ+W1W9QvfJNsra8R07rTgDqNYNlHMa2gglklE+lfNIZlI0cae0jxiSQJQ5LHMlHFXaupW7V21SteIPgxgUMbfiIIO45IutkKFsGTCJYdjwjJ51B0cij3JMRjTH9whKHJY4DQ0sDm1e8w6alc0irfIcRu5dQiHskbo3ksTl/AoGy4xk84SxyyyZDwNpIjIkXSxyWOA5IbW1hKj54nw1L5iDr32Hk7iUcKhsB2E0WH+dOoqH0RArHTWPk2KkEAtbLrzF9xRKHJY6DQkNzG0s/WsmOZbPJqPwnZbXvUYZLJLs0l4+yJtIwcDwlhQUMLh5AUX4uEsyCYAYEMyEYgsJyKByZ4CMxJvlZ4rDEcVBSVTasW8XmRX8jbe0bDNs1j8HhLT1vWDwKRn0aDpsGZSdBKDv+wRpzgLHEYYkjZTQ17qZi4w4+2lBFxcbtrN68g8qqnaS1NZFJCxPT13N25lImtCwmXZsJBzLQEccTGH0mjJoGeUNBw25nGt57QCB3EKRZlZg5+FnisMSR0lrawqyqqmNJZTXLNtawfGMNqzZtY2zLUk5JW8ypaYsYk7Yhtp0FMqD4MFdqGTjGD6OgeDRk5sf3QIzpR/3+BEBjkkl6II0jhuRzxJB8LvLzwmFl/c4zWL6xhlkba9i0biUFm99EG2tQIEwaaWlpFOdlMTAvk5K8LAbnBilp28yA3WvI2LIcWfE8aNueP5Q3FIZMgKETYOhEN14wwi4jNgcVK3EYE6W6oYWKrXVUbK2lYmsdK7fWUbG1jsqdDfusOzhbmJi7i6MyNjMqbRPl4fUMb6ogp3YV0l7dlVmwJ5EUHQaBkKvqSgu6V4kYz8hzySdvCKRn9e+BGxPFqqoscZhPqL65ldVVu9lS08jW2ia21DSypaaJqlr3uqWmkW11TYQVMmlias5mpg3YxKT0dZQ1V5BX8xHS1hz7H8wscEkkf+ieZFIwAooOdVeG5Q+DNOuixcRPQqqqROQc4NdAAPijqv4sankG8CAwGdgOXKKqayOWjwCWAzNV9Rex7NOYeMkOBTlq2ACOGjagy3UamttYvqmGpRuqWVxZzcMbdjFzUx1hhSCtHJq5m5LcIMVZAQbmBCjKTKM4K0BBVoDCTKEkvYnBaTvJadyK1G2G2s1Quwm2roC6LXtXiwUy3GXFRYf6ZFIG6dm+9BKMKNUEIZDuXjPyIJTrXtvHLfmYXopb4hCRAPBb4EygEpgnIrNUdXnEalcBO1V1lIjMAG4DLolY/kvgxV7u05iEyQoFmDyykMkjCzvm1Te3snxjDUs2VLOqqo7tdc1sqmti6bZmttU1UdPYCrQnhAAwkJzQYEoLpzK8KIvSwdkMPzKb0oIQIwI7GNy6iQEN6wnsWgs7VsPOtbBmLrTU71/Q7Ykkq9Aln8Iyf69LGRSVu1JO0LrDN3vEs8RxLFChqqsBROQxYDquBNFuOjDTjz8J3CkioqoqIhcAa4DdvdynMUklOxRkSlkRU8qKOl3e1NrGjt3NbKttZlN1A5U7G1i/s571Oxqo3FnPW6u2s7u5ba9tRIZQlD2CkrwzKcnLYNCoDMqy6zkkN41h+UGG5qUzJDdARppCuBXCba6Tyebd0FzrHhHcVOt6MG6qhaYa2L3NJaHVc6KSkLhqsfyhkDkAMvLda8eQ76rVcgZC7hDIG+ym7YKAg1Y8E8cwYH3EdCUwtat1VLVVRKqBYhFpBG7ClSy+1ct9AiAiVwNXA4wYMWL/j8KYOMsIBhg6IIuhA7IYX7pvNZiqsqu+hfU769lS08TW2ka21jRRVdfkXmsbWbW1jll1TbS07d1mOTA3g9LCLD8UMyjvEEryMigpzHAJJy+D3Izg3r0Qq0LdVpdEdq5xrzvWwO6t0LDTTTdWQ8MuCLd0flDBTMgd7Npl8oa48bSgS2DR98a0V7+FfPVZRntVWv6eKrWsIl8Vl9kHZ9x8Usl6Oe5M4HZVrdvfbrVV9W7gbnCN430XmjH9S0QozAlRmBPqdr1wWNla20Tlznoqd7rSyvodDVTuqmfJhmpeXrZ5n8QCkJme5pNIJoPzMxicn8ng/EyG5JcyKP8whhySyZABmfs+dEsVWhtdEmmsht1Vvk1mM3S0z2yGrR+4UoyqK4VIACRt7wF1paGmWjfe+ZmAAcOh+FB3D02Rv5em+DDIP8QlKyvl9It4Jo4NwPCI6VI/r7N1KkUkCAzANZJPBS4UkZ8DBUDYl0IWxLBPY1JSWpowZID7kp9Stu/ycFipbmihqq6Jqlo3bK1t7BjfUtPEis21vPZh1T5VYwA5oQAD8zIozgkxMDeD4twMSnJDFOdmMDA3n4LsYvILx5M3NEh+Vjp5mUHSe/tgrnDYVZM1RVSnNddCXRXsWAXbV8H2CljyZ5esIkkapOdAKMd1IRPKiZjOibgwIHdPu077dEY+ZBXsqX7LyO+8d4Bw255E2T6EW11VXsFw93dSQDwTxzxgtIiU477cZwD/ErXOLOBK4C3gQmC2uuuDT25fQURmAnWqeqdPLj3t0xjTibS0PSWXMYPzul23rqmVzdWNbK1pZLMfqmqb2F7nGvQ/3l7Pe+t2sn13M91d0Z+VHiA/K0h+ZjpFOSEG5mVQkuuqyQbmhvyrS0IDstLJCQWQDP9lztCud6wK9dv3JJK6LS7hNO/ee2jZ7arXqiuhuc4NTXV7X53WKdnTlhPKdvtq2OWSWHeyCl2paMBwl0gGlEJOie9gMzOis82I17RgRElJ/HjEa7gF2lqgrdm1U7WPtzW5qr7MQsgphuyBLnH1Q6krbonDt1lcB7yMu1TkXlVdJiK3AvNVdRZwD/CQiFQAO3CJoNf7jNcxGJOqcjOCjBqUy6hBud2u1xZWdta7ZFJd30JNYyu1jS3UNESOt1Ld0MKO3c0s31jDttomaptaO91fmkBepiut5GWmk+9fB2SlU5STTlFORsRriKKcoygqOYb8zKh2mu60V7E11fkLBercxQHt7TYdpQk/3lzXyQUBEYOkQc1G2LXOJajq9a5taM3cnhNNXwtmugTSnkhyBsJ5v+rzTjztBkBjTL9rbGmjqraJbXVNbPOlmNrGFmobW6lp8K+N7cmnler6ZnbUN9PYEu50f2kC+Vnp5Gemk58VZED7eMT0gOwQBVnpFGSnU5AVoiA7nQHZ6eRFXxzQV1Rd4qnf7koKrY1Rrw3Q0rin9KMK6L6vgZAf0v1rxp5xSXMlqvpt7qq4+m1QvyNifDtc/95+d8ppfVUZY5JGZnqA4UXZDC/q3S/h+uZWduxu3mfYVd/iEo0v6dQ0tLCqto7qhhaqG1q6TDgAgTShOCfUcZVZ+4UCg/JdtdrAvAwygwHSg0IokEZ6II1QMM2NB9PIDKYR7KwtR8S1m2QV9O7kHAAscRhjDhjZoSDZoSClhb1LOE2tbS6J1LewK+J1V71LOtvqmtjqLxZYvqmGbXXNtIVjr43JDgVcqSYrvaPks2d672q3jlc/Pys9QHpACKRJfEo+cWCJwxhz0MsIBhiUF2BQXmz3gbSFlR27m90FAbubaGoJ09IWprktTHOre23xrw3NYWoaXcmmxpdwKnfW88Em17ZT10V7TjQR14uzK9UI6b50k5fprlIryNqTjFzVW+eJakBWOqFgfLuRscRhjDFRAmnibpTM++RdrbSFlbqONhvXfrOnLaeFxlaXhFxiUlra/HirG9qr3tbtqO+oeqvv5HLpSFnpe0pAT3/9hH3vwfmELHEYY0wcBdLElQ6y0/tsny6htHQkksjSTnV9xLzGFjKDff+0SkscxhhzgAkF0xiY6+6BSQTrT9kYY0yvWOIwxhjTK5Y4jDHG9IolDmOMMb1iicMYY0yvWOIwxhjTK5Y4jDHG9IolDmOMMb2SEt2qi0gV8PF+bj4Q2NaH4fQli23/WGz7x2LbPwdybCNVtSR6Zkokjk9CROZ31h99MrDY9o/Ftn8stv1zMMZmVVXGGGN6xRKHMcaYXrHE0bO7Ex1ANyy2/WOx7R+Lbf8cdLFZG4cxxphesRKHMcaYXrHEYYwxplcscXRBRM4RkQ9FpEJEbk50PNFEZK2ILBGRhSIyP8Gx3CsiW0VkacS8IhH5m4is9K+FSRTbTBHZ4M/dQhE5NwFxDReRf4jIchFZJiI3+PkJP2/dxJbw8+bjyBSRd0VkkY/vh35+uYi84/9nHxeRUJLEdb+IrIk4b5P6M66oGAMi8r6IPOen9++cqaoNUQMQAFYBhwIhYBEwNtFxRcW4FhiY6Dh8LKcAxwBLI+b9HLjZj98M3JZEsc0EvpXgczYUOMaP5wEfAWOT4bx1E1vCz5uPSYBcP54OvAMcBzwBzPDzfw9cmyRx3Q9cmOjz5uP6D+AR4Dk/vV/nzEocnTsWqFDV1araDDwGTE9wTElLVecCO6JmTwce8OMPABf0Z0ztuogt4VR1k6q+58drgQ+AYSTBeesmtqSgTp2fTPeDAmcAT/r5/X7uuokrKYhIKfBZ4I9+WtjPc2aJo3PDgPUR05Uk0T+Op8ArIrJARK5OdDCdGKyqm/z4ZmBwIoPpxHUisthXZSWkGq2diJQBR+N+oSbVeYuKDZLkvPkql4XAVuBvuBqCXara6ldJyP9sdFyq2n7efuzP2+0ikpgHhcOvgBuBsJ8uZj/PmSWOA9dJqnoM8Bng6yJySqID6oq6cnDS/PICfgccBkwCNgH/m6hARCQXeAr4pqrWRC5L9HnrJLakOW+q2qaqk4BSXA3BEYmKJVJ0XCJyFPAdXHyfAoqAm/o7LhE5D9iqqgv6Yn+WODq3ARgeMV3q5yUNVd3gX7cCT+P+eZLJFhEZCuBftyY4ng6qusX/g4eBP5Cgcyci6bgv5j+p6l/87KQ4b53FliznLZKq7gL+ARwPFIhI0C9K6P9sRFzn+Ko/VdUm4D4Sc95OBD4nImtxVe9nAL9mP8+ZJY7OzQNG+ysOQsAMYFaCY+ogIjkiktc+DpwFLO1+q343C7jSj18JPJPAWPbS/sXsfZ4EnDtfv3wP8IGq/jJiUcLPW1exJcN583GUiEiBH88CzsS1w/wDuNCv1u/nrou4VkT8EBBcG0K/nzdV/Y6qlqpqGe77bLaqXsb+nrNEt/In6wCci7uaZBXwvUTHExXbobgrvRYByxIdH/AoruqiBVdPehWu/vTvwErgVaAoiWJ7CFgCLMZ9UQ9NQFwn4aqhFgML/XBuMpy3bmJL+Hnz8U0A3vdxLAV+4OcfCrwLVAB/BjKSJK7Z/rwtBR7GX3mVqAE4jT1XVe3XObMuR4wxxvSKVVUZY4zpFUscxhhjesUShzHGmF6xxGGMMaZXLHEYY4zpFUscxvQBEWmL6P10ofRhj8oiUhbZu68xiRbseRVjTAwa1HU1YcxBz0ocxsSRuOem/Fzcs1PeFZFRfn6ZiMz2Hd/9XURG+PmDReRp/0yHRSJygt9VQET+4J/z8Iq/M9mYhLDEYUzfyIqqqrokYlm1qo4H7sT1UArwG+ABVZ0A/Am4w8+/A3hNVSfiniOyzM8fDfxWVccBu4AvxvVojOmG3TluTB8QkTpVze1k/lrgDFVd7TsO3KyqxSKyDddlR4ufv0lVB4pIFVCqrkO89n2U4broHu2nbwLSVfVH/XBoxuzDShzGxJ92Md4bTRHjbVj7pEkgSxzGxN8lEa9v+fE3cb2UAlwGvO7H/w5cCx0PBRrQX0EaEyv71WJM38jyT35r95Kqtl+SWygii3Glhkv9vOuB+0Tk20AV8BU//wbgbhG5CleyuBbXu68xScPaOIyJI9/GMUVVtyU6FmP6ilVVGWOM6RUrcRhjjOkVK3EYY4zpFUscxhhjesUShzHGmF6xxGGMMaZXLHEYY4zplf8PDK084s75eVkAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -395,7 +335,7 @@
     "network.build_decoder()\n",
     "network.build_autoencoder()\n",
     "network.dir()\n",
-    "network.fit_data()\n",
+    "network.fit_data(epochs=10)\n",
     "network.plot_latent_dimension(\"Autoencoder without classifier\")\n",
     "network.plot_reco()\n",
     "network.plot_score(\"Autoencoder without classifier\")\n"
@@ -405,297 +345,101 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Repeating the same procedure for the autoencoder with classifier."
+    "# Make an hls4ml config & model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"functional_3\"\n",
-      "__________________________________________________________________________________________________\n",
-      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
-      "==================================================================================================\n",
-      "encoder_input (InputLayer)      [(None, 784)]        0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_11 (Dense)                (None, 128)          100480      encoder_input[0][0]              \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_12 (Dense)                (None, 64)           8256        dense_11[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "encoder_output (Dense)          (None, 2)            130         dense_12[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_15 (Dense)                (None, 128)          384         encoder_output[0][0]             \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_13 (Dense)                (None, 64)           192         encoder_output[0][0]             \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_16 (Dense)                (None, 64)           8256        dense_15[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_14 (Dense)                (None, 128)          8320        dense_13[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_17 (Dense)                (None, 32)           2080        dense_16[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "ecoder_output (Dense)           (None, 784)          101136      dense_14[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "classifier_output (Dense)       (None, 10)           330         dense_17[0][0]                   \n",
-      "==================================================================================================\n",
-      "Total params: 229,564\n",
-      "Trainable params: 229,564\n",
-      "Non-trainable params: 0\n",
-      "__________________________________________________________________________________________________\n",
-      "Epoch 1/40\n",
-      "235/235 [==============================] - 2s 8ms/step - loss: 0.6008 - ecoder_output_loss: 0.0786 - classifier_output_loss: 1.5322 - ecoder_output_accuracy: 0.0047 - classifier_output_accuracy: 0.3926 - val_loss: 0.2311 - val_ecoder_output_loss: 0.0637 - val_classifier_output_loss: 1.1255 - val_ecoder_output_accuracy: 0.0035 - val_classifier_output_accuracy: 0.5605\n",
-      "Epoch 2/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.1926 - ecoder_output_loss: 0.0616 - classifier_output_loss: 0.9697 - ecoder_output_accuracy: 0.0056 - classifier_output_accuracy: 0.6523 - val_loss: 0.1711 - val_ecoder_output_loss: 0.0596 - val_classifier_output_loss: 0.8543 - val_ecoder_output_accuracy: 0.0040 - val_classifier_output_accuracy: 0.7155\n",
-      "Epoch 3/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.1599 - ecoder_output_loss: 0.0582 - classifier_output_loss: 0.7770 - ecoder_output_accuracy: 0.0082 - classifier_output_accuracy: 0.7458 - val_loss: 0.1498 - val_ecoder_output_loss: 0.0564 - val_classifier_output_loss: 0.7106 - val_ecoder_output_accuracy: 0.0095 - val_classifier_output_accuracy: 0.7742\n",
-      "Epoch 4/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.1443 - ecoder_output_loss: 0.0560 - classifier_output_loss: 0.6720 - ecoder_output_accuracy: 0.0084 - classifier_output_accuracy: 0.7849 - val_loss: 0.1422 - val_ecoder_output_loss: 0.0546 - val_classifier_output_loss: 0.6758 - val_ecoder_output_accuracy: 0.0081 - val_classifier_output_accuracy: 0.7827\n",
-      "Epoch 5/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.1346 - ecoder_output_loss: 0.0541 - classifier_output_loss: 0.6122 - ecoder_output_accuracy: 0.0084 - classifier_output_accuracy: 0.8050 - val_loss: 0.1310 - val_ecoder_output_loss: 0.0532 - val_classifier_output_loss: 0.5934 - val_ecoder_output_accuracy: 0.0078 - val_classifier_output_accuracy: 0.8124\n",
-      "Epoch 6/40\n",
-      "235/235 [==============================] - 1s 4ms/step - loss: 0.1267 - ecoder_output_loss: 0.0531 - classifier_output_loss: 0.5568 - ecoder_output_accuracy: 0.0089 - classifier_output_accuracy: 0.8251 - val_loss: 0.1259 - val_ecoder_output_loss: 0.0528 - val_classifier_output_loss: 0.5601 - val_ecoder_output_accuracy: 0.0082 - val_classifier_output_accuracy: 0.8253\n",
-      "Epoch 7/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.1211 - ecoder_output_loss: 0.0525 - classifier_output_loss: 0.5173 - ecoder_output_accuracy: 0.0092 - classifier_output_accuracy: 0.8408 - val_loss: 0.1242 - val_ecoder_output_loss: 0.0522 - val_classifier_output_loss: 0.5572 - val_ecoder_output_accuracy: 0.0080 - val_classifier_output_accuracy: 0.8168\n",
-      "Epoch 8/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.1171 - ecoder_output_loss: 0.0521 - classifier_output_loss: 0.4897 - ecoder_output_accuracy: 0.0098 - classifier_output_accuracy: 0.8499 - val_loss: 0.1167 - val_ecoder_output_loss: 0.0516 - val_classifier_output_loss: 0.4957 - val_ecoder_output_accuracy: 0.0085 - val_classifier_output_accuracy: 0.8459\n",
-      "Epoch 9/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.1129 - ecoder_output_loss: 0.0518 - classifier_output_loss: 0.4586 - ecoder_output_accuracy: 0.0105 - classifier_output_accuracy: 0.8603 - val_loss: 0.1149 - val_ecoder_output_loss: 0.0516 - val_classifier_output_loss: 0.4855 - val_ecoder_output_accuracy: 0.0094 - val_classifier_output_accuracy: 0.8566\n",
-      "Epoch 10/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.1097 - ecoder_output_loss: 0.0516 - classifier_output_loss: 0.4353 - ecoder_output_accuracy: 0.0107 - classifier_output_accuracy: 0.8706 - val_loss: 0.1115 - val_ecoder_output_loss: 0.0512 - val_classifier_output_loss: 0.4621 - val_ecoder_output_accuracy: 0.0115 - val_classifier_output_accuracy: 0.8623\n",
-      "Epoch 11/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.1066 - ecoder_output_loss: 0.0514 - classifier_output_loss: 0.4124 - ecoder_output_accuracy: 0.0108 - classifier_output_accuracy: 0.8801 - val_loss: 0.1125 - val_ecoder_output_loss: 0.0514 - val_classifier_output_loss: 0.4724 - val_ecoder_output_accuracy: 0.0134 - val_classifier_output_accuracy: 0.8608\n",
-      "Epoch 12/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.1046 - ecoder_output_loss: 0.0512 - classifier_output_loss: 0.3976 - ecoder_output_accuracy: 0.0108 - classifier_output_accuracy: 0.8850 - val_loss: 0.1074 - val_ecoder_output_loss: 0.0507 - val_classifier_output_loss: 0.4328 - val_ecoder_output_accuracy: 0.0111 - val_classifier_output_accuracy: 0.8738\n",
-      "Epoch 13/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.1018 - ecoder_output_loss: 0.0509 - classifier_output_loss: 0.3766 - ecoder_output_accuracy: 0.0102 - classifier_output_accuracy: 0.8926 - val_loss: 0.1060 - val_ecoder_output_loss: 0.0505 - val_classifier_output_loss: 0.4258 - val_ecoder_output_accuracy: 0.0098 - val_classifier_output_accuracy: 0.8824\n",
-      "Epoch 14/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0994 - ecoder_output_loss: 0.0506 - classifier_output_loss: 0.3581 - ecoder_output_accuracy: 0.0105 - classifier_output_accuracy: 0.9014 - val_loss: 0.1033 - val_ecoder_output_loss: 0.0504 - val_classifier_output_loss: 0.4010 - val_ecoder_output_accuracy: 0.0105 - val_classifier_output_accuracy: 0.8869\n",
-      "Epoch 15/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0972 - ecoder_output_loss: 0.0505 - classifier_output_loss: 0.3404 - ecoder_output_accuracy: 0.0106 - classifier_output_accuracy: 0.9077 - val_loss: 0.1019 - val_ecoder_output_loss: 0.0500 - val_classifier_output_loss: 0.3939 - val_ecoder_output_accuracy: 0.0103 - val_classifier_output_accuracy: 0.8943\n",
-      "Epoch 16/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0954 - ecoder_output_loss: 0.0503 - classifier_output_loss: 0.3268 - ecoder_output_accuracy: 0.0102 - classifier_output_accuracy: 0.9134 - val_loss: 0.1003 - val_ecoder_output_loss: 0.0498 - val_classifier_output_loss: 0.3812 - val_ecoder_output_accuracy: 0.0072 - val_classifier_output_accuracy: 0.9009\n",
-      "Epoch 17/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0930 - ecoder_output_loss: 0.0501 - classifier_output_loss: 0.3068 - ecoder_output_accuracy: 0.0102 - classifier_output_accuracy: 0.9206 - val_loss: 0.1004 - val_ecoder_output_loss: 0.0499 - val_classifier_output_loss: 0.3853 - val_ecoder_output_accuracy: 0.0101 - val_classifier_output_accuracy: 0.8964\n",
-      "Epoch 18/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0915 - ecoder_output_loss: 0.0499 - classifier_output_loss: 0.2963 - ecoder_output_accuracy: 0.0111 - classifier_output_accuracy: 0.9238 - val_loss: 0.0986 - val_ecoder_output_loss: 0.0495 - val_classifier_output_loss: 0.3727 - val_ecoder_output_accuracy: 0.0087 - val_classifier_output_accuracy: 0.9028\n",
-      "Epoch 19/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0896 - ecoder_output_loss: 0.0496 - classifier_output_loss: 0.2814 - ecoder_output_accuracy: 0.0100 - classifier_output_accuracy: 0.9279 - val_loss: 0.0963 - val_ecoder_output_loss: 0.0494 - val_classifier_output_loss: 0.3521 - val_ecoder_output_accuracy: 0.0120 - val_classifier_output_accuracy: 0.9071\n",
-      "Epoch 20/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0886 - ecoder_output_loss: 0.0495 - classifier_output_loss: 0.2743 - ecoder_output_accuracy: 0.0103 - classifier_output_accuracy: 0.9295 - val_loss: 0.0960 - val_ecoder_output_loss: 0.0498 - val_classifier_output_loss: 0.3475 - val_ecoder_output_accuracy: 0.0071 - val_classifier_output_accuracy: 0.9078\n",
-      "Epoch 21/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0866 - ecoder_output_loss: 0.0493 - classifier_output_loss: 0.2599 - ecoder_output_accuracy: 0.0102 - classifier_output_accuracy: 0.9344 - val_loss: 0.0954 - val_ecoder_output_loss: 0.0496 - val_classifier_output_loss: 0.3470 - val_ecoder_output_accuracy: 0.0078 - val_classifier_output_accuracy: 0.9095\n",
-      "Epoch 22/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0860 - ecoder_output_loss: 0.0491 - classifier_output_loss: 0.2570 - ecoder_output_accuracy: 0.0113 - classifier_output_accuracy: 0.9342 - val_loss: 0.0962 - val_ecoder_output_loss: 0.0490 - val_classifier_output_loss: 0.3610 - val_ecoder_output_accuracy: 0.0074 - val_classifier_output_accuracy: 0.9085\n",
-      "Epoch 23/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0844 - ecoder_output_loss: 0.0488 - classifier_output_loss: 0.2450 - ecoder_output_accuracy: 0.0095 - classifier_output_accuracy: 0.9380 - val_loss: 0.0925 - val_ecoder_output_loss: 0.0486 - val_classifier_output_loss: 0.3308 - val_ecoder_output_accuracy: 0.0111 - val_classifier_output_accuracy: 0.9157\n",
-      "Epoch 24/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0827 - ecoder_output_loss: 0.0486 - classifier_output_loss: 0.2345 - ecoder_output_accuracy: 0.0107 - classifier_output_accuracy: 0.9415 - val_loss: 0.0929 - val_ecoder_output_loss: 0.0485 - val_classifier_output_loss: 0.3380 - val_ecoder_output_accuracy: 0.0070 - val_classifier_output_accuracy: 0.9128\n",
-      "Epoch 25/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0818 - ecoder_output_loss: 0.0484 - classifier_output_loss: 0.2280 - ecoder_output_accuracy: 0.0085 - classifier_output_accuracy: 0.9441 - val_loss: 0.0913 - val_ecoder_output_loss: 0.0481 - val_classifier_output_loss: 0.3267 - val_ecoder_output_accuracy: 0.0090 - val_classifier_output_accuracy: 0.9173\n",
-      "Epoch 26/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0810 - ecoder_output_loss: 0.0482 - classifier_output_loss: 0.2242 - ecoder_output_accuracy: 0.0095 - classifier_output_accuracy: 0.9443 - val_loss: 0.0905 - val_ecoder_output_loss: 0.0481 - val_classifier_output_loss: 0.3220 - val_ecoder_output_accuracy: 0.0086 - val_classifier_output_accuracy: 0.9198\n",
-      "Epoch 27/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0797 - ecoder_output_loss: 0.0479 - classifier_output_loss: 0.2156 - ecoder_output_accuracy: 0.0106 - classifier_output_accuracy: 0.9474 - val_loss: 0.0896 - val_ecoder_output_loss: 0.0476 - val_classifier_output_loss: 0.3187 - val_ecoder_output_accuracy: 0.0053 - val_classifier_output_accuracy: 0.9210\n",
-      "Epoch 28/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0793 - ecoder_output_loss: 0.0478 - classifier_output_loss: 0.2150 - ecoder_output_accuracy: 0.0104 - classifier_output_accuracy: 0.9471 - val_loss: 0.0916 - val_ecoder_output_loss: 0.0477 - val_classifier_output_loss: 0.3371 - val_ecoder_output_accuracy: 0.0107 - val_classifier_output_accuracy: 0.9140\n",
-      "Epoch 29/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0780 - ecoder_output_loss: 0.0476 - classifier_output_loss: 0.2042 - ecoder_output_accuracy: 0.0116 - classifier_output_accuracy: 0.9506 - val_loss: 0.0889 - val_ecoder_output_loss: 0.0476 - val_classifier_output_loss: 0.3146 - val_ecoder_output_accuracy: 0.0107 - val_classifier_output_accuracy: 0.9206\n",
-      "Epoch 30/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0773 - ecoder_output_loss: 0.0475 - classifier_output_loss: 0.2000 - ecoder_output_accuracy: 0.0116 - classifier_output_accuracy: 0.9513 - val_loss: 0.0915 - val_ecoder_output_loss: 0.0476 - val_classifier_output_loss: 0.3406 - val_ecoder_output_accuracy: 0.0141 - val_classifier_output_accuracy: 0.9142\n",
-      "Epoch 31/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0768 - ecoder_output_loss: 0.0474 - classifier_output_loss: 0.1968 - ecoder_output_accuracy: 0.0121 - classifier_output_accuracy: 0.9514 - val_loss: 0.0882 - val_ecoder_output_loss: 0.0471 - val_classifier_output_loss: 0.3137 - val_ecoder_output_accuracy: 0.0088 - val_classifier_output_accuracy: 0.9215\n",
-      "Epoch 32/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0755 - ecoder_output_loss: 0.0472 - classifier_output_loss: 0.1877 - ecoder_output_accuracy: 0.0124 - classifier_output_accuracy: 0.9541 - val_loss: 0.0875 - val_ecoder_output_loss: 0.0473 - val_classifier_output_loss: 0.3066 - val_ecoder_output_accuracy: 0.0125 - val_classifier_output_accuracy: 0.9253\n",
-      "Epoch 33/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0747 - ecoder_output_loss: 0.0470 - classifier_output_loss: 0.1833 - ecoder_output_accuracy: 0.0124 - classifier_output_accuracy: 0.9559 - val_loss: 0.0885 - val_ecoder_output_loss: 0.0475 - val_classifier_output_loss: 0.3171 - val_ecoder_output_accuracy: 0.0096 - val_classifier_output_accuracy: 0.9214\n",
-      "Epoch 34/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0741 - ecoder_output_loss: 0.0470 - classifier_output_loss: 0.1788 - ecoder_output_accuracy: 0.0119 - classifier_output_accuracy: 0.9574 - val_loss: 0.0864 - val_ecoder_output_loss: 0.0469 - val_classifier_output_loss: 0.3038 - val_ecoder_output_accuracy: 0.0132 - val_classifier_output_accuracy: 0.9275\n",
-      "Epoch 35/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0738 - ecoder_output_loss: 0.0468 - classifier_output_loss: 0.1779 - ecoder_output_accuracy: 0.0130 - classifier_output_accuracy: 0.9569 - val_loss: 0.0861 - val_ecoder_output_loss: 0.0471 - val_classifier_output_loss: 0.2993 - val_ecoder_output_accuracy: 0.0096 - val_classifier_output_accuracy: 0.9273\n",
-      "Epoch 36/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0730 - ecoder_output_loss: 0.0467 - classifier_output_loss: 0.1728 - ecoder_output_accuracy: 0.0140 - classifier_output_accuracy: 0.9585 - val_loss: 0.0853 - val_ecoder_output_loss: 0.0465 - val_classifier_output_loss: 0.2991 - val_ecoder_output_accuracy: 0.0111 - val_classifier_output_accuracy: 0.9272\n",
-      "Epoch 37/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0720 - ecoder_output_loss: 0.0466 - classifier_output_loss: 0.1659 - ecoder_output_accuracy: 0.0138 - classifier_output_accuracy: 0.9607 - val_loss: 0.0861 - val_ecoder_output_loss: 0.0464 - val_classifier_output_loss: 0.3087 - val_ecoder_output_accuracy: 0.0133 - val_classifier_output_accuracy: 0.9250\n",
-      "Epoch 38/40\n",
-      "235/235 [==============================] - 1s 6ms/step - loss: 0.0719 - ecoder_output_loss: 0.0464 - classifier_output_loss: 0.1666 - ecoder_output_accuracy: 0.0142 - classifier_output_accuracy: 0.9599 - val_loss: 0.0846 - val_ecoder_output_loss: 0.0463 - val_classifier_output_loss: 0.2956 - val_ecoder_output_accuracy: 0.0128 - val_classifier_output_accuracy: 0.9276\n",
-      "Epoch 39/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0711 - ecoder_output_loss: 0.0463 - classifier_output_loss: 0.1606 - ecoder_output_accuracy: 0.0136 - classifier_output_accuracy: 0.9618 - val_loss: 0.0855 - val_ecoder_output_loss: 0.0467 - val_classifier_output_loss: 0.3022 - val_ecoder_output_accuracy: 0.0108 - val_classifier_output_accuracy: 0.9284\n",
-      "Epoch 40/40\n",
-      "235/235 [==============================] - 1s 5ms/step - loss: 0.0704 - ecoder_output_loss: 0.0462 - classifier_output_loss: 0.1563 - ecoder_output_accuracy: 0.0144 - classifier_output_accuracy: 0.9630 - val_loss: 0.0839 - val_ecoder_output_loss: 0.0460 - val_classifier_output_loss: 0.2943 - val_ecoder_output_accuracy: 0.0142 - val_classifier_output_accuracy: 0.9305\n"
+      "Interpreting Model\n",
+      "Topology:\n",
+      "Layer name: encoder_input, layer type: Input\n",
+      "Layer name: dense, layer type: Dense\n",
+      "  -> Activation (relu), layer name: dense\n",
+      "Layer name: dense_1, layer type: Dense\n",
+      "  -> Activation (relu), layer name: dense_1\n",
+      "Layer name: encoder_output, layer type: Dense\n",
+      "  -> Activation (relu), layer name: encoder_output\n",
+      "Layer name: dense_2, layer type: Dense\n",
+      "  -> Activation (relu), layer name: dense_2\n",
+      "Layer name: dense_3, layer type: Dense\n",
+      "  -> Activation (relu), layer name: dense_3\n",
+      "Layer name: ecoder_output, layer type: Dense\n",
+      "  -> Activation (sigmoid), layer name: ecoder_output\n",
+      "-----------------------------------\n",
+      "Configuration\n",
+      "-----------------------------------\n",
+      "Interpreting Model\n",
+      "Topology:\n",
+      "Layer name: encoder_input, layer type: InputLayer, input shapes: [[None, 784]], output shape: [None, 784]\n",
+      "Layer name: dense, layer type: Dense, input shapes: [[None, 784]], output shape: [None, 128]\n",
+      "Layer name: dense_1, layer type: Dense, input shapes: [[None, 128]], output shape: [None, 64]\n",
+      "Layer name: encoder_output, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 2]\n",
+      "Layer name: dense_2, layer type: Dense, input shapes: [[None, 2]], output shape: [None, 64]\n",
+      "Layer name: dense_3, layer type: Dense, input shapes: [[None, 64]], output shape: [None, 128]\n",
+      "Layer name: ecoder_output, layer type: Dense, input shapes: [[None, 128]], output shape: [None, 784]\n",
+      "Creating HLS model\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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09PTu5ZF169ahtWbFihURj7/88stZtGgROTk5ZGRkDOlaAEVFRTzzzDN897vfJS0tjeXLl7N79+4+x/X3u2DbNt/73ve6849effXV7lmSgX5mB2Okv1dCTCVKn57fFOIcopSitLSUkpKSiR6KEEKIYZKZGCGEEEJMSRLECCGEEGLS+P73v8/ixYtZtGhRd9HKaEZes1qIKUhWUYUQYvLZu3cvP//5z9myZQsej4err76a97znPVGX/mUmRgghhBCTwoEDB1izZg1xcXG4XC7WrVvHH/7wh6jHT+qZmIyMDGbOnDnRwxBCCCHGzYkTJ7oLbI6Hq989k/oG37hcyxfK6FGB+5577uGee+7p/n7x4sV86UtfoqGhgdjYWJ555hlWrlwZ9XyTOoiZOXMm27Ztm+hhCCGEEOOmvzftsVDf4GPb3wYufDkaVl75er/v6wsWLODee+/lyiuvJD4+nuXLl2OaZtTjZTlJCCGEEJPG3Xffzfbt23nttddITU1l7ty5UY+d1DMxQgghhDi31NbWkpWVxalTp/jDH/7Qo1FtbwPOxNx1111kZWWxePHiHrf/8Ic/ZP78+SxatIj/9//+X/ft999/PyUlJcybN4/nn3+++/bnnnuOefPmUVJSwje+8Y3hPC8hhBBCTHMf/OAHWbhwIe9973v58Y9/TEpKStRjB5yJufPOO/nMZz7DRz7yke7bXn75ZTZs2MDu3bvxer3U1tYCsH//fh5//HH27dtHZWUlV1xxBYcPHwbg05/+NC+88AIFBQWsWrWK66+/noULF47wqQohhBBiOtm0adOgjx0wiFm7di0nTpzocdtPfvITvvjFL+L1egHIysoCYMOGDdxyyy14vV6Ki4spKSlhy5YtAJSUlHT3ALnlllvYsGGDBDFCCCGEGLZhJfYePnyYTZs2sWbNGtatW9fdebaioqK7ERtAQUEBFRUVUW+P5IEHHmDlypWsXLmSurq64QxPCCGEEOeAYSX2hsNhGhsbefvtt9m6dSs33XQTx44dG5UBnb1nfLy3mQkhhBBi6hhWEFNQUMAHPvABlFKsXr0awzCor68nPz+fsrKy7uPKy8vJz88HiHq7EEIIIcRwDGs56f3vfz8vv/wy4CwtBYNBMjIyuP7663n88ccJBAIcP36c0tJSVq9ezapVqygtLeX48eMEg0Eef/xxrr/++lF9IkIIIYQ4tww4E3PrrbfyyiuvUF9fT0FBAffddx933XUXd911F4sXL8bj8fDII4+glGLRokXcdNNNLFy4EJfLxY9//OPuSns/+tGPuOqqq7Asi7vuuotFixaN+ZMTQgghxPSl9CRu57ty5UppOyCEEOKcMt7vfSuXZ0+atgNDJW0HhBBCCDElSRAjhBBCiClJghghhBBCTEkSxAghhBBiSpIgRgghhBBTkgQxQgghhJiSJIgRQgghxJQkQYwQQgghpqRh9U4SQggxdC3lLdTsrUFryFmcTXJh8kQPSYgpTYIYIYQYB0dePELZlnLssA1A5c5KClblM2f9nAkemRBTlywnCSHEGGuv66Bsczl2yAYNaLBDNuVbKmivbZ/o4QkxZUkQI4QQY6zhcD3a7tumzrZs6g/VT8CIhJgeJIgRQogxZriMiH9tlaGc+4QQwyK/PUIIMcayFmahUH1uV0qRtShrAkYkxPQgQYwQQowxb6KXBdfPx3AZmB4T02NiuAzmv3c+MUkxEz08IaYs2Z0khBDjIGdJDukl6dSXNoDWZMzJwB3nnuhhCTGlSRAjhBDjxB3rJndpzkQPQ4hpQ5aThBBCCDElSRAjhBBCiClJghghhBBCTEkSxAghhBBiSpIgRgghhBBTkgQxQgghhJiSJIgRQgghxJQkQYwQQgghpiQJYoQQQggxJUkQI4QQQogpSYIYIYQQQkxJEsQIIYQQYkqSIEYIIYQQU5IEMUIIIYSYkiSIEUIIIcSU5JroAQghhBBi4nSSynb7pnG62uujejaZiRFCCCHElCRBjBBCCCGmJAlihBBCCDElSRAjhBBCiClJghghhBBCTEkSxAghhBBiSpIgRgghhBBTkgQxQgghhJiSJIgRQgghxJQkQYwQQgghpiQJYoQQQggxJUkQI4QQQogpSYIYIYQQQkwa//M//8OiRYtYvHgxt956K36/P+qxEsQIIYQQYlKoqKjgBz/4Adu2bWPv3r1YlsXjjz8e9XgJYoQQQggxaYTDYXw+H+FwmM7OTvLy8qIeK0GMEEIIIcZFXV0dK1eu7P564IEHetyfn5/PP//zP1NUVERubi7JyclceeWVUc/nGusBCyGEEEIAZGZmsm3btqj3NzU1sWHDBo4fP05KSgp/93d/x29+8xtuv/32iMfLTIwQQgghJoUXX3yR4uJiMjMzcbvdfOADH+DNN9+MerwEMUIIIYSYFIqKinj77bfp7OxEa81LL73EggULoh4vQYwQQgghJoU1a9Zw4403smLFCpYsWYJt29xzzz1Rj5ecGCGEEEJMGvfddx/33XffoI6VmRghhBBCTEkDBjF33XUXWVlZLF68uM993/3ud1FKUV9fD4DWms9+9rOUlJSwdOlSduzY0X3sI488wpw5c5gzZw6PPPLIKD4FIYQQQpyLBgxi7rzzTp577rk+t5eVlbFx40aKioq6b3v22WcpLS2ltLSUBx54gE996lMANDY2ct9997F582a2bNnCfffdR1NT0yg+DSGEEEKcawYMYtauXUtaWlqf27/whS/wrW99C6VU920bNmzgIx/5CEopLrjgApqbm6mqquL5559n/fr1pKWlkZqayvr16yMGRkIIIYQQgzWsxN4NGzaQn5/PsmXLetxeUVFBYWFh9/cFBQVUVFREvT2SBx54oLuCX11d3XCGJ4QQQohzwJCDmM7OTv77v/+bjRs3jsV4uOeee7q3U61cuXJMriGEEEKIqW/Iu5OOHj3K8ePHWbZsGTNnzqS8vJwVK1ZQXV1Nfn4+ZWVl3ceWl5eTn58f9XYhhBBCiOEachCzZMkSamtrOXHiBCdOnKCgoIAdO3aQk5PD9ddfz69+9Su01rz99tskJyeTm5vLVVddxcaNG2lqaqKpqYmNGzdy1VVXjcXzEUIIIcQ5YsAg5tZbb+XCCy/k0KFDFBQU8OCDD0Y99tprr2XWrFmUlJTw8Y9/nP/7v/8DIC0tjS9/+cusWrWKVatW8ZWvfCVisrAQQgghxGANmBPz2GOP9Xv/iRMnuv9fKcWPf/zjiMfddddd3HXXXUMbnRBCCCFEFFKxVwghhBBTkgQxQgghhJiSJIgRQgghxJQkQYwQQgghpiQJYoQQQggxJUkQI4QQQogpSYIYIYQQQkxJEsQIIYQQYkqSIEYIIYQQU5IEMUIIIYSYkiSIEUIIIcSUJEGMEEIIIaYkCWKEEEIIMSVJECOEEEKIKck10QMQQgghxMSxajXNP/ZP9DCGRWZihBBCCDElSRAjhBBCiClJlpOEEJNKyBfi6N+OUbuvBpQie3E2sy+fhcsrf66EED3JXwUhxKRh2zbbfrkdX5MPbWkAKrZX0HyymdWfWIVSaoJHKISYTGQ5SQgxadQfaiDQGugOYAC0pfE1+Wg82jiBIxNCTEYSxAghJo226jasoNXnditk0VbdPgEjEkJMZhLECCEmjbjUWAx33z9LptskNjV2AkYkhJjMJIgRQkwaWYuyMN0mnJ36osD0mmTOz5iwcQkhJicJYoQQk4bpNll59/mkFKagDIUyFKkzUll190oMU/5cCSF6kt1JQoghs4IWpS+UUrW7GjtskzozlXnXziU+I37E545Li+P8j67ACjm5MabbHPE5hRDTk3y0EUIM2a7f7qZqZzV2yAYNTceb2PbgdoIdwWGdT2tnB5K/NdB9m+k2JYARQvRLZmKEEEPSVt1Ga2UrtmX3uN0KW1Rsr6B4bfGQztd8qpl9f9hHsDMEGuKz4lnyd4uJTZFEXiFE/2QmRggxJJ31nRGLzumwpq2qbUjn8rcG2PWb3fhbAtghGzts01bVxvaHdqBtPfAJhBDnNAlihBBDEpcZj9Z9AwzDZZCYmzikc1XurMS2e87ooCHsD9N4TIrbCSH6J0GMEGJIErMTSM5P6rNbyHAZ5J+fP6Rz+Zv9Parznqa1JtAWiPAIIYQ4Q4IYIcSQLbt1Gbnn5XbXdEmdlcrKu8/HE+8Z0nlSZ6ZgRihuh4ak/KRRGq0QYrqSxF4hxJCZHpP5181j/nXzRnSe7EXZnHj9ZI+Gj4bbIGNOBglZCaMxVCHENCZBjBBiwhgug1UfW8mJN05Su7cWw22QvzKfgiEuSwkhzk0SxAghJpTL66Lk8tmUXD57oocihJhiJCdGCCGEEFOSBDFCCCGEmJIkiBFCCCHElCRBjBBCCCGmJAlihBBCCDElSRAjhBBCiClJghghhJiiGtsDnGroIBCyJnooQkwIqRMjhBBTTGcgzGNbTlLd4sc0FJatWTcvi0vmZE700IQYkUOHDnHzzTd3f3/s2DH+4z/+g89//vMRj5cgRgghppgntp2istmHrSFsO+0aXjtcS2ail3k50nNKTF3z5s1j165dAFiWRX5+PjfccEPU42U5SQghppBWX4iKJieAOVvI0rx1pH5iBiXEGHjppZeYPXs2M2bMiHqMzMQIIcQU4gtaGIaiTxQDdATDEzAiIQavrq6OlStXdn9/zz33cM8990Q89vHHH+fWW2/t93wSxAghxBSSkehBRbjdUFCSlTju4xFiKDIzM9m2bduAxwWDQf785z9z//3393ucLCcJcY5pq2rjwF8OsPux3VTsqMQKy86WqcQ0DK5enIvbVGfdpojzuCSxV0wbzz77LCtWrCA7O7vf42QmRohzSOWuKg799RC2ZYOGxuNNlG8pY+XdKzHd5oCPDwfDnHrjFNXv1KAMRd6KPArXFGCY8nloPC0vSiUt3sNbx+pp9YWZnRnPBbMyiPPKn3QxPTz22GMDLiWBBDFCnDOskMXhZw5hh+3u2+yQTWeDj8odlRSuKez38bZts/2hHXTWd3af49jLx2g81sjy25ahVKRFDjFWitLjKUqPn+hhCDHqOjo6eOGFF/jZz3424LESxAhxjmitaHUSJ3qxwzY1+2sHDGLKt5TTUdeBts4klNphm+ZTzbRWtpGYm0DNOzVU7a5GGZB3Xh5ZC7OmfHCjtaassZPGjiDZyTHkJsdO9JCEmNbi4+NpaGgY1LESxAhxjnB5XdB3QwsA7pjofwq0rdn7h33UHqgFO/L9LWXNHH/1OE0nmrBDzkHNp1qoP9zAohsWjsbwJ0RnMMyv3jxBU0cQ0GigIDWOD62ZgUuW0ISYcPJbKMQ5Ij47Hnecu8/thtugYHVB1MeVb6+g/nB9xAAGwDANwv4wzSeauwMYcJaqag/U0lbdNuKxT5Snd1dS1+YnaNkELU3IcmZlXjlUO9FDE0IgQYwQ057WmlOby9j0ndfxt/hBgTIVptfEcBnMuHgG6bPToz6+YltFj+CkN9NtYIUsrAj9e7StaTreNCrPY7xZts2h6tY+5VjCtmbnqan5nISYbmQ5SYhprnxrOUdfOtojEFEocpfnUnzpTDzxnn4f318AE5cex7Jbl1J/uB5lqh75MgCGYeCO7Tv7MxXYOurqW3epfyHExJKZGDE4ocPQ/itoexACO0FHf2MTk8vxV0/0CURsy6b+UP2AAQxA1uIslNk3Odeb6OWCT68hLj2O7CXZqAhJwyjIXDA1a5e4TYO8CEm8CpibLUXlhJgMBgxi7rrrLrKysli8eHH3bf/yL//C/PnzWbp0KTfccAPNzc3d991///2UlJQwb948nn/++e7bn3vuOebNm0dJSQnf+MY3RvdZiLHV+TS0/QKCOyD0DnQ85nwvgcykp21NqDMU8b5Aa2BQ55h50QxiU2Mx3c6fC2UqTLfBog8u6t555E3wsvTmJbhiXJgeE9Nj4o5zc97ty52E4inqvcvz8boMXF0BmttUxHldrF+YM8EjE0LAIJaT7rzzTj7zmc/wkY98pPu29evXc//99+Nyubj33nu5//77+eY3v8n+/ft5/PHH2bdvH5WVlVxxxRUcPnwYgE9/+tO88MILFBQUsGrVKq6//noWLpy6uxbOGVYD+F8Bzu7JEoTwEQgdBI/8G05mylB4k7wRA5a49LhBncMV42L1J1ZRu6+WphNNxKTEkndeHjFJ3h7Hpc9O59J/voTWilaUUiTlJ0WenZlCspNi+Id3z2XnySZq2/zkp8ayvDAV7yAKAwohxt6AQczatWs5ceJEj9uuvPLK7v+/4IILeOqppwDYsGEDt9xyC16vl+LiYkpKStiyZQsAJSUlzJo1C4BbbrmFDRs2SBAzFYRLiTxhF4TgXgliJrlwMEzGvAwqt1eiz8rjMFwGJVfMHvR5TJdJ7rJccpfl9nucYRqkFKUMd7iTUrzXxSVzR74kprXmVGMnx+raifWYLM5LIaGfre1CiIGN+Dfol7/8JTfffDMAFRUVXHDBBd33FRQUUFFRAUBhYWGP2zdv3hzxfA888AAPPPAA4HS7FBNMxUDkdnNgDO6TvJgYLeUt7PzNrp7ZqQriM+OZs76E9JLoO5KiCbQFCLYHiUuPw/TIbMRg2Vrz5NZTHK1rJ2RpXIbibwdquHlVEbOlaaMQwzaiIOa//uu/cLlc3HbbbaM1nh5tuc9u1y0miHshkYMYE7yrx3s0YpC0rdn9+B6sQM9tz6bLYNZls4YcwIQDYfb+YR9Nx5qcXUi2pnhdMTMvnjGaw5629lW0dAcwcGZ301Pby/jnqxZgTvFlNzG1udPcFN4avVbUqPrL6J5u2LuTHn74YZ5++mkeffTR7uS+/Px8ysrKuo8pLy8nPz8/6u1iClAeSPwEqFggpuvLDfE3gZk1wYMT0bRUtEbcGm2FbCp3VAz5fPv/dIDGo43YYRsrYGGHbI6/etyp4isGtLusuTuAOZvWUN7UOQEjEmJ6GNZMzHPPPce3vvUtXn31VeLiziwpXH/99XzoQx/iH//xH6msrKS0tJTVq1ejtaa0tJTjx4+Tn5/P448/zm9/+9tRexJijLmLIeXrTjKvDoO7BJR34MeJCaMtO/IEGmBHeDPtT8gXor60vk8NGDtkc/KNk2QtkGB2IP21j5I5GCGGb8Ag5tZbb+WVV16hvr6egoIC7rvvPu6//34CgQDr168HnOTen/70pyxatIibbrqJhQsX4nK5+PGPf4xpOuvmP/rRj7jqqquwLIu77rqLRYsWje0zE6NLmeCeN9GjEIOUXJAc8XbDbZC7rO/2YCtkUX+4nrA/TGpxKnFpZz6chHwhlNG3kB1AoD04eoOexs4rSuVkQ0ef2RhDKQpSJbdMiOFSWutJW3py5cqVbNu2baKHIcSUVH+4nnee2ou2NdrSmG6D2LQ4UBBsC5BUkMzsy2dhhWx2/XoXWuvuHUz55+cx56o5KKWwbZtN336dsD/c8wIKcpfmsPD9skNtIFpr/rSznANVrVi2xjQUCsWHLpjBjPT4iR6emGTG+71v8eLF/OEPfxiXa33oQx8a1ecm+/uEmKYy5mZw4WcuoHp3NUFfCB22qdxV1Z0rU3+onqZjTaDACvZMAK7cUUnarDQy5mbQUdtB1qIsqnZVdc/GKENhekyKLyse9+c1lTR3BtlxspEWX5hZmQmsmpnOycYOYt0mC/OSiZF6M0KMiAQxQkxjMUkxzLx0JrZl89q3N/VJ9o3UtNG53eboK8c4+dap7uJ1SikMr4En3kPa7DRmXjyDmOSY8XgaU9LR2jZ+t/UUtq2xNByoaiU1zs3dl87C45LgRYjRIEGMEOeA2gO1fWZbBtJe1d7nNqUVSbmJzL9W8qP6Y2vNH3aU98iBCVk2jR1BNh9r4NK5kgwtxGiQBpBCTHMNRxrY/6cD0VsyD4G2NLUH6rBt6ZsF0NAe4KX91Ty9u4LSmjZOpxjWtQUIR0iEDtuavRUt4z1MIaYtmYkRYpo7/FxpxJ1Fw6W1Bptz/iPQO+XN/HlXBbbW2Br2lLcwIz2O9y7L40htG5Yd+TV3m+f4CyfEKJIgRohpTGtNZ8PoFlNLykvCcJ3bb8TBsMVfdld0V94FZ7noSG07//PCYQycOK83t6lYVZw2buMUYro7t/8SCTHNKaVwx7pHdo6ukvjKpTC9Jguunz8aQ5tytNZ0BsKELZuTDZ30V5yidwDjNhUuQ7EoL5mlBSljOUwhzikyEyPENDfjkiKOvXI8YhuCHpRTDE+hQDv9l/JX5ROT5KWlopWErHjyVuTjTfCMz8Anic5gmBf3V7O3ogXL1iigMC2uxyxMf9yGYnlhKmtmpZOeIJWuhRhNEsQIMc0VXVhEOGBx6q1TaK1RGjBUj6BGmQpPgoeknEQMt0HQFyI2JYacJdkk5SVN3OAn2KmGDn791ok+AcuJISzRKQUFqXESwAgxBiSIOdeFq8D3LFinwMiA2KvAPWeiRyVGkVKKWZcVE2wPUrW7CmUaaK1xxbpQSqG1xgpYBFoD1LUEuh/XBFTvqaFwdQElV5RM3BOYIFprntxWNugZl2hsDbOzEkZpVEKIs0lOzLksXA6t/wOhd8Budho8tj0AgZ0TPTIxyqp2VVH9TjXa0lhBpwt12B8mLi0OO2Q77QYivFfbIZuyzeW01/atGTPd1bT6CYZHtpXcNODdC7KJ98rnRSHGgvxmncs6/wL0buAXgs4/gmd5/613B0PbYNeBigXj3F2SGA7btrv6HY1OZdeyzeV9c2I0tFa2YAxQPda2bOoP1ZNwjs0mKKWGVVrHUJAS52FWZjznz0gjJzl21McmhHBIEHMus05Fvl13gO4ENYLGdIHd0PkE6BBgg6sYEu4A49x6IxwqK2hx6NlD1LxTg21r4jPjWfCe+SQXRu5KPVjhQDjyHV3LSf1RhkKdg7VNshK9xHlMWnz9z8a4DEVSrJtWXwjL1sS4DC6dk8nyotRxGqkQ565z7y+TOEMlRrnDADWCJMRwOXT8xgmGCAJhCB+Ftp8N/5zniD2/20P1OzXYlrO801HbwY5f76TpVDPtNe3Y1vCWNzLnZ6DMvjNrrhgXA823KaXIWpg5rOtOZUopbl5VhNdl4DIiv0oKSI330OYPEbY1GugM2TzzTiUHq1rHdbxCnItkJuZcFrMeOp+k55KSG7wXgBrBj4b/VaD3J38brGonkdiVO/xzT2OdDZ00n2rpU13XDtnseHiHs7SkYN61c8ldOrTXcOalM6k9UEeoI4Qdtp3t1C6DgvPzOfnmyYiPMdwGaOd6sSnTd0kkZNnsPNnI3ooWErwu1szOYEa6MwuZmxLLP145jwNVrew42URFcydnx5EuU9HUEeyT/BuyNC8frGF+riyjCjGWJIg5l3lXgm4B30acz5QWeFZC3PtGdl67kYhZosoE3QpIEBNJZ6PPmS2JtPKj6W7gePDpQ8Slxg1pickT5+GCT62hckcljcebiE2JoWB1AYefPYwd7vtvpUzFjIuKyF9ZMK3rwoQtmx+/dJgW/5kX/WB1G++an9XdpNHjMllWmMrSghTeOlrPW0cb8IUs8lJiuWxeJr/dHHlZtsUXGpfnIMS5TIKYc5lSEHsFxKxzdicZiaBiRn5e11wIn6TPu7EOg5k/8vNPUwlZ8YPqcWSHbE6+dYqlhUuGdH6X10XRhUUUXVjUfVu0ztaGyyBtVtq0DmAAXjlY2yOAASf8fvlgLStnphPrOZP0rJTiopJMLio5s7SmtcZUCitC0J6VOAq/S0KIfklOjADlBjNzdAIYgJhLQMUBZ+968TjBkiT2RhWTHEPmgkxnGWcAgRb/qFwze0l21Osl5U//pZCdZU0Rb9fA4eqBc1reKW8mHKGjt6HgioXZIx2eEGIAMhMjRp8RD8n/Ar4XIbTX2eUUcxl4zpvokU16C9+/gJOvx1G+tYKwP9Sd4Hs2ZSrSStJ73NZW1UbpxlJaKlpxx7qZcVERBasLUANsk89bkUfV7mo66zqxQpazE8lQLHzfAoxzYEdStE7TwKCK3P3tYC2RDvO6TYrSR7C7TwgxKBLEiLFhJEL8DcANEz2SKcUwDIrXFlO8thiA45tOcGLTiR41XrStqdlbg8trUrimEF+jj+0P7cAKOUtDgVCAIy8dxd8aYM76/ivtmi6TlXedT+2BOhpKG/AkeMhfkUdcetzYPclJpDgjnoPVbRHvWzSIdgutUfJefEHLafEw0lpLQoh+SRAjxCRWfOlMEjLjOfnmSVor27or6/oafRx7+TiNRxtxxbqxwj1zW+yQTfmWcorXzsQ1QLVYwzTIWZxNzuJzb/njqsW5HKlt7zPrsnJmGjGegf88psR5aOrsXTASkmPdEsAIMQ6m/3yxGHt2u1MbRgcGPlYMWeb8TGZcNMNZ3jnrvdYO2zSXtdBS1hJlM5jC1+Qbv4FOQSlxHj51WQnzcxLxugxS4ty8b3k+1y4Z3A66KxZk4+5Vf8dlKN694NwLCIWYCDITI4ZPh6DjUQjuxflRsiDm3U4TSfkUOqqay1oi7iTStsbldRGgbwBpBSx2Pbqbkitmk7tMtrVHk5bg5ebVM4b12IX5ySgFLx6oobkzSHKsh8sXZLE4P2V0BymEiEiCGDF8HU91BTBhurdT+/8GRirErIn+OLsD7FYw00ZWGXgaCQfClG0tp+5AHZ44N4VrCkk/K3k3JsmL4Tb69D8yTIOcpdkcf+1E395IQLA9yMG/HiLkC9HZ4KPpeCPepBhmXFxE+uz0PseLoVuQl8yCvJG1hRBCDI8EMWJ4dBCC2+lbmS0I/pciBzE6DB2/g+BOnO3XdledmivP6ZmbcDDMlge2EmgNONV0gaYTzRReUEjBynw8CR5yluZw9OVjfR+soGBVAYm5iRx65jC+xr7LR3bIpvT5I049Qw2dDT5ayluYc2UJBSsLxvjZTR91bX5ePlhLWWMnSbFu1s7NZF7O9N+GLsRkJjkxYnj6y3/R7ZFv7/wDBHfhBD4BIAS+lyCwZfTHN4VUbq/sEcCAk+9y8vWTvPGDN3nt25uo3lPNio+cR2xqDGc3O9K25q0fvk1saiwX/cOF/V/o7HyakM2RF472uKaIrq7Nzy9eO8aBqlbaA2Eqm338fnsZW483TPTQhDinSRAjhkcldBW063MHuGb3vVmHu4KV3ltSu2ZuzlGVu6o48mI/wYTt5LYceekoJ984ib810CcYCXYG2fvUPgA8Q6yw29nYOdyhTylHa9v51ZvH+dFLh3l6dwUtvr47igA6A2EOVLVyvL4d+6zu3i8frCXUq/lmyNK8dKCm31ozQoixJctJYniUgrgboePXnAlMDMANcdf1PV4HiLiFBkBHrtMx3Z3aXEbp86VRX5az2SGb2v11ke/U0F7bTqA9MKhqv93ntGzccdO7rQDAjhONPLevilBXS4emziD7Klv55LrZJJ/1/N8oreOVQ7WYhkJr8LgMPnzhTLKSYjjZ0BHxn8nWmlZfiNT46f86CjEZyUyMGD7vUkj8e3AvBCMLPKucSr1mTt9jVZwzexOJq3hsxzkJNZ1sGnQAMxhKKbQNYV+k7pGRnQu9kSzbZuP+6u4ABsDWEAhbvHb4TFB4or6DVw/XErY1gbBN0LJpD4T5zdsnaPUF8UXpMWVriPOYEe8TQow9mYkRI+MuBvc9Ax+nFMR9sNfMjQLcEPueMRzg5HT42dELYABiUmKISfISlx5Ha8XAPX+8yV4Wf3DR6A1gErFtTWWzD6XAZTqzKr1pDcfrndytzkCYTYdrewQ6pwXCNq8drkMpIp6nMDUOr1uCGCEmigQxYvx4l4LxKfBtBLseXDMg9kowz73CYO21UZKfh8FwGyz+gBOQzL58Frsf2zNgwu7yW5cNWMl3Kjpe186T28qwuiIOl0GfXJbTEmLcbNhZzjsVLT3yX86mgFZ/KGJ/JAXMy0kcpZELIYZj+v0VE5Obexa4PznRo5hw7hgXoSEs/UTjTfKy5pOrcce6AUgtTmXGxTM49fYprEDkJRCAqj3VzL581rRq8tgRCPPYlpM9ZlQip+863IZib2VLv4m5ltYszk/mZH0HwV4zNaapmJUpXdmFmEjT5y+YEJOc1prOhk58zT4KLywaUhJuJAnZCVz46Qu6Axg7bLP94R2ceqv/AAagbHMZe3+/b0TXn2z2VrREXPKJ5nh9B+EIS0jQtdBpKq5elMvivBQyE2NwGWf2trtNxfycJLKSYkY4aiHESMhMjBDjoPlUM3t/v4+QLwTayWFJzE2k5VTLsM7ninFx3oeXY56VVFq2pYy2yrZB1X7RlqbuQB37NxxgwfXzp1SzwubOIPsqWwiFbebmJJGXEgtAZzDcp5Fjf/o78ryiFM6fmd597jsuLmbr8QbeKW/BNBUrZ6SxrDBlBM9CCDEaJIgRYowF2gPsenR3j95HnfUjq89ihSxOvXWKkitKum+r2lU95OJ11e9Uk5iTQOGawhGNZ7zsKWviL7sr0WgsGzaV1lGYGscta4qYmRHPW0frIyboRmIoIua6FKbG8d7lPSsZu02Di0oyuagkczSehhBilMhykhBjrGpXNXaU5NLh0pam8XhTzxuHMZmiLc2pt8pGZ1BjzBe0+MvuSsK2E8CAE4ScbOzke88fIsZlUJgahzHI16F3AKNU1xLSIDtYCyEmngQxQowxf4sfPcjZgaGI7VrqOC3vvDyUOfRIJuTvXUV5cjpa14YRJUIJ2Zont5XR4gsOKS8GnNjPYxosyU/m42tndy8hCSEmPwlihBhjqTNTe+SujAZbQUthIvqsd+yCVfmowU5DnCV1RsoojmzsqAGmmlr9IVr94SGX39E4u5CuWpRLZqIk6gox0Zqbm7nxxhuZP38+CxYs4K233op6rOTEiMlP2+B/Bfx/A93hVASO+wC450z0yAYlc34GJzbF0tnQgR0e+YyMBhoS3RxraCe+rInzitIAaDrehB0a2rKV6TEpWV8y8IGTQElWQo+grTetnRmZ4TCVoiMYJm4a1s4RYqr53Oc+x9VXX81TTz1FMBikszN6DqHMxIjJz/dX8D3b1R1bg1UFbQ9A+MREj2xQDNNg5V3nM+OSmcSlxxGTEjOoGRPDbZBUkNTndgVktIaIa/Kz6azS+YeeOzykcbkT3Kz51GriM+KH9LiJ4nWb3HBeQdScl1iPiWcYy2ng5MOkSf8jISZcS0sLr732GnfffTcAHo+HlJSUqMfLxw4xuekg+F+jb/frEHQ+B0lTo3Ce6TGZta6YWeucPlENxxrZ9ZtdUff5xqTGUHRBEcHOIK3lkdsIzDvRys4ULwDa1vgafEMaU9a8zD55NZPdgrxkPpsSy6/eOk5zp1NJ120qTENxy+oifreljJAVeUkp2m4kgGWFKZiGfKYT56YY22R2+/gUbqyrq2PlypXd399zzz3cc8+Z1jXHjx8nMzOTj370o+zevZvzzz+f73//+8THR/6wJUGMmNzsFqJuu7Grx3UoQ2VZFjXv1FJ/uA53rIf88/NIyuuaWbE1pseMWpQu3Bkm77xcTr11KuL9CnDZmgKXq/sGl9ckPECRu9NMt0nm/Km5XTg5zsNnLp/LifoOypo6SYxxszAvCa/L5O5LZ/GnHeWcbOw7/dzfStPOU01kJnhZWZw+hiMXQmRmZrJt27ao94fDYXbs2MEPf/hD1qxZw+c+9zm+8Y1v8J//+Z8Rj5ePHucKrSF0DALbwJrcb/49GMlEna4wInTLniTqDtXx6v2vcWDDAeoO1FO5o5JtD22nbIuznTkpL6nfHUtaa469fIzqd2qiHwNc2PWmq5QadBVg022QVpJG2uy0oT2pSUQpRXFmAmvnZnFeUSpel4k/ZNEeCHPZ/Cw8Q2ynELI0LxyoITzKW+GFEENTUFBAQUEBa9asAeDGG29kx44dUY+XmZhzgd0OrT8C+3RdERvc8yDho6AmewdeF7hKIHyQnsGMG+KunqhB9aulrIV3ntzbJ0jRYc3h50rpbPJRtLqQGRcXcWLTSXSEKQIrbFG2pbzfQMfjdVEyJ6P7++K1M7ECYcq3VqAMhRW2QIPhMkA7S05ps1IpWFVA+pz0KVWltz9aa148UMOWYw2YhiJsa+zhJPhqaOgIki2tBISYMDk5ORQWFnLo0CHmzZvHSy+9xMKFC6MeL0HMuaD9UbBrgbM+ZYYOObt9YtdP2LAGpf1XED5CnwAm4WPgmjlBg+rf8ddORA8+NJS/XU7F1gqW3LiYoouKOPnGyb6TTTboqDNQTrLwkhsX9UgQVkox58o5zLpsFoH2AN5ELyFfiMZjTbi8Julz0jFdkz1oHbodJxvZcqyesE132wGlQOmeL6uhnGW4aP80ltbEe+RPohAT7Yc//CG33XYbwWCQWbNm8dBDD0U9Vn5jpzsdgPBhegQwAIQg8ObkDmLCZRDaR9+kXgMYXO7HROhsGLilgLY0e373Dqs+vpK2ylaay1q6t0crl3KCmAizCcqlKLqgkIKVBcQkR54xMD0mcWlxzv+7TfKWT78KtLbW1LT6MZXipQM19O62oLUTtHhNhT+scRmKvJQYLBuqW330XjUylWJWRjwJMfInUYiJtnz58n7zZs4mv7HTnQ73c19w/MYxHOFjRM6HCUCoFDzRpxgnUmJeIr6mwe0U2vP4O1z4uQuo219H9Ts1mG6T7CXZ7Pv9vohBjLY0ZW+XY4c0c64sGVZxu6nueF07v99eRsjShG2734RdlMJUmrCtOdXow20q1s3NorrVz+HqNgylsLSmOCOeD54/NfpHCSHOkCBmujPiwcgAu3eCqAHuxRMypEFTCYAJ9A7EXGD0rZ8yWRSvnUnD4XqsQRSeC/lC+Br95CzJIWfJmUTlhqXZVO6s6hvDabDDNhU7KjBcqkcDyOmkqSPI5uMNNLQHKEqLY+XMNGI9Ltr8IR7bcnJQTR5NpQiE7B4vYcjSvH2sgX+6aj62rWloDxLvNUmIcY/dkxFCjBnZnXQuSLgN8HImZvWASoS46yZwUIPgWQIq0o+oAd6VEW6fHBKyEljx0fNJLkwe8FhlqIgzLkUXFvX7ODtkU76lIuJjp7qTDR385JVSth5v4EhtO68druNHfyulpTPIG0fqsQb5nF2mEXEeL2TZNHcGcZkG2ckxEsAIMYXJTMy5wFUEKf8G/recGRmzGGJWg5oEuzC0HSVQAZQHEj8N7Q+C3QEoUG5IuBOMxPEc5ZDFJHnpqOsY8DjTY5KQ1beIU1tlG6bbxApGz/2xLRsrZOGaRqXytdb8eVdFj5mWsK0JBy1+8soRQtYAy0dd3KYibEeeCbM1xLqnX4KzEOei6fPXT/TPSJ5cW5JDx6DzKbAqAS/EXAqx1/Td8u0qgOSvdNW2scDMix70TCLHNx0n7O8nH6mLN9HLnifewdfQSVxGPDMvnUlSbiLueLezRbofngTPqDeWHG8hy2ZfRQsVTZ1kJHqZk51Iiy9yV+1A7+zdfs8bOdIxlaIkM4FY2YUkxLQgv8li/IUroe2nwOnE4gD4XwW7DRJu7Xu8UuCaWjtsqndHL1J3traqNtqq2gDoqOuk4UgDy25ZStPxpr4bys5iuA3mXDVnStd66QiE+flrR+kIhAnbGkPBi/uriTKBMmKmoZiRHsf7VxSMzQWEEONOgpipTFsQOuDMUphZ4F40BYrXAf4XiNgLKbgd7PeCMT49PMaCFbY48KcDg5qFicQO2Rz866F+dzclZMcz58o5pM2a3BV3Ldtm+4lGdpU1o5TivKIUVhSlYXTtqPrzrooesy62PtMaQBG1TvOwuEzFVQtzpK2AENOMBDFTld0Brf8LdivOjIbH2YmU9PnR3bljt3UFF23gngOuuf0v59it4N/kdJg2cyBmHZgZPY8JVxH5LcoFduOUDmJKnyul7lD9iM7ha+x/e7aGSR/AaK35zVsnqGj2dS/t1LX5Ka1p55bVRQTCNodr2vo9h6nANAysrm3UIwlqDBQZiZMgB0wIMaokiJmqOv8IdgNn1hwCYIeg40lIvDv64+w28L8B1kknvyTmUjBSIh8bOgJtD3RdI+wEJ64ZkPgJUBF+dKx6aP0u6JBzfPgoBDZD4qfAXXzmOFc+BGvo+7YUBmMUPimHq8C/EcLlTiAVeyW4Rr8GSDgQpvFYE8pQpM1KRRmKqt3V2EPI3RiOjpoOtK0ndY2YY3UdVJ4VwICTp3K4po0fvHSYWZn9B6oa0ArCtk1Wope8lDjeqWjuPl9/HakjifGYzEiPG85TEUJMYgNmSN51111kZWWxePGZmiKNjY2sX7+eOXPmsH79epqanJ48Wms++9nPUlJSwtKlS3s0bXrkkUeYM2cOc+bM4ZFHHhmDp3KOCe6mb9KE7VS41dHqqtdBy3+D/0VnGcr/CjTf77zZ96ZtaH8YZ5bn9NJIEMInncAkks4NoP1nHW87j+n4Xc/jYtcDvbe1usG7xplNGonwKWj9HwjuArsOQnuh9QcQOjyy8/ZS9U41m77zOvs37GffH/ex6TuvU3+4fly2PBtuI2pj78niZEMHwSjJtc2dIXaebIp439ls2wlUatsCVDX7eP95BcxMjyM9wcPc7ESuWpTNvJxEBorlkmPdfOTCmVM6f0gIEdmAQcydd97Jc8891+O2b3zjG7z73e+mtLSUd7/73XzjG98A4Nlnn6W0tJTS0lIeeOABPvWpTwFO0HPfffexefNmtmzZwn333dcd+Ihx1PmHXkGGBQSc2Ru73amCa3UthVgVXTMqvQUhsCXy+UOHiTjpb9c67Q9OM3Mg6dNgzgQMp6hd7HqI++Awn9hZOv7ojLF7HBoIQcfvR37uLr4mHwf/fBA7bGMFLOcraLHvj/txx49tzRHDZZB/fj52yKZsSzk7f7OL/RsO0FrV/9LMeEuIceHqJ7oYSqhna6hr95Ma78GyodUX4khtOy8frKOu1c+ywhRchsKMEKSkxLn55LrZpCd4h/EshBCT3YBBzNq1a0lL67n+vmHDBu644w4A7rjjDv70pz913/6Rj3wEpRQXXHABzc3NVFVV8fzzz7N+/XrS0tJITU1l/fr1fQIjMUSeJfT95zPAvcDZzRNJqHcjxS7WSWj+GrQ9CC3fhNYfd7UkiPJWEyknxu6vX5Ciz8qlawYkfx7SvgepX3eWfAazdTraLNNpVlnk2+1aJxF6FFS/Ux11xiVjTkbE24fDFedyOlADylQYpkHGvAxmXjqDLQ9s5ciLR2g82kjV7iq2P7Sdqj1Vo3btkVqcn4wxiJmPwW6WD9vwyBvHqWzu7Go3oAlaNs2+EJ0Bi09eVsLSwuQ+szJt/jB/3Fkx9CcghJgShpUTU1NTQ26us+U1JyeHmhpnO2lFRQWFhWdyDwoKCqioqIh6eyQPPPAADzzwAAB1dXXDGd65Ie4GJ3nW7gACgNcpXhf/d9Efo9xRZlfAmZ3pmqEJH3OWmlRchP5KHvBe2PMmuxNav8OZLdNnc4Fn+ch2TWkN/pfB/xLoDjAynecfqXeSigUdaVbCw2gVqA4HrMh9jcKaUJQaJ5G4Yl3YIRutdZ+u14bbYOH1C8mcl0GgPUhnQydxabF4E72ceP0E/hb/mdwb7exqOvTXw2QtzJoUnarjPC5uv3AmT247Rbs/HHXmJTXeQ2cwjNYQsnW/1Xgj1YmxNRyubaO2zU9TZ9/X3rI1R+va6QyEiZtGRQGFEI4R/1VXSo3qWvM999zDtm3b2LZtG5mZmaN23mnHSITkf4P4WyDmKoi/CVK+HD1JF7qCj8Esd3Rt3U64vauqrxenh5HHmenx9Cr5H3i9a5dUpDcgt7NE5X+9/2aU/fE9D77nnAAGnFyX9oec5a/eYi4nYr5NzCXRZ6iGKGNuBqa776+O1pq6A4MPvMP+MO54NwWrCihYU4DpMVGGwpPgYf575pM5z5nV8SZ4SClK7l6qqjtQFzl5WEF7VfvwntQYKEyL4wvr53H1klxcUf7SNHQECVk2/rA96HYCvWlNxADmNFMpOoLD/NkTQkxqw/pokp2dTVVVFbm5uVRVVZGVlQVAfn4+ZWVnpvPLy8vJz88nPz+fV155pcftl1122YgGLnB2CHnPG/zxsdeAVQOhQzhBiYWTfBtpmUU5W6NT7oPgO2DVgpEauZ9RcB99mzSe5gPrBHRWQHAbJP6DMyujw+B7BgJvOrM95gyI/6BTofdsOgz+v9F3licEvmedbd9ni1kHdosTWOFyxuU5H2KvHfj1GaSUomTS5qRTt3+EM4UaAi0BKrZXMOPiGay7dy1WyHKCma6Ay7Zsjr50lIptlVghi4TsBJQZORjTtsaMmVyzDUopVhen43EZbNxbhS9CU8wx3syFUpAW7xnbiwghJsSwZmKuv/767h1GjzzyCO973/u6b//Vr36F1pq3336b5ORkcnNzueqqq9i4cSNNTU00NTWxceNGrrrqqtF7FmJwlAsSPwbJ/wIJH4KkfwTPeUTe6hKClm9B518gsNMJJDo3OLkzHU85u5dOG1RdmpBTqTe01/m24zfOlm3tB2ywjkPrD8FqOPMQqwnCx4mam2NFqIqrDIh/P6T8JyR9xgnCEm4Z1SKASincsaOXwGuHbE6+cRI7bKNMRf2heqp2V+FvDbB/wwHKt1ZghZxAs72mnfaadpSr17+ZgtiUGOLSYjnxxkle/583eOUbr7L78T10NvaXrzQ+5mYnMhZbqgYzubZmVjqmMflbVQghhm7Aj2233norr7zyCvX19RQUFHDffffxxS9+kZtuuokHH3yQGTNm8MQTTwBw7bXX8swzz1BSUkJcXBwPPfQQAGlpaXz5y19m1apVAHzlK1/pkywsxpGZ5XwBxF4Hof1du4d6zcjoTgi8wZkgomu2JbDZqecS+y7n+5i1EDpI3yq8vQUheADMIgjupe/sTcjJfYm5HNp/2dUvSUU/r5kT/VJGLBhjV16+Zs/g2goMllKKukN1HPrrYXRX8rJt2U7uTa8YTtuahMx4Ohs6US4DNHjiPSy7dRkH/3KQmv212F0zHvWH62k+2cwFf78Gb+LE7dDZfqKRwAC9oIYjxmUQtKLn0hgKLpg1esnWQojJZcAg5rHHHot4+0svvdTnNqUUP/7xjyMef9ddd3HXXXcNcXhizJmpkHyv07sosAd0Iz3rz0R6cwhB4NUzQYx7DsRdD51/xpncC9G3hk2X4NsQ3BzlvDaEy6DtR2A3RTnmNPeoLhENlW0NcQ3EgLj0ODrrIs+K2JbNoWcOEw4MIndDA0px8RcuprWiFXecm6T8JAJtQWr21vYcmwYrZHHq7TLmrC8Z2phHyYHKFl45VDuk4nQKKM6Mp6E9GLUhJIAvZBPjNiIGMW5TsbwwldizmmS2+II0d4bISPASL4m+Qkx58ls8nWnbyX+xTjkJv57loCJ8GjeSnSAEuvJPBsHuVRo/5lJwFUPbz7qWmvp7k4/2bubMKmC3RzhG4STshsHIdpaM3LO7xtLuFPkDp3/UOLQtSJmR4jRpjCAm2Ut8VgJNJ5qwQzauWBd5K/I49cap6CfUEB5s8qmCxNxEPPEeMuaemWXoqOtaZuo9oWZpWstbB3fuUVbV7OMPO8sHDGDS4jw0dp7Je9LAqYYOVhdnsOV4fb95M3ddMovXDtdRWt1G2NbYWhPrMblwdjoXlzibA0KWzVPbyjhW145pKCxbs7wolWuX5EoRPCGmMAlipisddOq9WFV091bq/BMkfjZ6R2gj0zku4lbpXlyzel1PQ/uDUbY3n04iHogNdnmUYzV4FkHCHT1v9m+Fzt9xJr3rSYi7CWJWD+J6Q2NbNvWH6vG3+J2qudGOC9ss/9Cy7scoQ3H4+f4rBg+l0q/pMpl5yYw+t8emxvbZqg2AgvjMiSm5/9bReqwolXvPZkeo/xO2YV9lC6nxXuraAhEe5XSmzkjw8sHz+28r8eyeSo7VtRO2nRozALvLmkiL83BhiSw3CTFVSRAzQXTIB9XbIdQBGQtRSaPc28f3MliVnMknCTofbzsegeQvRn6MdwX4nu6qJTPAG0/4IDTeC97VEPdeJ1iKWvDOCww2uTRasOMB97yeN9nNXQFMrxmMziecJS4zdZDX7F9bdRt1B+oo21KOtjVW2Op3oill5pnrGqYT7FiBkeeDKEORXJDM3KvnEBehD1BcWhzJRck0n2zuEcwYLoOiC4tGfP3haPaFBlWdtznKklF/S0kACk1zZ4jUfnYfWbZmT0VLnyWnkKXZfLxBghghpjAJYiaAbixFb/lfnCplFqg/ofNWoZZ+dPSmtoNbiJgQa9U7b/5n15PRYQjugOB+cC0Au7orABpoSSgAgbecXUKx7ybq7hMV4yQJR9S1RNTvW53LWfLyrOh5c3BX9IcEd53J2RkmrTV7f7+P+sP13YmygzH7XbP63JY5P5PqPTUj6q2UsyyHhdcv6PeYpTcv4dAzh6nZWwMaYtNimf+e+RGDnrEWtmzi3QPvClMMv0N12IZn3qnktgtm9nOM3Z0s3Zs/NPrJxkKI8SNBzDjTtoXe9iOw/GfdCFRtg6xlkHv+KF2pv2DorPt00GmQaNXiLCMZgAlxtzg7iMIH6H/XUbhrG7SXyLMobvBeBP5nI9yvwLsSAtuiXMMAI8PJ5Ym9HFSvT9vaImqC8KCWr/pXvbt6yAFMYn4iQV+QY388TsvJFmzLJj4r3jnHCOPThtIGwv4wrn5qwbg8Lha9fyEL3jsfO2zjmoDkVa01rx2u5dVDdYMKTkbaMvNYXf8F/jymQUqch8aOvsukRdLZWogpTYonjLfmY5Er11oBdNmm0buOZzURq/Oamc6sxmn+t7vqrZz+A28DIej8vVNfxXM+TqxrEj3mNUA3Q9z7uq55+t3a42zljl0LSf/kNHrspsC9BGI/EHmcKPCsgpR/g7hruyoH936Oi4j8I2yCZ3GE24emYkflkAIYgLaKNrY/uIOaPTX4W/wE24M0HWuipawlcr7KEAQ7gmz67uuUbiyNOrNwmmEaYxrAnGro4IFXj/Aff97Lt549wKuHarvzWrYcb+C1w5EDGAP6bQw5HAP1aFJK8Z6lebhN1V1XxlDgdRlcuTBKfpgQYkqQmZjxpu3oHz31ELft9if2Mgjvh/DpxF63U+yud2JsaCeRZ0GUk+eScAvoDzp1ZPybnP5Ffeq72GBkOTMmrgLwv+Ek+LqXOTMtyg2uPKfRo9XsLGm5MpwlLbuNyInE2tnx1B8zx6nQ63/1rDG5nLo1/dWQGaQhb6MeAWUovIleNJpAS+QkVrSTNFy+rQJvkpeiCyYmz6Wmxc9v3j5BqCso84Us3jhSR0cgzLVL83j1UF3U3Ug2YI9gSa03UymWFCQPeFxxZgIfu3Q2bx6pp649QEFqLBfNziA5Tir5CjGVSRAz3lJnRy4zanpRBReN3nWUBxI/B+FSCJ/syilZ1neLdaQZDgDsM8cqt/MVc7FTH6bHTJLL6Ujtyuv6diYkzIw+LjPF+TotuIvI6ywuZ5mqdxuC3uLeA56lENjhfO9dAa7ReXPPXZZDR13HkGdjhiN/VR4FqwpweVzsfmwPbdVtUYNdO2Rz6s2yCQtiXjtc2x3AnBayNNtPNpKd5MU3xnkmCnB1tV7ITIzhqkWDm03JSorh/SvGrgCiEGL8SRAzzpThgvM+gd7+f3S1HwbTC+nzIW+UtwUrBe65zlc03ksgdJQ+syFGApj5Z74Pn3BmWMw8py6LXYuT77IaYtY7QYQywD2/n8AoAu0ncv6K3XXfILiKRi1wOa21qo3afXXjEsAAlG+uoHxLBXHpcSy5aQmm26C9toM9j+2JePxQumWPturWyP8utoa/7qkas+saQEKMi2uX5NERDJOZGENBaqzUeRHiHHbOBDG6rRIaD4MnEbKWoszR630zVCprCbzrG1D5NjrYjspcDGlzR/ePsVUDnU9D+JiTixJzuRNw9L6Ge6GzbON/FSfvBWcGJvGeM8f6N3VV4z29i8gDZgEkfQ6Cu6Hlv3A+H3c1lPRcAvHXO7M3A3HPB99G+u6EMp37JkBrZSvbH9oRuVP0aDCIvPFLQ2d9J9t/uZ1L/uliMlNiiUuPpbPB1+fQpLzB9KsaG9lJMRGTZGHkSbr9mZkZz62rZ+AyJZVPCOGY9kGM1jZ6z8NQuQVQzmyBYcIF/w+VNHFTyyomGWZdNQYt8XByTlr+BwgAGnSHk6hrN0LcNb0Gopw6LzGXQugYGPHgmnOmU7Xt6wpgzv7kH3SCJN+r4H+ePjk1wU0QPgTJ/xS5QvDZXIXO1ungTs7MBnmcxpSuUa6dM0hHXjw66gGMMhSGy0BrjekxCXVEn0kJ+8Ns/cU2lt6ylMTcxJ5BjHLqvsy5amJaCACsnZvJwepWBsgtHnWzMxIkgBFC9DDtgxgqt0LVVmfZ5jQL9LYfwLu+OT2non0v4AQEZ7/LBJ2WArHvirzcY6Q4+SS9hU84HaB17zfdUFcPpChv9na9M4MTe8XA442/xclrCW51vvescmaIJkhbVaSqw8MXkxrDqrtXEmwPEpsWy74/7KPuYH2/j+mo7eDtH73d5/a04jTmXD2HhMz4UR3jUKTFe8lO9FLdGiUBeQwoYOXMvk1jw5ZNiy9EQowLr2v0OpULIaaGaR/E6FOvgBVh6jvYDm3lMNqVcieD8AkiBxcmWHVDm+FQMUT/yN1fOwG7q+DcIIIYpZzt0p5Fgx/XGPIkeAj7B9nHaBAKVuXjiffg6aoqO+PiGTQcaex/tkcTcUt2qDM4IQFMMGxzsqGDpo4gfztYQ2gcd24BXLskF89ZhfO01rxeWsem0joUCls7vZCuWZyLMcpbuIUQk9e0D2Kwo7wZKRX9vqnOzAC7JsId4Z6VegfDNQOMWLB7f+p2O9ubO/9A1GJ4Q0nwnSAddR00nWjCFesmc14Gptska2EWJ147MWrXCLafCaIDbQGO/u3YsJer2mrascM2hmv8llX2VTSzYVcFCgiOsNbNcFW3+tFad8+c7jrVxKbSuq5dUl29kE414TEV6we5W0kIMfVN/yAmbw20loPdazZGmZA0MVtUx1zMFRA6TM/gwg3uBWAkDu1cyoDET0LrT7p2CykgDLHXQsyFED4FwbciPNADMZcM+ymMNa01B/96iOrd1YCTs3LwaVjx4fNIKUzGcBmjlhfjb3YCwHAozJs/fGtEO54Mw0CN40xDc2eQP+2s6G6aOFH2lDczMyOexfkpAGcFMGeEbM3WE428e0GOzMYIcY6Y9llyqmids2RkdiWYGi4wPajz7kEZ03QN3V0M8R8GlYQTp7qcRNmE24d3PjMHUr4Kse91ZnKUx1kqCh2E+Jsg/jacqrtG139d4L3QKXY3SdUdrKNmTzV22MYO21hBCytgsfuxPSQXJA+iRYDGcA2iUaYBqcUpAOz7w/6hBTC9xqBMRfaS7HEJYura/Dz69gl+9NLhCQ9gwKlD89bRhu7vO6I01LRsTcge36UuIcTEmfYzMcp0w4X3Qu1udN0+iElBFVyMiu2bJDiteJc6pfd1u7Os07vvUG92G/hfg/ARMDIh5rIzBezA2art20D37I51EtoehPjbwbvKaU8QPuqcx1U8ah2kx0rF9kqsCAGFFbLoqO9g4fsWsP9PB/qZjVEYhkYbGm1Ha3wJ3kQvuUud5Y2G0obIx0URlx6Hv9mPMhXa0iQXJjP3mjlDOsdwNHcG+cWmYwTHaov5MHUGzyz/5qXEcqKho88xCV43HtnBJMQ5Y9oHMYAz45KzApUTYffNdKaMrtmYAVhN0Podp7UAYeCEs+U54S7wdHVN7jwrgOkWAt+fnJ1FdgPYrWCkDj3vZgLoaImpymk3kDY7jTlXlXDor4ejnsN0W1jh6L9CRRcWMvOSmZgeE631kHonGW6Dhe9bgCfeQ0ddB7FpscRnjH1Cb8iy+eueSkKTLIAxFMzNPrMUun5RDg+/cazHkpLbVFy9OGd67jgUQkR0TgQxYgC+Z0D7OLOjSQMh6Hgc3F9zkqCtKJVY7WZofwRC++henTRSIOnTYPQTQNkdzhbs8EEn8Im5zEkiHic5y3JpqWiNuLzTfKqFXb/ZjTKVs6QTMfbQKNMitaCOxrJM0D0//WfMy2DO+jOzJkopEnISaK/up+Ny17UMt0HqjBQO/PkgHfUdeOI8zLh0BnHpcWP6Bn2ivp3Ht5wiGLbHtGjdUJmGIsZtcumczO7b8lJiueuSWbxyqJaqFj9p8R7Wzc1i5jgEekKIyUOCmHONVQuBN53gw73AKTQXOkjELdm6A3QLqBQnILEbI5zQgNA79NhqbddB+6+dQCYSux1avgW6E2fm5yQE9zr1Yrznj/AJDk7Okmxq3qmmpbwVK2ihTIUyFDMuLOLEphPOMlK/m9cU/tZ4Qn4Phmk5RRWtM79O867p2+ph/nXz2Pbg9ohnM70mOUty0FoTnxHHkRePds/cBDuCHH3pKFYgTPHa4pE87aiCYZvHNp8iOM5bpwfDbSiuXZJLQkzPCtA5ybHcsnr8Al8hxOQjQcy5JLjXmTU53R4guA98z/fzAH1mm3TMlV3bqXvX3InS9yh8zJltMSJ8Mva/6ARI3Y/tmvnpfMrphK3GPuHaMA2W376chiONNB5twB3nIXdZDgefPjiE5FuFFfTQe6rGcBu017YTk9xzi3lyQTJzr57D4edLezzEcBuUvHs2BaucCtLbH97eZ+nJDtmcfOMUMy6egTEGOR+ltaNb4G80+cM2f9pZTnq8l+zkyb9tXwgxfiQD7lyhLeh4FCev5fSbdMiZXdGR3sBcTtXc00GMdw3EXtP1/WCWNBRR68cE9xMx+NG2085gnCilyJiTztyr51K8diYxyTEE2iP3BBrgTJz9mtghm3ee2Et9ad+qvIVrCln0gYXEJDu75TwJHuZcVUL+yjPNNjvqOiNeRduaYD/tCkYiFLaxxruPwBCELc0bR+omehhCiElGZmKmK7sdAlvBbgF3CahEJ0gYkAJcTlfo+A+ddbNyWhZ410DzvzPw1uJkUMlR7kvo6oLdmwVG3CDGOPoajjRw7LXj/eesDIEdtjnywlEy5mT0uS9ncQ45i3N6FG87W1xGHC2nWvrcrgyFJ35sGpfOzkrAmgRbqU0D0KpPQKWB+vbxa3MgxLmkxazi2dSvT/QwhkWCmOkodAzafooz4xKGwFtgphO1z1EP2pmBSfxoP8cMYiYm9rq+HbNPi7kM2svpuTRlOIHTBOxsOvrSUU6+fQodHt038c7GyDMqp0VL0p39rlnsenR3j+3dhtug6KKiMVlKAkiMcUfPYR4nJVkJnD8jjd9vL+szEENBQerEBLji3BKwwuxrqqPG30GmN45FqVnEuuStcrKS5aSpTttO7om2znzf/jBOgHA6MzXg9EzCy6ACkNDeKNfSTt0ZY6AaO94zXbAj8SyFmMtxYugYwA1mAST0FziNDX9rgJNvjX4AA/TJiRms1JmpLL15CXHpzpu2O87N7HfNonjtzFEcXV9ZSQN0HB9j5Y2dZCR6WTEjFbfZ8+fUbRpcVNJ3VkuI0dQaDPDLw7t4vaaMA831vFlbxi8P76Qx4Bv4wWJCSHg5lfnfBN/ToIOA6cxweJZ1tQfoLeTsMkL32k4diQUt34bEj5+ZGQmXQftDTjG7AT+va2fbdH/iroaYtWBVOK0QzJx+hlMDwXcAw3l+ZvoA1x+85pNNGKaBZUVrZDk8httg1ruKaTzWyJEXj9LZ2El8Zjxz1peQUpQy4OPTS9K58DPpUZecxkJhWhw1ETpTG6orfh3j6/vDNs/uqeT2C2eSFu/hraMN+EMWM9LjWb8wh5S4AQo2CjFCL1edwG+Fu3/Ww1oT1hYvVh7npuKFEzo2EZkEMVNVYCd0/pEzybNh8L8MdidR3250I84/+SCWlawqaHsAkv8f2D5o+3GE4MjsutbZ5zOcIMPspy+V3e6cy0gD9wAVaH3Pg+9FnERgBb5nIe79EHPxwM9hENyx7sHlKQ+BMhXzrp2HMhS7Ht2N7so1aS1vZfvDO1j2oWVklAwuEBvPwm2VzZGCXxjNVJnMRC/NncE+fY9OO97QgVKKNbMyWDNLZl7E+DrR3hzxr2dFRyu21hhSSHHSOSeWk3TIh330Oey3vom942fopqMTPaSR8z1L390/QQi+gRNcRGIBg02OtJ0lqHCV0ycpYlJw1+yPkYYTHJngKoHEv4+cD2O3Q+v/QfPXoOWb0PxVZ5t3NOHKrgDm9I4qy/n/zj86dW5GQWpx6uj2IlKQd14eectzOfj0oe4AppuGfX/o5zlPoLEu128q+OS62Vy3NC/qMS7jnPiTJCYpM8oyuFJqtD/riFEy7WdidKgTvek+CLSAHQIUunYXetFtGIWTt8vygKK+idtA5E/Uw9L2f04eTMTPJyFQsZD8ZacoHp7+dxe1PeAsH53eXq1DTv5O0hd69mk6LbibyHVolFPzZhS6ZHfUdw6pHcBADNMgf2U+tmUT9kWulhf2hbHDNoZrcr1hnzcjNWI/otGilMIwDJYVplLe5GPnqaYeO6JchmJ5YcqYXV+IgSxMyeCdptoeu+MMFPOS0qWdxSQ1/YOYEy9CoBns028oGqwg7PstOm81ypyi6+xmHlgnxvgiISfQiMrtNHtUqivfph/hqq7WBb2DEgv8r0LCrSMb6iAE2gJU7akm2BYgJiWWphNNNJQ29J0tGQblUiilmHfNXBKzE9AD1FyxQtakCmK01oTD1pjuUDr7vOsX5lDfFqCiuROlFFprClLjWL+wn9woIcbYpTlF1Po7qPN17SxUkOqJ4V15Myd0XCK6aR/EUL3rrADmLEpBaxmkzh73IY2KuPdC24+I/JZjMLjt1CPhApUAHb8HI9ZJ0nUvi76t2m7GWebqHRTZTvPISDzLwf83+j4XDZ4lQxpt4/FGdj+2B20PrRHjoGm48DMXdO9IUkrhjnMT6uwbBCpD4YqZHL96ta1+/rqnkrLGTpQa2+Rd29bsKWtmSUEyHpfBHRcXU9Pip749QEail+wkqcYrJpbbMLm5eBHVvg4aAp2keWPJjU2QWZhJbPJ8FBwr3sTIt1sWuKdwszj3bKfvUUQGEAecnmVy42xl9jJg3GoWgmvBIAZggm4Fu8ppMdD+W/D9OfKhViOET9K3ZQHOeFx9+ww5d+VC7Hq6821wOV9xH3CK6Q2StjV7n9qHHbLHJoABtKUpfaG0x22GO/qvlxUc3d1Qw9HmD/HL149xqrHTSc8e4+1HGvjz7nJ+v72M5s4gG/dWsXF/FTWtfuI9Y99qQojBUEqRG5fA4tQs8uISJYCZ5CbHx8GxlLMS6iLUPXF5UAlTfOo67r1ObkiPZF2Xs8STeDf4NoN1FMxciFkHaKf5o++vkc+n0iH5n5z9tM1f7sqFiaZ3gnDQ6Uodc1nPAMP/FnT+vuub3jMqppNT099Oo9grwXOes8VaGc5sjznA9u1e2mraexSOGyu1++p4o/JN5r9nHmnFaeho11TOcpLLO7G/fluPNxAe5yq9lg37Kls5VN2KrZ3A6WRDJ1tPNPCxS2eTnjCxtWqEEFPL9J+JaT4W+XYrgA5M3qZ3g2KkQNJnwZxJd7sAzypI/JhT1yXwNwgdAP9LTgVfHXRmNjzL6Ru/up2AAbpaDFwT4ZiBmF0zLl3s1q4AJkzfltDxTguD+Fuc1gj95ZCYmRB7uRMgDTGAATAMNWCOymjxN/nZ/egetj+0nbTZaRG3b3sTvXjixycXy9aaQ9Wt/HlXBS/sq6Ku7UzSd0WTb8JaDYTtMzM/lq3xh2w27quekLEIIaau6T8T03Ii8u2GGzpqoi83jQLta4S6d5xrZS9HuUexbLpVD4SdWZbkz3dtgVZOABLYDR0P0yPDwSpzcmiSvwTxt4L9MIRLQblAh52ZGu/qM8fHXAzhUxDcPLRxqbNez+BeosfJnRDYDoEdgA1GklNcz8we2vUGIT4rHk+8B3+UOiijTduatqp2EnMTcce5sQKWMxNkOLuXFr5vwbhMUdu25rebT1LW2EHQ0igFW443cs2SXBraAhyrH7udSMNxvH50+lYJIaa2mTNnkpiYiGmauFwutm3bFvXY6R/EJOY7Cby9UxbtEMSNXTEt+8izULqB7sBi769hxadQWUtHdmKrDtofBKuh69xeSPgwuLvySsIVfQMYcL632yF81Ckwl/QJsJqchFsz29kabXd01W2xwb0IvCsguJPIuSy9Kafyrmtmz9uipopqeixJ2fXQ+iNI+Rqo0c2PUEqx9OYl7PjVTrSlxyUfxQ7b1B2s58JPX0D5tgqaTzYTlx5H4ZoC4tLGpwfQ/qpWTjV2dBeW09qpQPqX3ZXjcv2hco9xnRohxNTx8ssvk5Ex8Hv0tP+roWZfA2avzr+GG7JXoGJSxuSauuUklP7ZCZTsIFgBsILoHT9Bh0bQg0Nb0PpDpww/ISAIug3afuEEJOBUuO0vcLDP6o5spoK72AlgAjudInQdT0HHH6D5PgjV0HcZ6DQ3qNPJwm5ny3fvIneeRQxpl5QOQujw4I8fgsScRC75wsXMvGQG7rix6QTdWzgQpnJnFdkLszjv9uXMu2buuAUwAHsrmqNWxp1ICqeVwdlchmLlzIF6cgkhRE/TfiZGJebDqs+j9z4K7ZVOQFO4FjX/xjG7pq54s6uwXm8G1O2BvDXDO3HoEOgAfYMUCwJvQ9w1XcXkorHBNSPCzW3Q8Vv6bH/2b8DZFdQ7EFHOkpRnmVP7RcWAGSFiNpIg5mrwR0kkjkSP3RLHiddPcPKNU2O2Q6k3K2Bx5IUjHP3bUWZeMoNZl80al+ueNtYVeAdLAS5Tdfdfumh2OmVNPiqaOjGUwrI1c7ITWTs3a1Sve6i6lVcP1dLiC5GXEsu7F2STkxw7qtcQQgxNXV0dK1eu7P7+nnvu4Z577ulxjFKKK6+8EqUUn/jEJ/rcf7ZpH8QAqPR5qHX/gbbDoAxUfx2WR4NtEXk2RHfdN0y6Ncp5LbC7ZmKM7H7qrix1kmR7C+4hcgOh06X++wwEOp6A4DaIvTZyAHNa3Hon+LKOMfCsjAWusXmjP77pBCdeOznwgWNAW5oTb5wkc14mibljl4PV24oZqRysbp3Q2RilYGl+MiXZSYQsm1mZ8STHOknNdW1+GjuCZCXGkDrKic47Tzby7N6q7ud+pLadkw0dfPSSWeRKICPEhMnMzOw3xwXg9ddfJz8/n9raWtavX8/8+fNZu3ZtxGPPiSBGaw1NR6DxMHgS0LmrRjfJtheVsxJd/oZTGbjHQGzIXDz8E7uKiRzEeMA9z/lfMy3yCpBKhfjbe95mtzk5MdpP5ACjvzc/P4T2Q6gUkj7dKxeml6R7oONPENwCWGDmO0tHdhNnZn884L3AGf8Iaa059XYZp948RbAziOkyJ7wuiw5ravbVjGsQMzMjgQtnZ/DGkXqnEzUQHueAxlCKi+dkkpnYt5BdZmJMxNtHytaaF/bX9AneQpbmbwdquO2CmaN+TSHE6MnPzwcgKyuLG264gS1btpy7QYy2LfT2H0PDAbBCznLS/idgzRdQqSVjc9H0eZC7Gqq2OvkwzrYUWHATyps0/POa2c4STnAPZ5JtXU7XaM8y59vgrsiP1e10z7booFOcLrTXeTxhhl/hNwSdf3a2ekejPJBwE+gbAe0k7uog+N90EoeV19kN5R5h0nOXIy8eoXxrBXbIeU4THcCcFvaHqdpTjTvGRdrsNIxxWO551/xsVsxI5XhdB163yd7yZvZXtY75dU8zgPT48a390hEIE7Ii/zxXNI8gJ00IMeY6OjqwbZvExEQ6OjrYuHEjX/nKV6IeP+2DGCre7Apgut70u/6rt/8fvPs7Y7K0pJSCpXdC4cXo6h1geFD5F6ASo3fvHbT4D4G7BPxvOH2NPCsgdp2zVRpAd0Z5YAh8z0DcdU6rgNBeItdvGYZw+eCOO/u1Vh6Ivcz5GkXhQJjyLRXjUtxuqCp3VVH9Tg0AhsvgvA8vJzFnDLf4a82+yhbePFJPmz+MZdv4Q+P7uoRszTN7KnnP8vxxu2asO/rutuSY8UnqFkIMT01NDTfccAMA4XCYD33oQ1x99dVRj5/2QYwue73vsg5A2O9svU6OkOg6CpRSkDYXlRalpP6wT2w4yy7eC5zlIN9GZ2uymQ+x13U1howSVPhfBu/FENzOqAQv3SxovNeZEYq9rmtX0sTwN/tRphrdpzdKtKWxLGdWyApa7P7tbi7+wsVjVjPm5YO1vH2sfsJ3KO0qa+LdC3OIHafWAi7TYMWMVHacbOpRkdhtKtbOG93kYSHE6Jo1axa7d+8e9PHTPogZ25Z2EyhcAa3fpXsZyCpzCtPFfgB8FUR+3mFo//UYDMZyvqxKaH/Yyb3xLhuD6wzMm+wdt91Hg2F6TWKSY+io7bvrKhywaK1oJblgcH2gbK0prWmjqtlHcpyHRXlOI8XTGtoD7K1owdKamWlxvHW0ftzbCkRiGoqqFh+zMhPG7ZpXLcoFYMfJJlDOFu4rFuSwIHcEy7lCiEln2gcxquBSdGtZ39kYVwwkFU7MoEZD+4NE7O7s2wBmCVilkR7l9FKKuBNptIScMUxQEOOOcZO7LIeK7WNY0E05nagHCpYMj8GiGxZStrmcDiJsHVeDz9cJhC0eev04TR1BgpaN21S8sK+auy6dRUaCly3HG3hhXzW21tgaNg3neY0RDcR7xvdPjWEorlmSxxULc/AFLRK8LozexWmEEFPe5CgkMYZ07qrIFWCLrxr7rdZjRVtgN0a5MwzWcfoPVMb407nd2NUGYWxZYQsr1DcImHvt3H47SI9E8oxkLvz0BVz2xXUUrins9zoKhSvGTc7i7MjH2ZrkwsHNwrx2qI769gDBroTVkKXxhSz+uKOMNn+IF/ZVE7b1mHeiHo5Er4s4z8T8rrlNg6RYtwQwQkxT034mhvJNYEVIkDj6V3TxFShjfNbpR9dAf5AnOCFEJfRM4h1lgbYA+zccoOm4UxsnqSCJhe9bQGxqLIG2AK4YF5lzM6jZVzvq12451ULIFyIuPY45V5UQkxbDyddPEmzrm3dlekxSCpNJLkiiclcV7dXtTtClnB5K8987H7OfJNSzvVPRHLFZY01LgL3lLdiTMXrp0twZ4vsvlZKXHMONK4tIipXkWiHE6Jj+QUzlZmcXT2+2Ba2nIKUYHeoEXwPEZqDcU6AQljLAyAF7Mnb9NSH2qjE7u23bbHtwO/5Wf/eEUktZC1t+thVlKuyQjdYaV+wY/Whr2PGrnSz/0DKq36mhek81ylAoU6FtjeE2UChMt8Hy25c796FYced51B2op/5wPZ54N3nn5RGfGT8qQ6pp8w17g/x4sAFsTXmzj4ffOM4/vHvOuDTAFEJMf9M/iFFRPvVpC61M9Du/hvI3nDoutoWecRlqwU2Tf6kp8RPQ8l+My6yLSgHdwqCWocxi8F4y8mvqEISPA4ZT5K9rSbChtIGQL9RzKLpvbkmoPVLbh9Fhh2z2/n4fIV+oR16M4TLIXZZL1vxMUmamYBhnfoYMwyB7URbZi4a3O2ZpQQqbjzX0SNRVgGkqdpe1RH/gWceO9VyNy1D9JhJrDR3BMCcbOpiZMX5JvkKI6Wv6BzGJedAUoamgtqBqG5R39Tk63evo1Ktob7LTOHIyM1Mh5b+h9QdgD7JOy3C45kPCR52GkESrQXMW67jTHkENLtcjouA+aP8V3ctmyoCEu8E9G1+jDztKIbPxFGzvu3xkh22ajjUy/9p5o369tXOzOF7fQX1bgJBl4zYNwrZNaJD1cMY6gIl1GyTGuGnsCPa/I0pDq39wgXdNq59XDtVS2dRJWoKXdXMzJfgRQvQw/YOYtihv8MoFJ150ukyfzQrCsedhsgcxAIYHPOeBf4yCGPcKSPyI8/+exRDcysBvhxYED0PMquFd025xtmmf3YxSA20PQOp9JOQkoJRCT9Kt85GCm9HgcRl87NJZHKvroLLFR4zL4Pm9VeO6jJQW56ItYPWpO2MouHZpHvOyk9hxqpEDVa34Qxb17QF6x5u21uSnDLxkW9Xs46E3jnVfq9UfpqKpk/efV8DCvBEEyEKIaWWSr5mMAh3lzU6prpYAEYTGrpPyqHPlAGNU1v3sAC/2Kqdb9WDoEZS1D2wnavPM4G5SZ6aiJvFOk8S8savAq5RidlYCl87JpCg9HtMYv19fj6n49OXzuOuSWcS4TTwuA7epcBmK82eksSgvGbfLYM2sDO68eBZ3XzKbpBg35lm5L25TsSA3ifSEgX9eN+6vjtj76Lm9VU4vNCGE4FyYicm/EFqO993ya7ggJh06ItQTSRqbKr5jwr0AjKSuztUDfS5XOP/kg8wXscrA/yoED4DVAEZuVzfqAZi5gzt/JLqDyHk+NmgfSilse+KXk6IpXlc8LtfJSPAwnrmxMzPiCYRtcpJj+acr51Fa244vGGZGenzEoMTtMvj42hJeL61lf1UrbtNg1cw0zp85uAafVVF6HHUELAJhm5hB7uoSQkxv038mxvRGrlliBVFLbnOWZLq3LCswPahFt47nCEdGGZD0OSeYGfDYJMAzhJO3QOcfIXwQdN3gAhhcTm+n4XLPJ/IYFbicXBNP7FCew/ja9ehuavaP/tbu3kzD4KrFubjN8Ylkjta28+DrR7Fsjcs0WJCbxIoZaf3OqsR6TNYvyuVzV8zj7981h1XF6RiDjLzivZE/X5mGU/tFCCHgXAhijvw58u12CB0OoC76ImQvh7gsyD0fddG/oVJno0Md2KVPY7/9bew9D6NbxzB5dqSMBKfU/0ATa9oHkSrHjho3xN3sNHccLlcJuOfSM5DxgOd8cDkzPIUX9l9kbiLZIZv9f9xPZ8MgkqCHKRi2OVzThgLiveMzI2FpaO0Mcah6fDpgXzons0+A5jKdpStzEi8nCiHG14iWk/7nf/6HX/ziFyilWLJkCQ899BBVVVXccsstNDQ0cP755/PrX/8aj8dDIBDgIx/5CNu3byc9PZ3f/e53zJw5c5SeRj/8zdHvaytDZS9DrfxMj5t1oAW96T+c3Bg7BI2H0ZWb4bxPoLKXj+lwh82IBbMArBP9HDQ2SaegnCAj5t3dgUa3cBmET4GR6syyDLR1XSlIuAuCu7oSiQ3wrgH3ku5Dii4oxN/ip2JbxcT2SYqyb1nbmoodlcxZP4IZqSj2V7bwp53lKFR39d7xErI1m481sCA3qbvOS9BqpzVYTowrjQT36DVXXFaYQrs/xKbSesBJCF5WkMIVC3NG7RpCiKlv2EFMRUUFP/jBD9i/fz+xsbHcdNNNPP744zzzzDN84Qtf4JZbbuGTn/wkDz74IJ/61Kd48MEHSU1N5ciRIzz++OPce++9/O53vxvN5xKZ3U9vGlfPYmPaCqAPPAmnXnO2YHffYYMVRO95GK743uStIaPGe/up29nlFX+bs3vpbHYQ2h48awnKcAKtxM8528P7owzwrnC+It2tFPOunkvT8aaIjRXHS/aSbOoP1PdpfaBtTbBjZAGj1potxxp481gD/qBFQVosF83O4I87yru2ME9M8Fbe1Mnvt5dRkBpHQG2hXT2HqVzYWKR4ZrAq+5O4zbgRX0cpxSVzs7hgdgYtvhAJMS68LsmDmQw6wyG21VdyrK2ZeJeb8zNymZU4wO+0EGNkRO/G4XAYn89HOByms7OT3Nxc/va3v3HjjTcCcMcdd/CnP/0JgA0bNnDHHXcAcOONN/LSSy+N0y6D/mpW9Hrz2fpDKHu9z+3drAB01jnH2ha6Zhf6+EvoxtLJsWNiXGfZDYhZCylf7xnAaAs6N0Dzv4J1CCeJOAQEurZPPzIqV7cte0IDGNNtkJSbiI5QE8V0G2SUpI/o/Bv3VfPSwRpafSGCls2xug4e23wqYuuB8WRr2FfZysZ9Vby2P5f9R64nYIWxdYjmwHF21Y/Ov+9pLtMgPcErAcwk4QuH+PWRPeyor6Yx4KOso5WnT5WytW4MG64K0Y9hBzH5+fn88z//M0VFReTm5pKcnMz5559PSkoKLpczwVNQUEBFRQXgzNwUFjpdo10uF8nJyTQ0NPQ57wMPPMDKlStZuXIldXV1wx3eGUY/fVpiz+yU0K3l0Hz0TNG7SLQNrli0rxH98hfRO3+OPvgkesv/oN/+Frp3p+zx5l0JjFdfGgM8S/o21+x4CvybgEiBoHZ2PNntfe/RekiBoDIUhmviZsS0hvTZ6eSvyu+Rn2O4DeKzEshckDnsc/uCFttONPbZYmzpyVMdR6OwbTc+fwaVNU5NIBuLus4DhKyxywcSE2t7fRV+K4x91k9iWNu8VVtO0BpcR3YhRtOw3wWamprYsGEDx48fp7Kyko6ODp577rkRD+iee+5h27ZtbNu2jczM4b8RdEuJvuVVJRWe+aajin5fDmVCagnKm4Te9QsINIPld4IeKwDNx9FH/jry8Y6Ee2nXLqVxeHM388HstRXd7uzKY+mvIqvi7AAn0B5gzxPv8PLXX+Hlr7/CnifeIdAepX7P2WdRitxlOREblI81w2WQXpJOfGY8c64sYfEHFpE2O43komRK1pew4s7zMEawg6axIzBlkle1dtHYdFaFYqUIa//EDUiMqZMdLVgRPmwYSlHnn0L1tcS0Mey/tC+++CLFxcVkZmbidrv5wAc+wBtvvEFzczPhsPMmVl5eTn5+PuDM3JSVlQHOMlRLSwvp6SObch8MNe8GJ2+jt9Q5qLizgqT43MhbscHJ0UgsQJ33SXTIB01H+h5rh5weTGPJqoHOZ6DzzxA61reQnzLAswrUGK9Pe86DpE/Tp1CJ3QIMEFWoVDCciqu2ZbPtF9upO1SHtjXa1tQdqmPbL7YPqrXAnKvmEJc28vyLobItm/QSZxZPKUXm/EzOu305Kz96PoWrCjBHuPSRHOuZ8GWjodBn/RlxG3HEmCkTNxgxphJckXce2loTF+U+IcbSsIOYoqIi3n77bTo7O9Fa89JLL7Fw4ULe9a538dRTTwHwyCOP8L73vQ+A66+/nkcecdbLn3rqKS6//PJx6WSr0ubA0o+AKxbMGKfIXeocOO+TPY9LKoDUWX2Xn0wPrP5HjEu/gvIm0m9Buf6SiEfK/wa0fAf8L4L/b9D2U+j4Xc9Axv8GdPwKdN9lutFhQup3IOGOyNuozTQGLLgXf2P3/9YdqneaOZ79EBtCvhB1h+oHHo3bJH1exuCGPpo0HH6uFH/L2Mw4JMS4mJeTiGuSzMYYCkxDRRmPRUrSUUBhKg9LMz40eRPfxYidn5GLq9e/rwFkxsSR6h1kRW8hRtGw/9qsWbOGG2+8kRUrVrBkyRJs2+aee+7hm9/8Jt/73vcoKSmhoaGBu+++G4C7776bhoYGSkpK+N73vsc3vvGNUXsSA0qeBQm5Tl8kOwzNx+CVf8U+9Ef0WTMqatVnofASp0AeCtLnoy7+MkbGmUJyyh0Pifl9r6FMyFs5NuO325yic4Q4844fhOAOCB9xvtUWdP6FQVfjHZakyLNapykvxFxG1AxjldJVA8bRWdfRp/s0OB2pO+vOTE0HO4IcevYwb/zvm7z9k81UbK/ozqGp2lk1vKcyQlprasewqN37zytgWWHK+OZq93K6TcC/v2cR//6eRXz00hw8LhvTsLvvj/faLCqqJj9hFRfn/jPZcUsGOKuYygrik7g8byYew8BjGJhKkRuXyPtnjH7TUyEGY0R1Yu677z7uu+++HrfNmjWLLVu29Dk2JiaGJ598ciSXGxa7owZe+zI9dilpy/k6vhFtelAl1wGgTC9q8e2w+PZ+z6mW341+85ugw05gZHohJgU19/1j8yRCB4gcbwadeiruORDYCox1LkIb+DdDzJq+d+kghA6CkQGetRB89aw7u9odxN/cYwkqPjMe02P2CWRMj0l8prP9PRwIs+WBrQTbg907gQ4/X0pbVRvx2QmEOsYuaDPdBgk5ibRUtPSdYNJgj2GNGpdpkBjjwjDo00RxLBkKZqTHYxqK5YWpLMxzasLU+Q6yu+mnLJ7nor6pmEAwjZwkF9fNvwG367zxG6CYcItTs5ifnEFjwEeM6SLJM0a924QYhOnfO2nPQ0TdZm0F4dhz0BXEaDsMNTvR9QecoKTgEtRZO5hOU4kFcPk30eVvQWctKmUW5KxAmWO0M0ifPQPTYySAC2wfdD41NtfuIexcx0ztMaNC6Di0/6xraUs7Y/Ve5vw3fAzMLIi5HFwFPc6WMS8Dd5wbO2x3ByjKULjj3GR0LRNV7qgk1BnqsZXZDtlU7qyKuL151Cg47yPn4YpxseVnW/v0a1KGInP+6C5laa0JhG08poFhKDYfbxzXAAackv5r52YxM+NMDSVbW+yo/QWWDqKMIJnp+wAwlYcaXxEFiRGCWjGtuQyDrNj4gQ8UYoxN/yCmaYB+P6FOZ0nJDqHf/AZ01Di7jQwX+uizsPJTqNRsMFKc5ZIuyh2HKn732I4dwPca+P5M5B0/LmdbdWgfTkJtf7uCejqdSjP0tKQQ+F44E8ToMLQ/AL13pATehMRPQvwHop7JMA1WfWwlh549TP1BJwcmY34G866Z2727p+5QPXa47zv5WAcwK+9eSXJ+EgBFFxVx6q1T3eMwTIOiC4uIzxi9P+J7y5t5fl81ncEwpmGwZlYagdD4b1m1bE1GQs98p+bA8R7Lrt3H6iBl7W9LECOEmDDTP4gZqPZIXBbU7UPvfxw6qs/cbodRMzzAL9EtXhQ2xKyD2GsHLp0/GuwOaH8SwruiH+OeD2YOWBUMJYBx6OEnVttNZ/4/VBplV1cQAm+De1a/p/LEe1hy4+Ko93fU9q0pM5aUqZh79ZzuAAZg9rtmkTU/k5p9NaAhe3E2CTmjVx35SG0bf95d0V0XxrZs3jxSz3hvUDKVYnF+MgkxPWcUFQbRZjPVOdB+TQgxeU3/IEap/iu0J81E7/g/Z2npbPkeyPZ0xStddUv8r4GKh9h3jdFgcbYpW83Q9gjQ2P+xoUPQ8m3QHiIXl4tuRDvDXGcHJv3kpIRKwW4FIyn6Mf3wNfkIR0j8HTMK0mankb+ib+J2Ym4iibmJ+Jp8HPzrIRqPNWKYBtmLs5l79RxcUbouD8YrB2v7FLabiB3WRelxvHdZ3+ee4p2Jodyge9bvMZWHosQLx2t4Qogxkuy3uXb/wPW5RsPXRvl80z+IMWMgHKUIkzKhZnvENgMqz4Mye7/RB53tzWMRxNid0P6rM7uNBjWzEgR76DtktB7BLAwuiL3KGS+W03U62rZq3exsC0/5N1ADb78MtAc4+cYpGo824kn0kL0wC2WocWvymJSfxLJblkZ9bUL+EFt/sc3ZFq7BDttUv1NNe007qz6+ctivaVPnBFd6BjymYu3cTIwI26iVMliZ/Qk2V/8IAFuHMZRJdtwycuMj97cSQojxcA4EMZ7oQYwGDCNyr6Ro9cr0GFWlbP8VhEsZ6ozKcA07kHEvhbYHwK7DaeqY5myr9r9M38BLO7ky/q0Qe2m/pw20B9n80y2EfWG0remo66ClrGVc+0G1VbVx4vWTzLi4CMPou0xStavaafbYY6ObprO+k5byVlIKk4d2PX8If8giOymG4/UTV+3UZSryUuOYkR49xyctZjZXFP031R27CNrtZMTMI9lbNI6jFEKIvqZ/EBPqL6ein0/4Pg1xEd5BzQg1YkbKbumagRmfAEYpNfyGlaEdZ31jOzNB/te6Apm/0XdWJgjWCSByEKNtTUd9B2Wby7oDmO6zh+wRle8fKm1pjr96nI66DhZ/YFGf+9ur27BDkWadNJ11HYMOYtr9IZ7YVkZlsw9DKca7pp0CitLi6AxZKGB5USqri9MGDGrdRiyFsnwkhJhEpn8QY7iiN3VMmQmBFvA10iOgUSbavhDYgeqR8+GGuPeP/hjtdoa6u2ikRrdastX1HNx05w91c4GRFfFR9aUN7P/TfuywHbHoHTCo9gOjSVuauv11+C73EZsS2+O+hNxEjP21fQIZDcRlDm6nktaa37x9ktpWPxqwJqCl4+ridK5anENHIMwL+6t59VAtmw7XsawwhXfNz8YzgY01xdjQWuO3wngMEzPCLKMQU9X0D2JccRD2Rb5v0e0odyx66/+Cv7krCVjD4tsxCi6C8IXgex6sajDzIPZqcBVGPtdArHqw653dREZKz/vMLPrPPh4bp2djRh7QhJ2t1kZq1zLT2QGJCTF9P713NnTyzpPvRJnZmFjKpeio7egTxOQuy+HEayecrdant6ibivjMeJILBpe8XN3qp6E9MGHdqF2G4l0Lsghbmp+/dpT2QLg7iXjriUYqmnx89JLicWkJIsbH4ZYGXq46gd8Ko1AsTs1kXe4MTGkPIaaB6R/E6H5mNyw/KmUGrPsvaCt3gp3kmSizq06GayYkfmKE1w9A20MQPuokEuuw00Ax/ha6WzArN8RdD50bOLPbRzF6gU0c4OtzvtF7o/KApwTc74eO3zq7pgDMbIi/NeLupPJt5eM+yzJY2tbEpPRNRHbHuJ26Ns8cpvFYI8pU5CzJZs6Vcwb9Wrb5wxPW3NFtKlbOTMPrMtlxshFfyOqxC8qyNdWtPsqbfBROQGNNMfpOtbfwXPkRwt3Lx5q9TbVYWrM+v//yB0JMBdM/iOm3KaPzxqOUgqRhzrAMpOMPXfku4a7KuzitAsxMiL3yzHExl4CZAb6XnBwZd0lXK4HRKKvfN4AZVUYCeFY4TSET73FaEOgwGJHfCEP+EI3HmgbsFTkhDGc7dUJW5DowsamxLL9t2ZBPG7ZsWn0hMhM8EzILYyhYOzeLi0u6KiE3+/ps6wZnIrK6RYKY6cBvhflL2eGzAhhHWGv2N9exNqcIrzn93wLE9Db9f4KtfvoJ2RZ2WwU0HYXkmRjJo7zbQlsQ3E7fXJcQ+F/vGcSAU7zOPb/rkMNdQcyoDGSUzhNJHCR+rmdXa+WJ3OUa8Lf42fLAVmeb8iSUmJPIkr+LXnxvqLTWvHmkntcO1wFgDzeheoRchiI93tM9Y5SZ6MVtqj6BjKEU6QnSC2cqawz4qPF1sL2+koAV+UOcoRSd4ZAEMWLKm/4/wXY/y0k7ftwjX8aOTUdd/CWUt+cuE601VLzltCEItkLqHNT8D6IScge4uEX0Gir9BFfaD22/YGw7Ug+W2fVlEznx2OrqjzS4JoClG0u766xMRh11HWx7cDurP74Kd9zIe2HtLmvm1cM9i9mN5kLhYAUtzYmGDhbkOT/bSwtSeeVQHaGz3uQUzpLTC/uqCVk2i/KTuWh2Bl53tHoDYjKxtebpslJOtDUDEI5YSfuMRLcEq2Lqm/6ZXaqfP8C9E359DejN3+tzmD7yNHrvr6G9EoLtULML/frX0R0DFJpTHjQpfc+nQbtmR39ccC/jWiClX7YzY+QqiXJ/4KwCfQNrKG2ctAEMONu6A60Bjr16fMBjO4Nh9pQ1s6e8GV+U3VWbSuv6zHZMxNN3GYqk2DNBWazH5O5LZlGYGodSznJTYoyLoGU7yccdQd44Us8vNh0lPElzl0RP2+urONHWTFjbAwYwazLzcckuJTENTP+ZmNTZ0Hh48Me3VaB9DajYdAB0OABHn+nVlkCDFUQfeRq17K6op9LaRh9qhhINyul8rG0NNuiOXFS0DS06yORJGNHg20j0GjYuZ1fSICmXmhwTTP3QtqZ2fy3zrpkb9ZjdZU08vbuyq8aLwtaa952Xz+L8lB7HdQTGb9t8/yzm5/b8dc9I9HLXpbMIWzZNHQEeeO0Y4bMyfS1b0+ILsbeiheVFg/83FhNjd2PNgMELwIz4JFZn9qx3FbZtfFaIOJdbdi2JKWX6BzG+AfoPRRLsgK4gho6aKA0fbWgaYAairRJa/Og9QVSuBx1rQLuFrg5B7G4ouj7y49zzmFzTFf2VxTfAu3rQZ8o7L4/yLeURO1NPJkY/tVJaOoM8vbvyrDd8578bdlYwMz0e0zA41diB12WQkxzDyYbOcRhxX0pZKGVjGgFmF/2NrXX1XOz+ZxI9eT2Oc5kGlS3+rnyZnj93IUtzrK5dgphe/CGLQ9WthCxNSVYCKXGRc8DGU8DqP2A2AK/p4qqCM7OqttZsqj7F7sYa5xiluCirgBUZAy2VCzE5nANBTP0QH6Ag8aw/8jGp0fNq4rMHPBUAfo0+3qsIXGw/y0VmOsS8u6sC7iSftkj4xJAaPM56VzFtVW20lLc4W6wnYSxjuAzyz8+Lev++ytaoIeaz71RxuKYN01BonM7QLgMmImaL9dZTmLeJYCiRYNhLIGyxp/4xLs77pz7HJsa4ibRL3FSQPAq5QdPJkdo2nth6CoVT+fp54NI5maydF7mo43gx+tnmn+T2UJyYyprMfBLcZwKuN2vKes7gaHi9poxY08WC1MyxHrIQIzb9g5ghU9B8AtKcTyvKm4jOXg41u3tW/jU9qNnX9n+qhHxwx4PVK4AxPVB4Sf+PjbsGVAL4/sR4tSMYMiMdPP3k9kRgukxWfOQ82qrbaDzWyLGXj0/MrIwCl9dF8bqZlG+pINgR7C7+l1acStGF0XeqhW0bO0Ktl7Ct2V/V2v3/p8W6TWZmxFDd4icl1k1Fs39c5tl8/jQOH38fSjlX01oxs+BvXJRro3rNLs7MiCfWbRIK2z3GZhiK82ekjcNop4Zg2OaJrWVdeU5nXqnXj9QxOyuB/NTx3Zpe2dnGnsYa/OEwITvy75GpFLfMWtwjeAFnFmZnY3WfJaiwtnm7rkKCGDElTP8gxvCAHW05JNI+EQt99K+otM+dOWrZ3ei9v4HKzc4N7nhY9CFU2px+L62UgpWfRr/9HdC2EwQZbkifhyrsvyEiAN4l4Nsw8HETwgWxUZbDBiExJ5HEnETiM+LZ98f9aFv3aa44pjSE/WGO/u0Y592+HDts42/2k5ifRGJ25Boxp83NTvz/7P13lGTXfd+LfvYJlXPnHCYnzAAzgwwSJJglkqJEUZRlkUqGLfvZlL18bb/17rr3+equJzldWaZkW7RJKssSKQaJSQwgCBAZk/NM55y7cjphvz9Op+qu6jTdPUOwvmthYbrqhH1OVe39Pb/w/fLi7ekSorIeCqZF/0wWAczaRby6StbYfWIq0UAKVnZ1D4w8Q/qwSdBbuqApQvBLT3TxV28MMZ0qIAS4NZWPPNS6Y6mSZM7gjYFZJhJ5miNeznbGCHh+tKI8PVOpshEr05JcHI7vKYm5MDvOixPDG9bBCAQedW2Dg2FbFYUX0+a9d1avoorN4K1PYry1kBkr/57qWhslAUhPII0sQncmJKG6ECd/BXn8F8DIgzu45km2EkS4E575DzBx3vFpih2EyL7NKbwqEXCdgOIl7ru8i//nwb010TczbzL4yhBT16fQ3CqtD7fReKKBp/7lk6Qm0iDg2l9fIzdXwSZiF2AbNoMvDXLy5zd/LY1hL2c6Y5wbnCsrGLfmHBIWmcRmic9uQaBwYyLFw101a96L+Fw8+/b9JLJFDEtSE3DtmKrzZCLP517qw7JtLBv6ZzK83jfLr75tH7U/Qro0lRZ9CVgVIiG7gbxl8sLEENYmdIcsafPq1ChPNpZGF12KilfTyJhrU9b1ns15gVVRxb3GW78MvZJvEjhidOWQnUZ+559jv/lppLFclClUN8IT3jSBWdpP8yJan0Ds+wAiun9rC4P/F0DfOfG1u4cO7ifAfXpLe1mGxev/4w0GXxokO5MlOZri5tducusbt1FUhXBLiHBzaN2C2t1CdnbrpOm9x5v4iQea99yBemsoNziF4gbpu7DPRW3QvaP+SV+7PErRdAgMOGQgb9p860qFB4z7FPvqA2VTiboqONYc2bNxjKSTmyIw4BCsc7Pj5FaRFSEEb2/sQFs1n2lC4anGyunUKqq4n/DWJzGFROX3Gs860Zg1kI5s/vRV5Juf3vapZX4e2fdt5J2/RcYHtncQoUHgk6DUbnscdw8BKE6Njvc94PuZLR9h/NIEhVQBuSJyYRs24xfHyScc4T/bssnudSePoKx5Y7ZoMhbPkStW7vg42BBct5jyfoQiBAcbgnt6Tiklo/PlieLAPerc2i58Lo33H29CU8QSgdVVwZGmEN2bdDLfDCZzab46eIvP3rrAVwdvMZnLlLw/XchU2LM8VKGsOQbA4UgtH2w/SKM3gFfVaPeH+VjXUZp9e/sdqaKK7eKtn05aNw0jEcc/gez7ptNKvboLyTYh3o/MTCI26kRafdbx83DxM4B0/Jt6v4FsehjxwC+Vf8I1RiD7v8CaBOEFz3vAu1j8a4Hn7ZD9EvdIKg2i/zeg4wjebH3hnuubW+FYLYk0z9JwcBShCjLjflITHUzdnEHu8fWpukrn2zqX/rZtydcvj3FpJI6mCExb8mBbhPc/0LyGsHhdGsdawlwbTaxJEwXdGumCeV81yuuqU6RbH1prbrnbUBfu5Wpo93coqywe6ozRXuvn8nAcw7I53BSiPebbscjVcCbJlwduLtW6JIwCg+kEP915mFa/Q7i3KkBoS4lfL19/1BWM0BWM3NWYq6jiXuHHgMSsA9tCtD4GtUeciEtiYO02iuZozWyBxEizAJf+R2k3k1WE8Teg6QzUnyjdwRiE1O+sOIABuS+CNQ6+90Ly/wE7zb0hMC7QT0Lid8CeANzgeQq8719fDXkVPGGPE/ez4cBTV2k5NoSiWmTjfi58dYpiLo609lbeXnWrnPnVM/hWmB0+f2uKy6NxLFsu1T9cGokT9OhlW2g/eLIZt6bwRv9cyaeTKd5fBOZ0R5QTrRHa74GxoxCCk20RLg7HS2pKNEVwuuNHU3+mNuDmnUe29mCzWXx/fKBsx9D3xwf4xf0PABDzeNGEWGPuKHCibStTTQKIuj3UVetcdgSGYSGEQLsHqe8q1uLHm8TkZrB/8L9Ddrqy27VtQrB1a8edvVFeIM8qIEdfQawmMZk/K3+c4ksg02AnuTeFvTq4n4TCiyzr1RQg/wOwsxD42KaP1HqmhdFzo/hi87QcG0LVLaSEy19/hELazb3IbKqaQmAhBZArmiTzJq/1zWCuKtY1LMmrfbNlSYyqKDzYHuX80HzJfjtfvytZrnFZ+W8Hiqh8TgH85MmW8m8CWLNQeBXseUdo0fWgk8bcQbznWBNzmSIj81lnkbUlXbV+3nF4d4jAjyqklMzky6fYVr5+MFzDi5PDmKvqXAK6iycb2nhufABbSmwpafD6+WB7ZfXpzSCRyNPfM0M6VcTr0+naFyNW8+NFilLJPDeuTpLNOp1b0ZiPI8cacLl/vJfRe423/t3X/GBWyB8nNvLHEdDxNMK9g/nhciFnex1BPuMa964zyYbC66wV3DOg+DrYPwlK6ZO9WTAZemWIyWtTqLpK69kWmk414avxceJnj5Pq/zJCca4nMxckn/awPoGRRJrn8EXSTPc1YuRd7JSvVKgtjGnZ/O2lMa6NJVAVQbFCt1HerNwSPTCTYS/MqQU2Qkii7iSJYhDTVli8d+uRJq9rnQiXcQtSn8XRIrKgeBly34PwPwexc11DLk3hE493MZ3KM5MuUhd0/0h1Je0VhBB4VI18GfXdlY7TuqLy893H+M5oH8OZJAJBRyDMu1u6CeguDoZrmC/kcavqXRs9xuM5Lp0bXSpoNhIWVy6Oc+RYA/WNPx61M4WCyYU3R7BWzA/zc1kunBvh4cc6drQIvoqt4a1PYtzhyiRmQwhof3rru9UeoeyqproQLY+X2UGjsrT/zkYopHR41OL/14cFpCu8Z4M1V0JiZu7McOULV7ENG6HYtD/YQzT6JYojkmxqP8HWn6am5QDkbgEmVlFDKOut/hJ3IEvryV50b5GJ2y1sh8AIVZQUFCOcWph979zHt66Oc30sgWXbK6ji2nM0hb0Vj+9za6hCYO1aAkny4f0/oDU0hVstEnTnGUo08Pr4UYaTDSSL6+vaPL5vbTu1c1gb0n9C6XevCPYs5H4Avvfs2BUsoi7ooS649zU59wvmCjmeGxtgJJtEEwrHo3U82dBeYsb4UE0jr0+PlaSUNKFwuqbUCiDs8vDRrqNYC9ut9DxShUKtZ2dSh723Z9Z0ZNm2pOf2DHUNgR+LBXx8NLFmSpcS8jmDudkssZqdq4mqYmt465OY7OT299VcCGPrBEiobnjoHyHP/zcn8i8tJ73U+gTUHl27g/sJKHy/zJF84DoJxVfZiXqYlcRl80SmEmywJwEn1Tbyxgi3/+7OElk48YHXibXOoOrOBKt5rlGc66MY+ye4F04arE+wvk6XoJD2c+3bD9FwYAzb3B6hU3WVIz97hOFXhsjF84Rbw3Q/3YU76uXSa/0okQKBkBNtkjYUZt2YGX1hBKCpgvcfr+wlc7gxxDcu72arsOCbfY9jSQUQdIbH+Mn9P+TR5qvcnO1c2koRznht6XxbNEVQF3TzSHeFzjZrcsFs1IFhqVyd3kd/oomIN83pQ0XC3nvvCfRWQcYs8he9VykspK6L0uLS3CTzhTwf6Ty8tN0jdS3kLZNLc5MOOZaSB2L1PFxX3gpjtw0b06kyWlo40QnblqjqW3/xzmaMsq31tg2XL4yhaQrtnVHaO6NVMrPHeOuTmEpaMJvaV0KobVu7ivoH4B3/HibeBLMAdccRoQq1Nb4PgTUK5kq3bQ+E/4VTm1B8ZVtjWDMmsfbvuyIyxQvgPo1lWPR8t3eJwARqE8RaZ5cIDICqSnRPgdmeNwk0fIx438u4fAax9mlm+ppYL8IiLY3ZwYZ1t1kP0pbYRYvTv1SqbZMtmGixAlrAWCphEgp46goUpCCi+GkIe3jqQN26HT2LqZI/f3WATHF3lHiL9jKZ6Iu38AcXfpqCpSFZThVpisKvPdXFxZEEmbzJ/oYgR5pCjo+TtJnN95C35om4Ogm4GkDoLJLjvOnif1z8MKmiD8PWUYXFq6N3+IVHO+n4Mat92AkUTZsb4wlmc3nqg26ONkS4NLvWZdqSkv50nM/dvsA7mjrpCjqL4NNNnTxe30bSKBDS3bjKKO7uFVxulXxubXpLVQXKj2B32XYQjniYnkqXJTIApmkz0DfnVCB0Vm069hJvfRIj1G0SGQFHPoYoqyOzySO4g9DxjvJvGrch9xzYcdAPQuDvOYzCuAFKPbgWnGZTf7Tt829qjHczB8ksGHfITNWU8ItgfbxsNk1zWchCL6//YRjESQQ2lrG5CJOR234tjFW0yM3n145HAz1orDmsUCBYZ/GPT65vK7ESzREvHz3dxh+/MrDrXUkShbxVWuegq4InD9RSF/Ly7qOlqa+cOc8r479DwUot7G/T6DvJg3W/hFBrwZrgxeGTJAoBLOkslpZUsSzJl86N8BvvPlh9utwCppJ5/vCVXkRNFsVtQR6+M61Q5/VWFKiLFwv87dAdPtxxkFZfiIlcBk0R1Hv89/zed3bFuH1zumQBVxRBW8fGUYd83mBmKoOUktr6AF7vj5bNxCIamkIM9s9TLJoV699sWzLUP0/7Ju5LFTuHKomphFArSsfTOz4cAPKvQPbLLNUiFKah+CaE/xV4VtTMmGNgXNqdMewEzCFIfRaX6UJaTy+9nE+Vz8VbpkJ61rfK8HH3f+yqSyVQxg8pb1kVa1k0fetU5OXemT1rq1YVQdCjkcqb+F0qj3TXkM6b/Jfv3salCc521vDQwmR6fuqz5Mw55Iqqn8nsZQaSL9AV+FVIfprrs91LBGYlcoZJPGsQ9VfTSpvFF84NIerSKLp0HhKEQxyn8hkn+llhP1PafG+0n6xlwoJikkfR+KmOQ9R5nWiYlJKpfJaUUaDB4yfo2v3i6MbmEIZpMdA7v2SS2tIWobN7/YjD6EiCnlvTS710fT2zdO2P0f4jZigqpURRBGceaaOvd5aZqTSGUT4Pblk2liXRtCqJuRtYlsWZM2doaWnha1/72rrbvvVJzHaguhBdO1/UCDhKwLmvUlpMaYHMQ/bbpW3L+R+yG51JG6eQdNZ2JJWDCZh4gnlCDbMkJmqQFswP11LMulG0LCvqFZG2wvj1rcuZC8Va2H97IXVP2EPtgeXiVtOyuT6WpHcmBRXISpN//WLZ1ZhJ5emdrlQEvQuQkk881kXU76JoWvy353tI5cylJ/2/uzbO6HyW9z4QIlEYKiEwAJYsMph6ga7w0xD5P9G162VPY0unJqiKUmSMIlnLJOrylBTlJnJFkmYelybL/sY2Irlxo7T+xLCLfGHgBs8eeoiibfHXAzeIF/II4aSijoRreXdL964++QshaO+I0doWpVg00XUVVV2/DiefN+i5tRy9Wbzu/p45amsD+H4ESLGUkoG+OUaG4pimjdenc+BQHYePNvDGq0Nla4U0Xf2xqBHabfzu7/4uR44cIZlMbrjtW1+tJ7p/c9spC2q0qgvqjkPLo3d9ajnXg33lj7EvfRY5dcV5irGmy3cuYZfWxEjLic7sAjYmMFv/ET7wgTcJN5somoLiUjn/1SdJTtVhWwq2pZCZD3D122+nmNtqZ4ok0jKD7jGouAQooHnL83GhCA6+9wBiIXdfNC3+xwu9fO3yGGMzAzweuIAmVub7JZqweby+fC1UMmfwwztTfOfaOP0z6aUn0+/emNwFbZjKUBRBxOeE5i8Nx8kUzJJUhWFJrowmmE3nKn7g1qK7u1A429WEvmryFQIaQx6CP2JO07uJgmXypYEb/M/bF/hffVf5bzff5NLsquYBtcKDh9he3NGWNgPpON8c7mE2n8WQNkXbxpKSm4lZLs/dRfPCFqAoAo9H35DAAMxMlW+IsKVkajK100PbFfTemWFoYB5zIXKcyxpcvTROfD7HvgO1a+qBFEWwb39NNZV0lxgZGeHrX/86v/Zrv7ap7d/6kZjOd8Bs+adMBwoc/hmE7oViGmoOQ8R5spGTl5C3vgTZGQg0Ig7/DKJcd1EZ2Le/An1/B5az+Mrxc9DwIDzwcwgqpLeUFR4+xm3ujULvZiIwa6F7Cpz+2Aw58yMYGQN/vR9V/wCFZBwjm8PX0UBkYoj54YFV6aSNIJgfXl8QTffqdDzWTu9zfchVTELaktm+OWL7nBD2K70zzGWKmLbksZYrnAzcIaRneTV1jLTlpcE1x1PBa9S7jgClBa23JpJ88dwwUjoGhm8MzNNd5+cnHmjm1sTeTsymJSmYNh5dpX8mU9ZNW1EEcyk3biVIzporeU+g0eR/aOnv050xhuay3BxPOp5AQuBzqfzs2e0Vtr9V8fXhOwxlkthSLpHGH0wMEnG76QhECHtdBBU3BdbWYMH6v2hR4X1bQqJYYCSbXBOXNaXNhdkJTtY0budydg0SWf5ZTcLEWIq2juimyNC9gmXZjA4nyraW9/fN8uDpVk6caqb3zgzZTBGPR6Nrfw31e+xL9qOI6elpzpw5s/T3s88+y7PPPrv092/8xm/w7//9vyeV2tyc+tYnMQPlWpcXcOCDiI53INzhNW/ZY2/Cpc/C4tNqYgD5xqfh9D9eq7i7CjI7Db3fWmU7UIDJ8xB/G+gHwLyDk45ZhAs8zzhRmuJ5yH6N7RKKewMXuI7iDXjxRpYLS92hCO5QBIC2h9uYvDpFZjpTqttyF4h0RDj6U0eY759HUUWJrD0ACiXO2NdGk0sePvW+eVRFcsg7zCHv8IqdPI4AobosiW9aNl86P1KiymtYNj2Taf763Mp9twuJqmSxbC+bCZCqikBfWAQiPr28Yq+EkFenyfdJXp/8faS0sLFQhQu3GmJ/5H1LmypC8DOn25hJFxidzxLy6HTW3vui0vsJaaPI8AKBWQlT2rwxPUZHIALAzz7UxR9fv4biW+56Y1GfqcKxfarGkUgdl+cnMexSqiKRNHj8iAo0p1hJbfweorYuQN+d2bJEJpc36Lk9zaFdsm3YCRQLVsWwWTbjrAmxGh+xmqrb91ZRV1fHm2+WzzJ87Wtfo76+ntOnT/P8889v6nj3LxXeKcz3VH4v0rWGwEiriBx6ES5/bpnALMIuIm98YeNzTl8r/7pVRE5ehMAnQNuPwyE9gAu8HwDXMch9HTJ/CXJ+4/PsGQSwXj2KC7QW0Ncnd6qucvbXzuDfQbdfX41DmuoO1ZWdMBVFofHE8mS5sr5jJF23oHq7GiaopRPs0Fy27JxmScngjjgxi4XlaWPSIICTbRHUhXD2mc7Y0r+XthEQ9Gi0xXzUeA/wdOv/QXf43TT7z3A09lHe3vK/41LXFmDXBtycbIvSVffjIWK2FWRNA7XCPRnOJPn87Yv8wY1zPDd9h9qYiltVUVFwLfx2KhEYXVH41UMP8VRjO3UeH9oK3RddKByP1tPiD+LV1j5zKsC+0P3nP+X16nTvrySy6ERjZKU2n/sALnfl+S5QVZreNbz00kv8zd/8DZ2dnXz84x/nueee4+///b+/7j5v/UjMamfqlZjvh/qTS39KI4P84W9CPrGWwCwiM77xOVVXee8koYLqAcULoX/ktFfbKWfBFC6wM5B/ntIIzf0AHdR6sKZYLkjWQHhAaQTPGXCd2dAQUkrJzO0ZMtPbVVBei8yMQyB0n86xnz7KtS9dd+pfpBPO3v/u/fhrl0nT2a4avnllzPFDGj3Bg/V3UITNMgfQwXW6NLWHIyS3u3OuxLZ9bIbEHGoK8b4V4nsxv5uPP9zBV86PkDctbAnNYQ8fPdO+RES8WozDsQ/t1uB/LBBzeysSEQnMF50UUiZXGkG1YN2P9VikDn2hOPijnUe5Nj/FjcQsulA4EavnQCiGEIL3tuzjK4O3sKTtiBkKBbeq8mj9Fr3d9ghtHVF678yU/d3YtrxLsc2tIZ0qMD+XRddVausDG5o3qqpCe0eUoYH5Na3lXZUUsKu4a/zWb/0Wv/VbvwXA888/z3/8j/+RP/3TP113n7c+iVG0yoSk5+vYriBK1zMAyJ6vQ27e6SCqBGkj4wOISGflbRpOwdUyN14oiNYVBcNKxPlvEfkXoVK9zD2D5hCY4Keg+BoUXnNedj/q/LdJJ2spJVf+6iozt2fW1K3cDQINAcyCiebWqD9ST/RfRJfOUXOgBveqp6ZTbREGZjJcH0uQNUP80bWf4t2dr9IZnkAIr+PQ7XlmzXnaYv41aYSdxfqzuRBwuDHI04caygrvddcF+OfvOcR8toiuKrtWjCulZCyeYyZdoD7ooSlS2Y7hrYa0WaTO42Msu7NdaLcTc7QHwsTcXmJuLydrGsvWuLQHwvzi/hNcmJ0gXszT5g9zIlaPR71/p/FI1Mv8XG7N64Gge0+E8qSU3Lw+xdREaqlV+vbNKU4+1EJ4g+9uZ3cMXVcZHJjDKFoEgm72H6wjFP7xtc24HyHkfRzTO3PmTMXc2WZhf+efQ3GDNq1AE+LIx5BX/xRysxsftOkRlIeeXXcTOX0Vee6/LifCpQXH/z5K25PldyhcgMyfcu9JjA5qF9gjgHAiLL73O1GXu8BszyxX/uoqlrGz1ycW0kM1+2s4+uEj6JsU05pJFRiezxL0aHTXBjY1of735+8wmSwvwb7b+LWnummJ7owXznZRMCz+5JUBplJ5BAKJpDni5e890olrgyfbH3VM57P8r76rmLa9K+X2LkXFlpIWf5APtR9aisz8qCOTLnDu9RFs216KyCiq4NQmSMROYGoyxY2rk2sKdHVd5Ym3dy1FKjPpAnduz5CYz6FpCi3tETruoYXATqx9WzrfgVpe/88f3pNzPfx/XtrRa7t/KfxOwaxkrLgC6XHkm78H+ia1QaYuYcf7ne6j7DTEDiK634PwLOemRd1xeNfvwMw1sC2oO4rQ16kFyXyBe0tgNCet5f0JcG2uA2srmL41s+MEBlgqEJ69M8vFP7/E2V89s8EeDmqDbmqDm8ttZ4smX3xz+J4RGIA3++fuOYn51tVxJpL5heJp576PzOf47o0JPnCivK/PWwEjmSRfHriJsb7R111hsTh3JJPkhYlBnmnu2rVz7RTSqQK5rIE/4Kqo++IPuHn48XaGB+Okknn8fhdtHdE904kZH01W8DyySSTyRCJe8jmDc68PLzlUF4sWg31z5LIGR47dv8XHVTh465MYu3yr4xpIC4oJRy/G3qAryC7Cy7+9oAQsITmMHHkJnvw/EL5lsz2huaHxocrHWYQVB3aiOHS7cEPkN0HZvYlFde2u94u0JcmRJL3P9bLvnfu2vr+UpPImmiLwuUt/Fn/x2iBj8bUh8b3EpdE47zrWiN99b36yUjq6M6u7vyxbcmk4/pYlMePZNF8auLnG82i3YEnJtfnp+5rEmKbNlQtjJJP5Jf+1aMzL8ZNNKGUiSB6PIxJ3L1A5z7DcKjY0OF+2lXpyIkX3/hrc9+g3V8XmUP10VmNxIRfKgsZLmclL2qWvSwuMLPL2VxCnNifQU4L889sY6E7CBOOqc135b4OdBK3NMabUtq4TIqUkMZIkOZrEG/FQc6CGplNNDL08tAtjL8Xgy0OE28LUHqjg3Lw4vuIgpl0g6u5iPG7ypQsjpHIGEmgOe/mZM62EvS5m0gXG47k9FbJb7mNZ/I6pCGA2XbhnJAaoaH63pq39LYRXpob3jMAswpQ2Usr7tjvszq1pEol8SXfR/FyO/t459q3zu9tJ2LbEsmw0TVn3PjU2B0nEc2u/u4Kl2pZkolChs1GQzRSrJOY+R/XTWQ0zC2/7t4jEIDI9Af3f3jgyA4CEmfVE9SrtJndNmXfzsCDzxzgNmwsTtnkHkp+G0D8DbVX3gzkGhVdApp22atfJpQJfy7S4+GeXSI4mkbZEURU0t8rpXyl1kN4tSEsy8OIgsf01TCcL6KogtqK4N1Uc5/XJ36doZRAICoabq7f/Hqa1ovU6nuUPX+rnnz5zkPlMYY8JDCwW+QohObr/LxkaezvpTOs99S8SQtBR42dgtrSzTADdtW9dl+vp/N5HSFt8wR0hMLtBhKSUTI6vbY+2bcnYaGLXSYxtS3pvTzM2mkRKia6r7D9US0NjqOz2DY1BpiZSzM/nsC3nfggBx040LtXBBQIu0qn8GiIjbfkja1j544QqiVkNRUUEmhHBFrAM5PALUHQM2TaEy6mpkZYB2SlwhxCudRQcpQWpzzhk4L7A6ifOIuS+CcF/sPxS/nXIfgGnDVxC8RoUXoDg/wuExuBLQyRHkkuqvJZlYRUt3vzcubsa2aKJ3GaQGEvy5f/yMlO1XubDLiJ+Nx8/207Ur/PqxH+hYCWWtp2aO4ZlW6z8KUgJ2aJF/3SGyyPxe6KbDCCETSbXwL6Ob5Cf/0cE3BoDMxmujSVQFcHJ1siedgf9xAPNfPaHvZiWxLQl2oLg3vvfoqkkgKjLQ8bcXdHJRQk7RQg0odx1KmlqIkXvnRnyeRNdV+joitHaHtkhYkRFfRfTsOm9M0Nnd2zX1Hjv3JpmYmy5zqVYtLhxdRLLlDS1hNZcoxCCE6eaic/nmJvNousKDU2hkuhKW2eUyYlSYqYogliND0+VxNz3qJKY1Qh1LP0QhKrDY/8GeeEPID3mvO+rB3cY5u6UtmKrLkT3+7D7vwu3voSTczWRdQ8gTv0qQivT3VN4Fcz+3b+mu4FxG+K/5YjZed6+QGBWTupFJzJTPAfuRxi/MF7WVqCY2kSBdQVsmURYkki8QDBZYLzWy2AL/OHL/fzikwJzVY1UoRBCyrU/AykliVyR62MbG5DtJoSwURQ4vX+cr10KcWU0sWQxcH5wjqcO1PHUwfo9GUtt0M0/fedBzg/NM5HI0xzx8GB7FK/rrTuNRFweRrI7ZymhCUGHP8JsMYdX1TgWrSNlFJnMpanz+HmwppGAvv2I28x0hhvXlrtxDMOmr2cWKSXtnXfnHm1ZNlcurq+TNTIUZ342y+lH2nY8CmSadgmBWYSUcOvGFAP9c5w42URwlQSBEIJozEc0Vr4w3u93cfKhFm7fmCKTKaIogsbmIPsP3ps6niq2hrfu7LNdJIeR09cQdcecv311iDP/FCkthKIhPFGkkUOe+31HDVjRHEG9zmeQmheu/glYKxbs6SvIS59DnP7Ha89VeJ1SN+v7EQbYk1CcguJlyos8F6FwHtyP7KgGzCLKTYWLNGm95z3VhubpHON1Pgqawkg8viDdvoyAf5z5xD5sWbpwWLbk9kRyR1NJDUE3k2WcbytCCsLBQYQwGU+YJQQGHJPHF25Pc6I1QsS3N6kmn1vjyQNv/ck9WSwwnc/Sl47vyPE0IXikroWH61p2tdalv2embJHqYP88bR131zJ859Y0iQ0K3G1bks0WmZvNUrPDaUajaK4bji3kTS6cG+Xxp7qQ0qmZcbu1TV1zJOrl4cc7sCwbRRH3bT1SFWtRJTGrYReRvd9E1B3DHvw+3PzSguqvRLa9DY7+HEL3Ih79l8jsDOTnINiC0P3YL/3/SgkMOPU0U5eQxTTCtckW7vsSEieFVOHHvaAj03C8nuHXR3bMG6niSBaanVwbdG3bQhBOFZn36kizDXuVGnIscoeJ6dMYho4tl6/NlnBrcufSfArwK09287mX+plMVuqYkwhhLiiwCLrav42qGhSKIfrmGjEsCyEkUpZ2evVMpTlzl0/ZVTiwpeTbI73cSs6iCmVHfIl0ReHJhnZOxRp2fXHM5cqnvizLxjRtdH17XYKLtTCVirtLzyXp65nBH3Dh2UHRxWzWwN5gXpG25Pwbw2QzRYQQ6LrKoaN1xGo25wN2P5tSVlEeVRJTDvl5x3X6+l+WFvUOPudEZE78ovO3t8Z5Px9Haj4oxMsfT6iOQ/ZqEuN+FLLj3P/RmJUoN4nooDZC8QpdT+1j5s4s2ZndK4gUgL7ptUViaApSSjpj9QSM99Gb+DaWdO65ZUYIejPETcdDa7eKeKWAv7k0ykx6vUiMIBa5g983QTTcjyJMegbeTzLdhhA2QghCgSEa6i4wPPY2cvk6hABtD5RP3+oYTie4NDfJcCZJznKIriXvnsA0ev18vOtY2dbj3YDP7yJVRs9IVZUNpfbXg5SVu9PKIZ0q8sYrQ5x9rH1HiMzEeJJb16c23M62JZm089uWUlIomFy+MI6qKXR2xWjr2JnaoCruH1RJzGoIFeqOIm//TZmuJAlDz2Pv/wBMXYaer4ORAQToXvA3lVf8tYrIi/8Tut8LTWeWf0Tuh53WZuMm916pd7PwOPdoqR6oCFhLbeIacOrjf4+Xf293uzo2Mw1JQApBJuLmRGuYWMBNjA8Q9XQxkPwBqZzk1Z4nMC1l14t3pYRbE6kN25Fn54/g906iqQWGx54gmW5DSm2pcyKZbsPjjnOo+ytcu/3zSBnkUIXOjCo2hmXbvDAxxJX5qV1ppZaSPSMwAN37a7lycays38/dLN6KIggG3aS2kA41TZvB/rm7dquWUnLn1vSWSNRqWKZNf69TG9TRVY1arsaYbOA3rX++R2f7pR09WpXErIRQQfMi9n0AOfxy5e2e+1drX7MKkI9X2EFCoh95+XOQHEYc/mkwRyH3DTAHuOcERjsNxMHs3WBDHel5mqn+U4y92YPmTnDsXd9HUUxgeXJz2X+Cqr8by7iHlf2agqkIJk/V84HjjTzQGll6q857hDrvEb5+eQzTmtvT7qOwVydRIeTvVgscrukn5s9TAGbmjq4pOpZSZ2buGM0Nb9BQe5Wn2j+GdxeEBNPpgtNKa0vqGgJ7IhG/l0gZBb42dIfx3O52Bu52Z9NqxGp8HD/ZRO/tGbLZIi63Rmd3jOaW8F0f++CRei6eGykxb1QUgculksuV95sr55u0VRTy5oZppM3AtiWDA/O030M7gSp2HlUSswhfA9QdQ+z/gFO86w5DdpNqv5uFVYT+v0N2HEfk/if3RxpJQPDjYA5D6r9TfkxuwAL3WW4+187E5ZvYhk3Xw4MLqsWlkJakrnuciVvtuzZqXyRNoC5BPukjORlxrkM4ysD73tlNqCVMqHl9vY1b48k9JTCKEJztjPHinWks22lTXkRnaJSfP/YdJKAKE9B5uUzXFIBl6yiKxbG2AgcXojCWZTM1kSKbNQgE3dTVb84PqhyGBubo751bevIdHUnQ1Bzi4JG96YLaSSSKeQqWRY3Hi7rgLG/ZNn/ee3VPWqfb/HsfJaup9e94US044nBnH+tgZChOOlUgFPbQ2hZBCHj5xf6ygnEbCcXNTKUZGpinUDSJRn10dsfWtDVruropB/lQ2E0qWV64bhH2XdYGVXH/oUpiALrei+h+d4n3EW1Pwq2/3v4xBRBWQROQtKC46H6mQ/arIO4HAgOIIKCB3g2+D0L2bwAVsB2Hbf8nQEhQYmTmYOLSG0st1KpuIkSZGUOAqi9YMmxa3WWTw1Vsjr/3HDUdk8iFQtxs3M/Fv32C+qMdHHzfQZRNFOdZtk2muI5b+S5AUeDh7hpOtkV4uW+I/pk55tM6KjY/f+w7uNSV4xG0BqcYSdVTeg8lQf8YitCJeRw9kVzW8X6xbRvLkqiqoK9nljMPt6FvMUqTzxslBAacJ9jxsSQNzSHC97GDr5SSa/Fpzs2Mky4WsbExpUQVAnVBf+VwpJbe1DzZPSAwuqLyWH3rhtv+KMHrLW8hEK3xMT+bLSEQiiLo6Iqu2XYRw4Pz9PXMLn3XxnNJpqfSa+poNE2hrsHPzFRmTUppMRrk87s4dbqVntvTTIxVLkC+29qgKu4/VEmM0KD/28iB7yKDzYiHfh3hb0B0vRt560tsQ6UEfAriqNf5hQmc/yaKyMGiE7kQMzt8EduFDt6fdMYJ4HkK3GfBHALhA7Vl+T1gfmC0ZO+Z/kZajg+irWoREkIyO7R68V0fhiZI+nU0UxLKGBX3bH+wh5qOSVR9uX4hUJPi1E9dJ7j/PZsOE48n8jtMr9aDRFUUPvl4F7qqMJD8O0zft2hts2hFUC98qMra79lP7HuZz1/5MIYlkSiAiaLYtDW/hCpcdIbeBsCN65MYK8w1LUti5w1678xweIsGdrMVCrJtWzI9mbqvScxLk8Ocn51YU9tiSokpLb492kvE5WG+kNuVCFxAc1Hj9pI0CrT6Qzxc10zYdf/er53EsRONXL8ywdxcjsUA4L4DtcRq1kaEpJQYhkVvzwyry5Asq3wdzeGjDdywJ5mZyaAIgS1t6huCBENuAgE3kagXIQQHD9cTCLgZHJijkC+dl3aiNqiK+w9VErNYoCotRyPm5d+GZ/4DQtWRJ38ZLn2eTREZRXPcqpGIw17QSrUGZIMLkhLsZoSiOdor9xQaBH4BXKdKXxYe0A+W3UP36YgVKYr4WA3zI7XUdk0iBNg2SEtl8EI3+eTmw9lDDT6GG/0oC49xmik53hvHW1ibqmo9MVBCYAAUVRKqGcFJha3vTC2l5NvXJnhjYI5d7AJfgM2+9m+guzKowmQk+VN43G3cSXwLWy5HAnQtg0RlNelrDMzy64/08+rEGfpmRtFc/dTXXqY13M6R2M/gUgNYll1Wu0NKmJ5Kb5nEKKKEt5a+dx93QRUsk3Oz41jr5BJMKfnLvmu41J1PJWhC4bH6Fk7E7q3rsWnajI8lmJvJ4vFotLZH8Ac259Z+N9A0lQcebGFmOk1fzyzZTJGhgXkQ0NwSXpoLR0cS9PfMlpDulZAS4mXqaFRV4fjJJopFk2LBwuvTy7ZDCyFoaYvQ0hZhbjZDz+0ZshkDt1ulsztG0w7UBlVxf6FKYlbDzMPkRWg6g9L6BNLXgLz5RchMQKAJstNOu/RS55IATxS63g3uCAz86RoCAyBUAa1haPgN4DZk/pJ7WxMj1rN4LYvagzX4omnaT10jUJvgzb96G7eeP06sY4pi2sP8aC1j19tJjNds+pjzQZ2RBh9SEVgLi7ilSK51hzl9Y25NtETRKhdBZ+fT5OMZ/PUB3L4cduavEcZ151pdp5HeD/G9WwneGJjF2gNPP0Ux0bQcfq/TsTZW/DOuXz9ILFp6DdOmjaDcwuoiEj7O+2pbgBbgYeDnSrZYDPbtFB+rrQtw++b0mtcVRVT0p7kfMFvIoQplw7ZoC7nUQr2T2B+Kcjx6b2uGDMPizdeGKBaspXTKxHiKoycaqavffY2qVDLPtcsTS+fO5016bs1gFC06u2sYH0vQs4kuI/c6Ldkul4ZrkwrRsRo/Dz/21vX1qsLBXZGYeDzOr/3ar3H16lWEEHzuc5/j0KFD/NzP/RwDAwN0dnbyV3/1V0SjUaSUfOpTn+Ib3/gGPp+PP/zDP+Shhx7aqevYOUizpMtIxPYjHv83K97OI4degKlL4IkgOp9BRLqX36+LQLp80a6ItII7CJwGOwW5b+EIyO1tbYYDAzJ/DmoDaJvzvlGZ4uzP/gApi0z1OPsUMn6y80G84RQjVzpJz2xtoRuv9WKvfqISgqKukPFqBFZ1PcwMNNB4cBRFLZ0Ic6kAr/3pFRTVsXt49Fe+ja4VlqIKA9P9fOHmDbLG7j+VLsK2dW73f5jm+jdpqLuIopj4fMPYtmRl121Bwo285IhHdSIhSEAH14OgdVc8Pjjtu9GYj7nZ0jSQENDQtI5vVwXoLpUjxxu4cXVyiR1JoHt/Df7AvTOg3AhB3YW5A8J020XU7b3naYqhgXkKeavEA8i2JTevTVJT69/1SNrqWqrF8w8OOGrBA2XeX42N6miqqGI17qrC6VOf+hTve9/7uHnzJpcuXeLIkSP89m//Ns888wx37tzhmWee4bd/+7cB+OY3v8mdO3e4c+cOn/nMZ/j1X//1HbmAnYeASDcyM4ns/w5y8HlkYdk/R2gelO73oDz6v6Gc+gclBAZA6F1rpO0d6OBaQdq8T0P0/wbfh3fnMjYFA5K/A3P/GyT/G5jr+6KQ/SJCFFEUKGY92Jbz9bn6zTPYhs6pD75K96M3EIrNZmMDZoUiOwGY6tr72PvKUYy8C9NwIhemrWCYGtf+7gFs08YsWNScuIWq5pdy83nTxV9ce9+eEpgFlRqkdDE+dZbZuJOiU9Vi2WLovrzFH199Hy+NPMDt+IPYgX8I/o9Xzu2swOGjDbg9GqrqOPSqqsAfcNO9f/MRsZWobwjy+Nu6OHi4ngOH6njsiU7aOkoXlqJVYCjdx/X5i/QkbpA27p3HlC0ltpQE9b38fJehCQWveu+D2jPT6bLmjFJKspmdjfqapsXEeJLR4fiSSnC6goaMAAoFk0Kh8sOaooCqKRw4XLfkcVTImyQTecwyXmxVVLGIbf/yEokEL7zwAn/4h38IgMvlwuVy8dWvfpXnn38egE9+8pM8/fTT/Lt/9+/46le/yic+8QmEEDz66KPE43HGx8dpamraievYOUQPIKcuQ9+3WPCWhev/C3ny11Caz2y8v9CdxSfz5zj6LzbgcqId7kdWbauB6wxkv8xaB+m9wkJazLwFyf8M4X8Fao1DaIqXAUE6sY++5y6THO/GE2qk6+xtIk1zCEUibcjGA/zw8++mrnsS3ZtHUS0se3NfrZr5Ammvjr2KsEggmFnbQVLMeHj5z94Jb5umoXaGuVyY630HmK4N4Q5ZdIxn6O4eLVGxvTbTdY+cqB2CZkudianT1EZvU/lzVhhO1jOUaEZXBe+wwzy2f3NPzm6PxqNPdDI7kyGXNQgEXURjvruKDOi6SlNz+ahawcpzI35pKXWTs7KkEgnaA/uo8eytr9Kt+AzPjQ9g2PauiNVtBkLAofD2CONOQtNUSs1ZHUjJhh05lmUzNDDP+FgSKSUNDUE6umNlW5HnZrNcuTTmBOokcHuGto4IXp9elqjYtsTl0vB6dbLZteNzuzVOnm7B69VRFIFp2ly7PE58PodYyHp3dEXp7L7397iK+w/bJjH9/f3U1dXxy7/8y1y6dInTp0/zu7/7u0xOTi4Rk8bGRiYnnQLW0dFR2tralvZvbW1ldHR0DYn5zGc+w2c+8xkApqfX5uZ3FW1vh/pTcO7TrFlsLv1PZN0RhL6JHKv7Qcf1ufCqkzZyHQP9hCOmtxqKF3w/Ddkv7sQV3CUMyD8HSghy3wUs0jNB3vwCWGYQUChkvFz+xlkOv+MS4cZ55kdqAAVpq0sppq2gcTbHZI2HvFt10kq2RJGwbyTNYsbIG/VQSBWRwjE9HGyuYTbehjVngwRr4baamsLNzjCt+ShtMo22sIjnDDemvde6EKUEwjR9zmKiGmWDK1IqeNxz5PL1GJbk+VtTHGoKEvNvLrqgKGJP6h4AxrPDa2pPbGyGM/3E3LV7llYZTif45kjPntB/BTgUruV4rI5vDPcseSppQuGD7QfxavdQ2HEBre0Rbq5wsAaHYPkDrjXaKyshpeTyhTGSifzSviPDcWZmMjz8WMdSGkpKiW1Lrl4aWyM+NzwYp7E5RHy+vLidogj2HawtqZlZ+brfv5yqvHFtgvm5XElUabB/Hp/PRX3j1lOkVby1sW0SY5om58+f59Of/jSPPPIIn/rUp5ZSR4sQYutuoM8++yzPPvssAGfObCLysVNQdJg4B8M/qLCBgMnL0PrY5o6n1oPvQ86/pQWF1xxSgwTXw+B5fJnUeJ4EUQOZz7BzJZrbgQ1GL9izLD7R9bxyCMss7ZyxTY3bL5zAtmH1Yr1VqBJO3p5nOuZhNuzGZdg0zeSWamGEKnj8nz1OdjaLmTdx1Xjpn8+BlLx4Z5rxRKkgoa0KfjjyJKdav4Ci2CiKpDM8jqZYGPa90oeQ+LxTxJMdBANjaOrap9FUuplcfjmKUbRs/uAHvfzKk900hO6vNt2kkSj7upQ2RbuAW9298c7ks7w5M8ZoJkXC2IIj+F2iMxjh3S3daIrCs4ceYjKfAQn1Xj/KfdKyW98QIJnIMzqSWBiTxOXWOH7SeVA0DYts1sDj0XCtEKFLJvIkk/kSciGlkwKamkyRzxkMD8YxTRu3RyvbD2DbkrmZTNlxCQHx+Ry1dQGOP9BEb88MuayBx6vTvb+mhHwbhsXsdGbNOWxbMjQwXyUxVazBtklMa2srra2tPPKIkyL56Ec/ym//9m/T0NCwlCYaHx+nvt6p2G9paWF4eHhp/5GREVpaWu5y+DsI2yjjlbQC0kLm1nbLbAgpIf05MO6wVOybmwDjMvh/Eax+kG7I/c02B76TEAvEavkpOzkRpRxRMYvaQm3H3U/gqoTG2TyNs2sVkqUtMfMmvhrf0mtHmpynyi+dHyl7vKz085kLP8VP7H+V7sgYjf4Z6n1zjKXrkQvj1RSBZUvkLgjylcJGERYtja9iWm4Ght9Jd/t3UJTl+IGUMDz+1JpxFE2bb18b5xcf69rF8W0dutAxyhSuSySq2NqUks8ZzExnQEBdXQC3p/z+k7kMz43177pNQDl4FJWf6ji89LcQgkbv7kW9LMtmdiaDUbSIRH243CpzM87CHqv146ogYCiE4MChOto7oiSTeVxujVDIieT13J5hdDiOEAJbSurq/Bw+1oCqKo5hZDliYkmGB+Yd9+gFglPIV65rKVePA470wthInEjUQ02dn5q6ytFs03CMTssdq1j8UfGXq2IvsW0S09jYSFtbG7du3eLQoUN873vf4+jRoxw9epQ/+qM/4t/8m3/DH/3RH/HhDzuFqx/60If4vd/7PT7+8Y/z2muvEQ6H7796mPUgLbjzN9hmFuXIzyKlDbM3nXbr2IFltV+5sBCLhadRc6CUwABgIM1eROL/i6OOu7f+KpWhgbYfrImlV9z+PEa+TEpDCqTc/ciGqqtkZrOEWxbk9U2L2TtzmHmDgEslXmZSFUIQL0T5s2vvR2AvEBen3FoVAksuSv7vBoGRC2MwUNUCAe80zY2v4/XMYduCfCFCJldPwDexkO8XFItBikb5J8zhud010twOGn0tDKR6sFckcgSCsCuKpmx+SllUbF38FHpvz7D/UB0traVaHmPZFF/sv7G5mpdd+Eg7ApGdPeA6SCXzXDw3ipSyxDlaVZ2eNSQcPFxHU0uYVDLP8GCcfN4gGvPR2hZBd6m4PRp1nmWSNTIcZ3Q4vnAs53jTU2k0TeHQ0QY8Hq1silMISKc3XxBcU+dnYjxV1udodibL1UsTPPDg+ilnt0dDUUXZLqZo7K3l31XFzuCuSuo//elP8wu/8AsUi0W6u7v5/Oc/j23bfOxjH+Ozn/0sHR0d/NVf/RUAH/jAB/jGN77B/v378fl8fP7zn9+RC9hTSBMGn8MOtcONvwIzA9hgS2T7GURrDmEPOduqHY6YnNlH2RZqaS10ntxHlffaAfA8iSy8tLQOdJ69zfXvPohtLn9VFM3CG86QnQ+tUdzcaUhLkk/mnXCFgIt/esmZ4G1JQ9hNqi1YYp+pCFg5/8mSBjwbSy5KKMPuRGAWj6lweN9XcLtSy+8ISX3NVaRUkFJBCBspBfuDP8d1ISjXfaprJv3J52nyncKjRXZhvMsw7CLJYhxFKIRcUdRyNVxA1F1L3soxnh1FIJDYBPUwnYH9mz5XNlMskZxfvPSeW9PU1PpKZOd/MD646aJdsdASvlMfrUtRebyhbeMNdwBSSi5fHCvbjWOtIAa3b05j2Ta9t5fvXzJZYGwkwdlH20tSReCQxdWkQEoYG02y/1AdsVo/mqZirdLP2aKMFKoqiEa9zM5m10R2bFsyP5clmyni81du1VcUwcFDddy8PrU0ZqfjTqFzX7Wwt4q1uCsSc+rUKd588801r3/ve99b85oQgt///d+/m9PdH7CKcPWPHVG8lRh+HTxeiC3cUmvA6fbxfgDnNpc+0dxrTYmyMHuQSi0TnKJBngegbv8U3Zmb9L56FGkrYEliHQr73vUYr/3Btd0djwBb2tz46g2ngNewSibHupkcpioYaXWIjL3BrKsIm5bgFEPJJnZmlav82C/l2kVg8SMXojSV9OZAnoc6Grg4NF9iCqkIg5rYG9yYu8aNuS9zoubnaQs+uv3RSslUbpzp/AQ2FmE9hkv1MJ0fx7Cd76dAWYhZSfaFjhBylVc4bfK1Ue9pJm9l0RUXLnVr7c3TUxXagYGZqQyt7ZGl16by5WstVkOxoTXhZyqQI6/Zd/URexSVA+EYD9e17Jl1QDpVwNpEO7GUkp7bpZL90nak/G/fmqa5JUQo7F3qSDKKlY85OhynvTPGQ2dbuX51gmQiv2XysohsxqCxOUQ6XSybdhICMun1SQxAQ1MIt1dnqH+efN4gEvXS3hktIbZVVLGIey9u8KMIq0xBoQ1yoohYJDFIkAaOdJmyhz49G6EemKrwnoUlTcbtfczQQJhRQJB4oIXQMS/WjCT9xSJGMcz1rw/t/lAXot9WGfuBRTRNZjkR9dPygQP89+d7VlgJSBRs7AUlXGUhGjaUbORuCYwQjr2ExzVHrlADZdR2g/4xXPr6i6+UYJh+JuZDfOiBKLmiya2JFIoiMSyTmth16mouLkVorsz+BfW+o7jV7Snn9qfvEC/MIReifzOFtdYXEnuJJ/Ymb3Cy5ixKhYiMqqj4le0VWpYjeeD8WlaSG7lg4LiencDCwHGbKtG8m3DBxUAkTdptICTYK4Nvm4AmFP7B4dPoyt4Wgm8kBLcIuZwVWvP69GR6qX7mwOE6mlvChCJu5mfLdw2NjiRp74zh9mh0dtcwM51idHjrmj+Lxbvx+VxJ1Gj1+Ly+zRGRSMRL5MFq+qiKjVElMVuFooGwoNy6uubHWwCZgsA/RM7+juNovXQclgrY9jQqowhQDoF5m9KZUIB+GCGcibuIn2mWPZSC6jS1NXdQPpJjqreJ0asdlFu8dwrhthDJsRRyEwZHtmGSN200VVl6km3wzdISnObGbCc50+NQGrl6NdteAYWUKm7XYvv/ouh/6XGCgZGSiEt5CEYnHnW0MWz46Jl20nmDixPPMWN8G03LrdpaMJG5TEfoyS2POW/liBdmFwqZNwkhSBTjeDXfUpop4qrZUt1LJdTV+xnsX6vgKgERVkkZRYK6i1uJWQx74+iEx1A4OBtGQaBIwf75EIZiUxQW04E8877N1XZoQuGBWP2eExiAYMizMBfcXYfiIom4c3OaYNBNZ1eM+dnRstuahmNRcPnCKMlEviIBWYlF7ZaVkJJ19xVCEAp5CATvjSBhFW9dVEnMViB0aH0CRl9gzUSjADWrb6cb1BaEqwv7dggsR8pdHPAgfCoyaUJwj/VL7EmQxYUBC5x6HRcIF7b3I8T74rhdXvLeHIvXWM8tmsVVFN1CtEKoIQ7AyOUudqWuRIF8srApAiN1E6MjTzxTxFhhiKQpNqcbb3J5+gDO8/3OjrNQrGVJDHF5NIBEERYed3xDsV1FaDx2pJZEJkXQ67DigEenNpQnnijTqYVkuzVUWTOzNQIDICUz+UlSRhKQCATD9LMvdJiQK7KtcSzCH3DT3hllaGC5XmPOm2ckkuXCiFPsG3G5SReLmxq1y1KRQoJcvum6raAiyLg2Z+uhAA/WNPLEHtXALKJYNBkdjhOfzxMIucsaIG4Hti0ZHUlw6Eg9ukspm1aKxryMjyVIxPNbiwRtAUIIGpqCHDi0t0KIVfx4oEpitgIBouNpZMgN1/9ueT1RAI+CaFiZ69Uc5Vv9iLPriU8iX/9/nOJgr4I0JfJmDnF2bwTKSiDnF/6hgnYYXMfJpI5w/j/fcOpOXOB9r4paq6BaRVo8V1BWRBVU3drdgl4bCslKGiByoXpTIR5VudFeg5a04fIYsFzYO5au5erMPmx7PSax+J5c9fdmUfq07nFPEwqME430EPCVdyl36pNVEIKo92lUVSUWtLiTvMbR6Encqocm/0P0JZ8rcbpe3LnB98AWx+jAXE8+oAJsbFJGcin9tEiCepM3100zbRZd+2qobwgwNZlm3s5zsTBbQljixc3rwCQ9BrmMhdcAdeFzsYRkzlugqC1/WQMFjaxuYQtZ8nFrQuGXDpwk5NrbSEE+b/Dmq8OYpl2xRflukE7luX55AsNY+4NVNYXu/bXcWCWQt5PweB016fuyBrCKtwSqJGYrsA3kzb9GOfsppDeDHH4DihIRdUFdAOE6CuZNHEG70+B7PyykZ0TsADz5fyCHvwTcgdmis3bmbPDttZrsIiyw55DuJ7j4B69QXPRXKUL6ry3UOoX2RzLYLQqKy5kELVNh4M39jF7dKApzl72u5eolVJP67gk8oSypnJ9X3SewULEslUVGqQhQBdgo3Jzdj70pe7DV41yMeChl3is/OCFMDu//CqpS/qlfFW4UodPoPU3SNPHorShCXzq9jcVUbpy2QBdhdxvdoXcuEBkLgUAIhaOxn9l2h1LG2Jq+ikDBq3nJmmXqeoQgWUwQcce2NZaV8AfcdAXcnOu/gbwb7ToBPTVJYlk3sZwbW0hmfAUSnuU0UmPSS33GS1G16I+mKKpO8a+iCD7SeXjHCYxl2cwvGHNGa3yoq41OcUwTDWP39E9SySKpZBkzWgGnHmrG53ftWr2eoggam0NVAlPFrqJKYraKeC8Igaj/ZUTNB50WahEA/VB5W4EVEPoEouEOANKUTo1M1kZ6F4jOCpGnPfvh29OkJuIYZTxNrGkbY9yN2uWMT0o4/+XHSU5G2JigbH78QhUbpo5soK57kmPvPY8Q8NrYUehnDZ9QheBdxxp5sD3Kp797i/LFSxvBJuAfx7Y0svnGMu9LdC2NLV1YltO54vPMLLhPl0dH8EmOxD5CykjSm7qFLdeOK2MuE43DsQ/THDjLROYiitBo8j+IX99+OH6zqSS34iakR6n1NjCZGy1PYuTmj7dZzObvXg9HCpj1F5j1r2VDmiVoyHhREHgtjaMzUQqqhanYHN3fQJt/e8XSlTAznebalYmSWN+xE43U1pVGXiup3G4Vi9PF5oM5gvn5HKGwF69PJ1EmfQngdqsU1imsrwRVFXh9LtpXGYdWUcVOo0pitgrXislOrXX+2wjWJGSfB+OV5dcimpN+WlHgK6WE+YU6GX2vnl5cWEUqco7UVAQhAiDnKGTctBwfQIgOEuM7o9kgFEG4NUx8KL5uPaMCDM80c8S+iCZsCqYLq4zYniUlRdPm5liS1DYmXwcqmWwj3W1/R9/we5ByuaPC752gs+17uPQ0IMnkGugfejeWrcM6hbya4kUIBbfqcYQSy8CnlSqZhlzNhFxb96Mqh5i7lkRxrkSgrhyKdpGEMU+zv42Yu454Ye0+EklIj9z1mAqmybdGexlIxbF22W7DEpK0bhAyllO+bkvFbank5w1o3blzFQvmGo8ggGuXJ3jsyc4SHRehbO93LsSirQsoqsLxB5qQUjI6HCefN0mnCusSGiklxoIC7qIL9WooiqCjK0bvnRlsu7Ii70o0t4QQiiAS9VJbF1jyXaqiit3CvTKTuT+geJxi3a0gM4H9rV/HvvInyHKt1lKCOQbmMEjbUetN/Edk4ZWSSUD4VVCXJ7Elnym/WtrFtKvQwfMkoZZQWQKh6AoNx+tBc5SVPYECTYdGePDDr1K/v7zk/1ahuTUOf/AQmltbd0K3gbjq5cbMPoQQ7IuOoCnlScrV0QRfvzJ2lyOTeL2z7Ov4OzzuOcBG1zIc6PpbPO4EimKhKDYB3ziH9n2JSKhvnUiMQp3Xka13qx7CruiCFsvKLRQavDtDWMoh7IoSdkVRNvjJSySGXWQ8O0JIjxBxx5b2EQgECh2BfajK3aVAc6bBf791jt7U/K4TGACpQH8shaGsJXF6BRn/7WJqsnLqbnIixdxslumpNNlsEbd7e8+RUsJDZ9t46GwbT7yti0jUSzTm4/jJZvYfqtswkisUiMYcK49yaS5wCoNv35xGCEEk6qGuwb9usXpjU5BDRxs4eLie+oZglcBUsSf48Y7E1B1HnPpl5LU/h7HXwN5cFwNWEUZeQmYmEY/+y+XXzWHHJ8nO4FQB60gpkKNpmDIQp0qftMtONK6tm2ZuDwponaB2oqqSIx86zPWv3MC2HGdoRVfw1/mp7U5i52+xuGYJBVTF4ui7LzLV08Tdtlmf/PkH8Nf4eeTXH2bwpSHmB+fJx/NYxVJhOykg3aJxoq53QbRuhqO1/Vyf6cKwl4molDCZLB8a3woUYeLSM7hdacLBIZLpVtLZeoQo/Y4IAW5XmpbGNyoeK+beR8S97H/UFTzIaGaQmfwkNjZ+LUBboHvHjBMt26RgF3Apbky7yHR+EsM2CGghCmaerL2Bfg2S+eIsbYEuOgMHyHhSJIrzKEIl5q7dkXF+ceDGxtovOwyJ0wHVkFn24VIUQXPL9lNJlmUzP5fFsiTRmBddV8nlimULZW1b0tc7i7TltgXlVsLn10sISLFgMjwUZ242s2GhbiTiJbbgR9bSGiYez5W1CwAwTZv4fI5IzLfuuLuqirpV3AP8eJOYyTeR374AnpjjUoYC2OBvgGIG1iuGtA2Y70WmRhHBFpAFSP1XkCvaI2XBaZWZMKAooSCR7vXrXXaSwCxOOGsPqQISzCEw/xSyCg2H/hGBZ88yen6UQqpI3cFawm1hpi99lrbja8mdkxG5i0CeAt1v7yLc5ijCekIeDr3f0aUppItc/eJVEiMJEAKpCcwHG/nJU0UURWexzuXDB17gSM0Al6YOMJuvYSYbKivdv3VIIqH+BYNLsGyNufg+2ptfRFG2fgKvVlPyuSpCoS3QRVuga0d1gqSUjGQGmM5PIISypu5mvjDj/GMTp1uKvghBQA8R0Le30Nu2xLZtFEVwIzHLhdkJ5vNZjHvg1i4VKGoSqTi1U0g4dKQef2DrBb1SOq7Kfb2zDulXFjrPxMJUUgGViMJWEQp7iM/nGOyfI583CQbdC0Jz9oYEqa4hwNHjjUvfu5o6P83NIcZGk06ReZkxSslSkXI5LKa3qqhir/HjTWLA8TDKTZe+lplc6ipaF4oC6XEItkDxinOsNcfHsSKYMJDXs4gH/Eh1j4p3Jdi2grqixdSZaBfHWVgeY+oP8Nf+Jgffuyxwd+ubt9FRkVKsSZVIKRZanbd3DY0PNNL5VGfZ91x+nQc/cQoja2IVTbxRr3OvrHlILBMqIeBQzRCHaob53OWf2yECAyBxu5NYlhPhGRl/lLrYtU2I15WHJZcjQ5ZdJGWM41aDeLXYjn4HpnJjTOcnkUhkue/iJk+loFDnKVfQvHlYls2tG1NOakVKbB16QgnS+iajnbsAXSg8eqiNeunDtmzCEW/FVMp6kFJy6fwo8yv0XBaJy44ElzbQu1NUQazGy9VL45tyl16NI8caSlI9QggOHK6ntSNKfC5L753ZLXdM6bpa0YG8iip2E9VvXSVsRgjFtiCwUMdgJ1jtjwQ4hbv6AgUoSOQbacTDAYS6uwTGtuHG9x6gkPFz/L3nUFQLIaCYc+MJZtdGZ6QFxm1wHVl6afrWNAotdDx0B9TSWVVRWOG2t3V4o541C7ht2tz5Tg9jF8awDZtAQ4DDP3EI30LuHjXqdIEZt1hpqnl+4jAjyZ3U2xFMzpxgPrGffCGMohi0t7y4oXhdOahAs7cdgP7E97k5/1UECjYmUXc3p+v/AS7VX35nmYf8G2ANgtoA7kdhHZn/ydzYkqbLdiAW3L5Drgj13vIO8/HCHOPZYYp2AZ8WoMXfsaYgGeDqpXHm53PIhUU2b5tkVfOuO++3CxVB1O1lfyjmWFlY9rZrNkaG4yUEZqfR0RllsH++7HuaJjj7WCdvvDK4LW0Xj0fDMm36e2eZm83icmm0d0aJ1fjwenW8LWGmp9LMzmytW+zIsYYtj6WKKnYCVRKzXSg6xA6Cvx6ZnQYrVWFuFhh5N7P17yTvbUMzE7Qq39z14d1+4TgTNzsBePGz7yVYm8C2FPIpP6d/5kWCdav9UQQrSdjo+VFHcE4GuPn9kxx+5yWkpSxtevkbZ8FeXQ+zaOqy+ul27co1/MoI3W/rLnnt6peuMXtnFnvBOiA9meb8n1zg4WfP4q9dWCgDn4TMX0PxHGAznNrPt/qeuMvkROnekVAfPu80+UKEuthVwsH+bR1VBaKawJ99jduFFnrSf4O94h7P5Xs5N/U/eKzpN9bubCcg8Z8cIkMR0CH/PQj+M9CaF4pvR0kU51CFSoO3GVNuP8ohELT42gm5ong1X9ltZnKTDGf6l7qVkkacdDzJociJEiKTyxrEVxAYgIlAbsseRneLoOZy3DQUhaOROh6I1HPnxhQT4ymklLg9GgcP11NTW4FEloGUkv6e2V0c9fqFwbYNl8+PlnW63gxM0+b1V4cwDQspIUORRDzHvgO1S6abnd01zM/lNkWShIC2jgixLdzDKqrYSVRJzGag+6HjHTB7G+I9oLqg9UnwRJHf+RR4JaJbA9/a0LSUGkOhX8QWLhAqhmtvdBMaD43QebqH9GyI/tcPkZx0zqvogCj3sZugHXDGbEt6vtu7tLZP3Gpjur+RmrZpbFthbqgO21pb0OuNJClm/FjG6vuwduUyCyb9Lw4wfXMal0+n5ngt8/Nz2KtSNrZpM/TKEEc+6ESIknnB9+88xZ2pU7g1BUWIJQfm7UGi68kFmwCb+prLhAKj24q6rIQCHNSC/KDnXXw3XQfCQoifp7P1+4RDgwtntpgv9JEz5/Fqq74X2b8BmWZZFtpwDEUzf4EZ/BQ34pcwVqjwDqR7UFC2rd8iEGiKqyKBkVIykh1c025tYzOaGeRA+Kjzt21zfnKMOVeeQEFHW2iDz7iMPSMwCoKD4RgfaDtQ8vrVS+PMziwXveZzJlcvjfPQ2VaCoc0VK2ezxqb8he4GuTKaTYuwbUkms/3vu64r5PNmSdrLtiV9PTM0tYRQVYVQ2MMDDzZz8Vx5vyWAcMRDMOSmoTFEKLw3Lt9VVFEOVRKzGYTaUQ59pOQlOf4G8vLnEPtVCGkgKFukOZl+Clu4V9TYCGypoordU+kECDc63j3uQJ5oyywX//YR4qO16O48gZq5FVsKQAPfh0FxFrBitoi9SqbcKupM9a7XAizJJwJ4IxnMgoti1s26q5aAgRf6sU1nNp0dmMN9SsX/ITf5cyaFc9biYUlPOE+m2aLJZ37QQ3bxKXLLOjCSaLiHhtqLaGqBRKqd8enT2JbGwa6vb/FY66NJU/j2rZ9gOhtBoiwQQo2+ofdweP8X8XqcdIGCRtFK49WiWNJiLDPEbGGK41xGW0Xo5mULo8ZxCnOvlz3nRhowa7AiQGZjM5juBSQ1nvo1mxq2gV0hxZo0EkgpSRoF/qTnMkXLRoSdcqlgQaM+48VE7loqSRNOiGfRO7HW4+OZ5q6SbQoFk5mZTEl0CJwFfLB/nuMny6fPVkPaEkVZv3j3foZpVuqMEmQzxSUyFwy5aWoOMT621tFaVQVtHVHq6u+BZUoVVaxClcRsBl3vAkCaORg/B4UkcugFqAdC6rr1LTmzFSFs/K4BFFEkazTjPKfvLolZ5FJCOF5HB992lXN//SQP/MRrK6IMwvFO8v0EaMtqX7pni9o5C8eSUiWXCOCNpDANFdsofxylQWDPyiUCA4AJhQsW7pMantMadkJi9NigQLDZ6Yx5c2COgrlx90UluF1xOlq+j6o6aZda/TrRcC837nysZLvFLpOtYHEfKQUuIYlYtczlgg6BWQFbaqSTP0Us+CI5oweJJKA3IqXkTuLaklGjFAopWcu03I+JGxdpZulgMz9ZbUH7yFztvbQaq65RYjOaHSpLYjRFq1hvI6UkXpjjrweHKNqOlP9ivXfKbZJyp8qebycggH9w6CHSRpGZQo6Y20ODd+3iWsibKIrAKpMiyWY3H9nwB1xomkqxuLu/35VQlEUxzPW3a2gKMD25fnt1JcE6KSW6rmLbCwXZE46YYzmomrKlFFwVVewmqiRmM/BEkIlB5Kv/wSn4tQzARhz2lyUwUgLChVB8+Fw56nx/yWK9iBD2vahrJFCTJPJJFZ97ZUGiBHsGw2ig77u3mLwyhW3aeCIeot1RZm9vPfcvbYVcPFCRBGhtCtiUEphFqGBN2ugdKu6HNIyeIqqm0vG4Uxg7OJvBvIsWJMMIUChG8HmdVmMhbCxbo7P5BbxADjBML6qa2/Jn5LTWeujwBDjoznJruhZTlvt5CXIFjZD7DEiT7tAjKEJjOj9R4jQ9IM+Sph4bFWeprmezLMCUBh7Fi664yFlbk7U37GLZiKIiFJJFjaBurvlshYDe1ChpswwZ2MUvuwB+cf8DeDUdr6ZT5628sHp9+poozCK2kg4RQnD0RCOXL44ttSIrCni9LnI5Y1eMFL0+nYOH67h8YRzLqhwCEkJw8qEWLl8YrZjyCgRcJJOFknEK4dwDj1fn5vVJpibSa65j8TMPhjwcPdFYFbKr4r5BlcRs1M8I0PstZGIAzFUdCZV+x1Iie4pw5FPUB34fZVXX0h5rfAFg4sZ26UzJ/TSL60uvW8V53vjjN8nNL19bZipDZmr7ni6KatN4eIjRq52OOMeKGxV9d5D4D1Pld5QgvM62ik8Q7Yxw4H0H8Ua9ANT4XfRPZ7ZdxCuBdLYBn3eGufg+hseecuwCpMLk/AjdHX9HPNFKbezOlo9tWRod/vdzsvEZMK5z9ebaMPziKOoiForQ8LuOciv+d8wVQQhXST1LkiZKv2BbWzQKdp7OwD5GMoMYG0VkVl6HLbgRn+FQuAZVKY0ijWQkhyPlR5K3zD0n5y5FpdZTvoZnNXRdpaUtzOhwomSBVlVBR+fWjCyjMR+PPtHJxHiSYsEkEvVRW+dnfi7L1csTIOWO1s1kswZjI0nqGvxMjFX47QDpVJF8vnLNjhCCrv21pFMFeu/MLEV3pHQ8kvJ5g8nxVFki5vXpPHimDdcOqxtXUcXd4sfbdgDYVI/w5EUoJNa+PmuWf7rL2zCTRt7+PZRdThttBrYUzMguJBpJSlshJ+8coJC6G/vgtbBMjbnBhjUERtEExUkTtVZZK/QrQPEL1LoFAa5oHQ998iGCDU5qYHA2w/WxJKxKaSjCwqttTqFXCIlLz5DONDI48k5My4eUOhKVZLqd3sH3outbV/t1FgKN5sBZEAq2foz++RCViEdbfXFh7D6i3qeRqGUKcu+OEkgk47lRWgNd1Lo31/5q2XAnJfneWD9fGLiOtarwQ1d8ZbV4JrNwYba45wq8myUwi9h3oJZ9B2vxeDRUVSFW6+P0w2243CoT40n6emaYnEhhr1Pwks0WyaQLuFwqHZ0xDhyqp64+gBCCUNhDfYPjF6SsM7NuOVVpS6Ym0/j8rnW3Mw2L3juVo6ehsJtI1EtLWxhNV0oepiYn0px7bbhiusko2lUCU8V9iWokZjOwLVDX3io5UkDEdKQul52YJciePCChmEASWLMc7YWw5Uq1XoGkXtwmI2OorGzD1YlPHsU2F5/UF1ukFwe4zYFKQa6MbottShLfyiwJBqPgfANtUEKCwE+6EEKgCpUWf/vSfqm8wZ+9OoCURZoDc0xkarGlM7p94RHe1fk6f3HjvcQL66vKCmER9I/QN/we7DWpHkEq3Yph+BideJzu9m8vFd9WvMyFe1woBijmH8DvWbi368jnqAroGkhpY9oJdDWG2Iyw4jaQt3IMpnoI6GEUoZZ1zl6s5SlY0J+GyZyjWj2dy3IrOcvRyLJz9mMNbfxw4iYHwk5KVBEwnIb+9Hr+3bsDATzR0La1fYSgtS1Ca1tk6bVC3uTVlwawTBvLkqiqoO+OukBulr8j2UyRK5fGyOecz1hVFY490LjkP1TIG7z60ua0Wzq7YwgFBnrnt5B+kng3qFVTVMhnKrfZL7ZBj48mKZYpii8WrSXl4dUIR6odSFXcn6hGYjYDRQNXGZEx6ULm34cc8yAni8jhAvJCBjILT3JpyerIwV5Bsqq4V9h0idepMSYWXgyD72fx1rYucJVF8rIYPdklpmXhyJ7Yzim8T2mEftZN/S9E8EV81LobOBI5WeLPc3FoHsOSWLbKaLoeS6ooQuLWDCazUf7g0k+TKGzcKSGlQjrbRKEQLnt9imLS0fICPu8kt/o+gmVX5vi2Lcjk6rClhq4VCIff4Aejv8mNua+gKILOGv9a8oqkMWYgpY3EImf0s221wE3CxiZpxCs4Zzu1Nq9Mabw6rSwQGAeGtLmTmCvZuiMQ4ZG6A9yOuxjLwmga+tICuUvfFQU4HK4pe/QnG9po9W/f82gRt25MUixYSykYy5Lk8yZ3bi2reNu25MKbI2QzxoKNgsQwLC6eG2VuNkMykee1TYrPqapCtMZPR2cNJ041bfqBxuXWiNZ4192msTG07vFiMR9SSibGK6U6wed3r6l3UVVB9/6qL1IV9yeqkZjNQJpw/BNw4b85URnbAKGCrw7a3gGjk8jsG86a4ALR4YFazWEPSgTsOM6qvXeEplzdnSxCzzdbUTzv5NhHjqF7dJofLND3g/4dXks32UtrgzKnc/DhQ4RdTl3CWDzLV85NkilanGyLcKotwvXxxMJRlxdZS2rkTJUcm39CtG03PQMfRGCUHaOUCl7PLO0tP+R2X4xMtp5QYK0btpQwM3eIWLQPVTGXrlhKGEg+T0A9yPuOd/NHL/djWDaGJVEE6JrkQFuGvDlOungRy04T4sya4wsUGjxNzBamMeTdaOAsHg/CepSkEV9qw1ZQ8KhefFotMFd2P8+q6KOUkrFsmumCyVRh959/3KrG+1r3E9BcXJqbxJISn6bzdFMHB8N3v6hKKZmr4Ac0M71cEzY2Eq/YjXTp/BiKIjYdUbEsm0LOgLCHbMbYVH2cEHDgYC3Dg/HK2yiCts4oxaLF6Eii7HEvvDmCy6WiapU/u2DQzb4DNQz0zZHPmYTCHrr21eAPrJ/KqqKKe4UqidkMJHD+90H1Og7VQl3wXJqBl/4VdGswXoSijTjqBb+KWGQR9gzgAv04GJfY7SfvdSHANgXx3nku/vklzv7qGdxBN43HG5m4PL5DJ1kkapsgMRKCdniJwLx4e4rnbk4tvT08l+XF29OkcpUKU7cXAZAshuUXTT9BEQaN9edQVRPbFhzq/krl/aUgV6gDete8Z9pFvt/3bYZG382B+gBddX7mMwZ3JlPEs0VevOzl2MFXUZUCiiJJ5t8g5DmLQFlIKwkUoTCdnwAEKirWXdZVSSQhV4QGXzPT+QlM2yTqriHmrgMEmlApriLYmlA4EStttf7iwA2GM5Wf4ncSAniophFFCN7W1MGTje0YtoVLUffEaHCxNqRQMOnZoEtvqx1JN65NMjWZYnqTxfNSQl/PLIVi5VTRgYO1KIrCvoO1FAoWM9Npp9V9xcdq206UaT20d0bx+V3Eaqot1FX8aKCaTtoUbLCKUEyAbS4bPVoFyGTgasIp5vUo4FtBYJaw4Et0LwkMYJkKiYkY0pakJ9OkpxwRudaHW1G0nRmbUOTmjQZ1hfrDTs1FwTBLCMwiEjlj1+JXfu8EmpbB65mho/X7NNadB5wCYEWxUZRVZ5Yg8zrDw2/HMIJlL9NZXy0sW3JnKs3tiTQHG4Ok8iaWBMvycLPnoyRSHdi2SsEcQxdZFKEuncSSJhYWFuZdE5hFJIvzBPQQXcGDHAgfpdbTgCIcxeOf6TyMT9VxKSouRUUVgicb2mj2OSlUKSV/O3hrzwiMIgSHQjWcrWspec2tajtKYIQQ1NSVX6ylhJnpNGMjcXb6d2vbctMEZhG5nIFVTpZgxTEBFEXh+MkmHn2ykwOH6sq3QgtHuXc1mlvDGxYPV1HF/YZqJGYnsDi3eJUK850FbM1QbSchJViGyuWvPbKkQiYUQT5RIFAfINwSovlkiLFLcWxzZUfRVhcMG184Qy7pxy639qoCFmoPVF0l3Bai9mAtABeG4tu5tC3BaaaXS38VjSBezyzhwDDh4OCCWF2FwmsJrr95N2oqxEEpmIspiPa1H7Zlacwn9jv/tiWDsxkawh6MFfoeRSNE39AHAHjmuA+bqbsybtwM4sY8trRWkKVl1Hn9PHv4IUYySYq2Ras/hFtRsWwbRQj603HupNYvct4pPFTTyEM1TYRc7j0536HD9cTnBzCNtZ/llYvjxGp890QSYTU2GsP4WBKvT6e2zqkN83h0XK5F0rdqZ+nU5uguDcu08PldtHfGiNVsrduriiruB1RJzE4ia98Th96NIG3Ba3/+NPnU8lOnNG2CjcvFsId+8mEaD3+diavj2CaMX+9Eyq1cjKTh4AiH33GF0aud9L16eHniFQLZFkMEXDCVIRr20PxAI3VH65aiVrugEVZmhE6HkltPkC9GMUw/RjpIOtPE1OwJDnZ/GZde4QlZCpSMH2yBAtTOS8zLj2Kdeg2JjS0tbFsjmW4jnlw2tlQVgSoEuqpQXCVU5tbAG5jddQKziJyZw6+XL4AWgCUl/al5zs+MM55NLcWA9ipc2+IL8PbGjl1LF83PZenrmSWTLuDx6HTtq0FRBdY6wa5UKr9EbstiEzJTe4FMusiVi+MEgi4ePNOKpqkEg27sCgPPF8ylcRtGvipeV8WPLKokZquIaYhGHTSBnDVhorjsIJCzIWkhN7Ai2EtICampMAeevEagLklmNsjg+f0EWo7jDpY+7Qa738/Vr32dmrYp3MEs+aSfzbIy1WVw5JnLqJpNx0O91HVPMNXbhJQKOX8Tk9kOZ8OQG09ziIZjpdolJ1tDfOf6RPkLgB3sS7cpmkFWLs1S6himYGbuOC2Nr5XdSySCCHPFz8WWuG40c/yxf8Gc+wo909P0jDSRTLew8p5ZtuR0R5Q3B+ZKnCYaY0VOHcjvaUK3P3WbY9EHS0iClJKcafKdsT4G0wnMMl1Mu02xBHC2tpknG9s33HY7sG2budkc1y6PL6VdMpkiVy+Pr09QcPRRKmGjfe8F0qki598Y4eyj7Xi8Og0NAaYm1yrwriReti25eX2SRx7fPQJZRRW7hSqJ2QpadESLe5mgeBSo05CXs0szvZwoIoLeBeuBex+YEQJCjXFHDVcBbzBLrH2a0Rs2c/0NRDujCCGwLZtrX75OPuFnNNG14XEVzQQpltysraKLwfP7aH+wD0238EUytJ3qp2AEeOPG0yX7xudLU2sj81m++Obw2pNIiWraWKqytRu5bGS0lvzI8pomUmrEE13lSYyp4Hrl9JqXhSIQST/Rxndxa2CA1KqiSV0VnOmMEfa5+OUnu/jyywP4+uaptQvUPqzvOdE17CJ5K7fkVN2bnOd7Y/1kzOKWgwmaJZACLOXuV/GjkbpdITCmYXHrxhTTU+mKZONuSMj9RmAWkc0WSSbyhCNeDh9rIBByMzqcwDJtjAXz1NXI5wxM00bXq4J2Vdxb5PN53va2t1EoFDBNk49+9KP823/7bytuXyUxW8G4gfQoiHqn+E2oAulSoE6HSQPCXsQhja1rl2nA+l0DdwOxQvZFKKAqNjUtV3jjL5yUztEPH+Hc58+THK0slb+SRQjVouXYADWd01z86mNLr/e/doTUVJS2B/rQvAbT+UOMjB3DQkWs+Ka5V4iI5Yomf/LKAEVzxROvlHjzJvWzeWLJAhcOb62dVtiSQNYg7dedGpgFIiOEic8zTTa/1uAQQFXL20NYt46gpKKsrtKQpsRX5+e/vtJf1lH78X21vP2Qc66ornH0+iyGKOL/gL5WsXgvIASWdL5nY9kUXx++Uzbysh68hkpHPIDbdC4gq5sMRNMY6vbiNZpQOBat23jDbeDShTFSyfx9STY0TcFc8Z13uRSKho2qKtjW9k1OF5HNGoQjXoQQtLVHaWuPAvDyi/0UKnQoVVNKVdwPcLvdPPfccwQCAQzD4Mknn+T9738/jz76aNntqyRmK7CB/gLSryL8ziQuVAFRFTlpIBqUBYGWrc5Aez95+KJpLNNk6voUQhHrEBjQXAZSKkhbIBRJoDbBvsduARJ/LElmbll0bKa/kZn+xoUdFbCc9lQZ9iAOxFDdGh1dy141V0YTa0zthIRIskgwZzITXdCBKRtVWXGfFyIvHivPkfwt3KZJsbeF/voY8YiGIkxqojdpaXyV230fJpOrZ2UuRxEG9bWXSw6fzjYwMPwuDOmHwyr+nMnhgQSeoo2iKdQfrWekmKdgmqz9DCWpYmEpPD96fgwTg8DPuhAu7k3YXkp8mlMT8/r06JYJjGoLDsyGUOXyffMZGgdnQlyrj2/6a+yo/QoEcCrWQJ3mIxHP4fU5xag7gXSqQDpVuC8JDFBCYJy/JUeONaDrKvmcQc/tmXVbt4VS2j5dAgn+Cl1GrW0R+ntn1xhA1tYFUNVqs2oV9x5CCAIBZ54yDAPDMNadL6skZquwQU4YiH0OiZG2hMLChOARbF2APYTjn7zzWK+cxCpqjom1YTNxqUwtygoceeYiimaTS/oI1iYJN80hBJgFDX8sVUJiSrByok7k4eYM+3/2ODW1ywXGqbzBar86qQjG632M16/olih3EYuvLVxoRB+h+8g3ENKRsxPKm+zv6cA+0F8S+ehq/xY9Ax+iaAQAiZQqdTVXiYR6sG0FRbHJFYLc7vsIS0RHgbRP4/LBKE8Mp2l/uJXoQyFeGOhFynKdNIKZTJKMkcar+pidn8b9NhWh3hsCIxC0BbpRFsKEs/mtf+diWTdiVbG3gkCVglBBJ+nZnNFkzO3lSKSW7kCUyZ4Er14fQCgCaUsamoIcOlK/rXuUzxuMDifIZYuomlLGj2rv4PNpZLObj67atmSgb45Hn+ikUDC5c2um4rZCwAOnmslkivSU2S4Y8hAMle/uauuIkE4XmJ5ML93zYMjN4aPlo5NV/HigKTvL/+fC5/fkXJ+bnubMmWWRz2effZZnn322ZBvLsjh9+jQ9PT38k3/yT3jkkUcqHq9KYrYDY2XqA+TkwuSdtpB+fWtERgjYgsvwViCEIzCMUsoBLENl6FIXi4/OlUzfFpGeDdFxuodabdWjnyKJJ5ftGGL7YySGEljl1E0liLxJeEUqaWQ+y9WRROXBbxYLbaRJu4ErPb/Avs5v4vc6svH2of6SCIECuF1Zjuz/X+TydRimD793Ck3LYVouegc+wKF9X6G3/4OsqboVAtuj0PjxE3Q0BLk89waxkFWWxKiKpC6W42ZiIbpzBnRFuWeFkwdCRwm6wgBYtk3W2vp3zm2pKGXCLUIKvIaKbilYwkaVgrxqkfGUb/uJF/NM5jJE53SmJxfqVRYiAxNjSTxenc4V0brNIBHPcen8KLYtS0qi7hW2QmAWkcsaSClxuzWaWkKMVfhtNLeGidX4nf+iPm7cmCSVKKAo0NgUYt/BuorfMyEER483kt9nkE4X8Xo1/IG9aWevogqAuro63nzzzXW3UVWVixcvEo/H+chHPsLVq1c5fvx42W2rJGarEEBEQy4IT8ne/EJrtYJMBlAatuiCLCss4jsERXWIjBQKtqkgFJux6230v34YWEiHSbnuhD96tZO2U30oir1U72NaCiOZOhLxKF4sEHDsp46i+3TO//EF4gPxNcdxtGnyBOoDTCbz/PHL/RirwzDbhsC2Xdi2izt9H+KBI3+EopgLxdUqoCBZFs5TFPD7ppeuO5FqZWDkPbj0JAG9jbqIj5HptWexbUjmDbJmGhsbj0vS3Vygf9yNZTsLh9dtcfZwjqBvBelTnWjITsJIuCiM+rGLKkK3cTdlcMXWOpJ7VC+a4mMsmyKsuxnOJDHXcWquhIxuEBOuknTSIhrSXoSjOwyAhWQ0lCHpL2KsIvWWlPQl5wlOiDXfOylhsH9uyyTm5rXJJf+jxeNsF6oqSo61XSxGOjYLTVOYn8uSThWJxrzommBwxe9IUVhqDV+EP+jmzMNbL4r2eHU83vUNJauo4l4jEonwjne8g29961tVErNtrC5xkcBYATllQk6AojvWxP5GROf6KpxSyl19El+MqKw5hxBc/tZHiHV76Ht+DstYnrzkJibrYtbDuS8+yaGnLxNpnsO2FG6MdvPt/kd5oJjEAvo7gpywbOqFINYVJTGSWCJ6K88VbHQiNy/ensLc7kJRUZFu4W0E8WQntdFhwu7HcGtNAOSMARKFV0q2dZ7YBZMzp5G2SnvTHfyuJ6mNmIzP6kvEZHl7QWvUhxD20vficEeRWMhicMKFaQkePJjDrctddSs3Ei5y/aEl8UJZVCkMh1BkGr0mj0Q6pEkKRjJ+vjt2HlUoWNJGF8q2Ei1xb5HGtA/FkkuETC7ZP4qSKI2KoCXlIxeyMcpEfQRrCcwibMsxWdyo0DSZyDM9lcaybLLZnYtm7gSBAdA1pWI3UDkoiuDqpfGla9c0lYcf62B6KkUmUyQW89HQFKoW4Fbxlsb09DS6rhOJRMjlcnznO9/hX//rf11x+yqJqYQGDdHihpyNHCw40ZZFFICCBe/6T4jUGLjDCJ8Gif9Y8XBSSudY3t1NKcg5E1FT+oRVSHvxxOoYfHWmhMBsBZm5EOe/9CQgsQW8eaSGmkSBvtYAMxEPpq7wxXPD/ON3HKD1TCvDr484i9fCBK7oCk0PNC5p04wn8hsspJVMJBdtDSqbTEopME0PMe+70JQQYkGl1pY5yquTScLBfupi19jXeAZFqDTVmPSM2GTzCvYCUVAV2FcXoDHsRUqJqmjYttPRVB+1qI/uTm1TuWstjPmXCMzSVjYYEyE62urJmCk8qpfhjOBWYgJLSqwFu4zF/295FALGghk648uWC2KJwqyFKhQ6XSFu5GbX3HGXqqxrnJhKOi3CK2EYFiNDcaan0hiGhVHcPEG4FwiFPYSjHno38F4C8AdcZNLL3XGWJbEsk3NvDCFthzzPTGXI5Qy69tVU9VyqeMtifHycT37yk1iWhW3bfOxjH+Mnf/InK25fJTHloIBociPcCrIoHV+kMhsp7gi4I86fdhqwkXkbOVF0hO9CKqLehdAFQgikd6Wk/y7ABszSkLptqtz54Uk6nm5i/ML6BbybO4VgOuzGn7eIJYvopkQ3bcbqfMxniiRyRcI+Fw8/+zD9P+hj5vYsmkej7ZE2Wk43Lx2nPuhmLlPOoXkhmoSFRKXc/Tp1IMfAuIt4WgUkocAIschtpBTMxg+TydYRDeVQlcASgQFQFA9Ohe/aeoXG2su41FYW62BUBZ44kaFnxM3YrI6uqDza3cDDXU4oXwjBvuBhbiWu7HIBqUQsnWH5XtiF8j3axaJFnbuJRp/jO/S14XNb7kJaD25T3fQ3WCA429hM72Acw7axF65CEwrvbO4imciQSq1NfymKWNMpk07mOffmCPaOpR93HzPTmRI37HJY5CIrCcxKLPslOf8fHozj97tpaAqW3b6KKn7U8cADD3DhwoVNb18lMashgKCK8DqTqBwrVJAstbGv/jkiUgd1BxCuVmS2GXnl+vL2CQs5bsAJH8LtRGB268lRSukEKQILXVMSMnNBhq8+Qde7nsAX820pP18JCmC6FA4PJFiUBvHmTern8lw9upyr94TcHPngkYrHeXRfLXcmU2s6k5YiJcJAFSaW7WK5wNZEUxVqQhZNNRlenxIEzdeJBPpQVRMpIRruJZ1pIuRb+9X2aO0kObeGcggB2GBaUac7aYEf6Boc6SxwtNOg1d9Bvbe2ZL+ctTd+WE7CZjGCtPrfq7eFoUyCrlAUKSWF9TT1t4GcXr5gdSl9tQBFEdTW+6nx+/jEgZO8OT3GSDZJWHdztq6ZJl+Q6W43169MrInGuNwq/sByi3A+b/Dm68M7/9sRzjjvJTHa6jXZtmR4aL5KYqqoYgFVErMaLgVaV2gs5NaZZeLfR9Z7EUmBrbjh9irCIwFDIocLiP1OaNypwZCQsMCQTrTGvTP6DDJvoQScj9QyVLwtP8+RA0edy4jndixi0DydQ6w4lCrBZdi0JwqEveu74Fq2zTevjHNpOL7OaARSumhueoFstpF4qgtFmPg8UyTSXbxyzcc7TqXwiCmioR4U4dx0IUBVTULBYYpWkNWLvCJcRL1vZz73PHIxGiMFyVQHU3NHyGTbeeb02idnAcTctWten8lPVLyntlyQDKqAzbspiDX/rnc1klyno20ok+Di3AT96Z0vGs9pVuVEnuK0XEugtt7P4aOOtURQd/GO5s6lzaSUSCmprfPT3BJibDS5dB9UVeGBUy0l6ZKBvrldIf+CrRXe3i8wynX/VVHFjymqJGY1CjbcyGF3uRExnbRWx4yyDwWbOvsGXlYsDAkbzmXgsBfhyyPzFULH88uTjszbyGtZx83ZoyBiO+ccu0hgbEtQSHvRA91L8ihDLw/tnFFdmeOoErrz4yALICq3bH7r6gSXhuOYGy4eColEJwe6v4llvYhlebjR81FAUCgq9I2q2IU5FO/aMJkQYMkUeWMYiYUQCh61GSFcaEqoxMn6bM2/4+V4HI/I0dnkpc3bxqzVh2U7JEcRKvtCh9CUtbVEla7AtODcLS/7W4pEg9bSAr2SsNxNScN0cRJErOwATGFzY36aYlkb8buHqUlGQhlak37kApNVhcKDD7YSjnjJ5w10XS0rX5/NFrl9Y4r5uRxCQE2dn2jMR1tHBEVRCIbcRGO+pcLVfK7IhXOj5HO7pWZ9/xIYRS0fIRKCqtt0FVWsQJXElIMN9BboGXmUMfsMtqoisJlSDvOQ+UeU9HbYIG/m4EF/xcOtFFqTN3NQXKhwaHHtnAGgdFJVUgqmepsYvHiWh//hMplIjCR3bM6uUE5LMBSH/KvgfXvZLQzL5uLQ/CYIjHOWZKaTq7d+Hq9nlmSqA1s6RMK24eZwgMba9YuUE4WXcG6wIIHEo3ZStMcBpyB0X/idNIQDfOShUmfnZhklb2WRgFfYCKsXim7QD8KKGpsadx05M7vGhXo+pWJagmjIcpI/O1wGJbGJtSnMDNmlui1CkqrNYOwSgVnErL9A3FskVNARAn7q2BEiXifS6POVRuJyOYPhwXlSiTzpdHEpdSQlzExlmJnKIIRTY1TXECCTLjAxlsKWktwOdhyVw/1YFCwEhCMeGptCCAVuXZ9eumdCgKapdHZvzYajiireyqiSmAqQCOatbuyFJ3CJQr11s+L2ImkhIyrErVKyIIAG3UkhFaQT6WnQEa0uWCj4vVtYpsLE7VZufu+kk+fXVA689wAX/vgCiZEkmlejrnuWo+94E38sxXRvI1e/dQZZRu/jbtB6oheMeEUSkze2urgKCsUohWK05FUJHNQ1zHwbtnwDRax33GWCkbf6lv4dcZ/iaM3PlD+rEHg1P+S+D7mvs8RChQrBfwSao8tR62lgvjBLynA0Yxb9HDVV8sjR7IYEZoNO8XVR06yTn4dMuoAQEqRADRQxAkVkfvc7VyxFMu8t8v66bmaG00yaSWrrA8RqfEvf6WQiz8VzI0sCdJXgkG/J5HiKyV0f+TIURSyktvbwpBtASvB6XTS1OMKEPp+b4aF58jmTSNRLW0dkx6wZqqjirYDqr6ECJFBkucWz1rpBs32+shqvJRH7/MgbaaczCZz1UwGKthN9sSQ0rXLC3gEomkrBej+Rzhz+Gh+1h+u4+oWrS8q5vuA4Bx57BVV3xlW/f4IDT13l9gsn2KluKVU3CDfFQZQX3ppJF/jWlbFNRmHWgyTmFkQ9s7yZjBIcfobOtueQ0ikrVRTTWdQrQlDjfR9urZaClcetespvZg5A7hs4nUyL9TNA6r8jw/8XcSPBTG6WN2eyTOWdstagBgfDkkhguUZnI2xUO1N2H1vwwq04qZyLU/tTSENF8ZgoHouZycpFvzsJVQiOumuZuppcihRMTqSIRL2cONWMEILbN6Z2THNlN6DpCqoiyO1aumrrUBRRYhkQCns4dqLpHo6oiirub1RJTAUUCGMIJ81QZ13nkPUN1EpO0xJkxIWiuxAPhLHTBYfIZC0YM2DSREY1p4i3eWcJDDjLVfejKXj7wwBc/+oNrBVRj+5HbywRmEXU7x/n9g+Pgb0DdsqKycGnrjGX7GJw+ikKxQFiMR8dXTHcHo1U3uB/vtBLwbz7Vl+PrvArp17msxePYUmNeHI/t3pDtDS+hq5lSGcaqK+9UXl/tR2X5oTjk8V56rwVFoj8K6xsxbakyqQ8yCydFOdeR0o4PyvImIvdQ5AyJRfnBA/XSlybvK1FCzxb+BVK6dTc9E0ohAMFpMdEDzjjnCuAk4DZRQKzQLqOhmrRb9usFP61LEl8PsfMVIbaen/Z9un7CcUyzuP3Gpqm0NBUwYusiiqqWIMqiakACw0FCxuFbuv5ygRGEdASQfG1g9UPSJSACgEVaWlIt0DUuxz5eyGWiiFX4+7UfA0wR8DtkJjkWGn9iz+aXrOH218gUJMiPR1m+4ueRNFNDr39JrI2wpXeJxcWNYOxXILJyRRnH23ntb7ZijL3NX6d2czmax+kbXBtWjBfcCb6oH+EfZ3fQBEWQkg87vU7cnz6/oWRS8Zzo8Q89agr6lwKVp6MkUK3BAHpqO6m7Fru8HYkyzo/KcPhqHJV95CUkvEcdJSW2ay9Drk9ApPMKFy448WyBZmcoGBDT1wwV1iyH9p5SMdZHAENaQ9nG1uwUjbj9lrnc8uSTE4kqa33rytm91bE3fo1BYJuHjjVjKZV3aSrqGKzqJKYCggwQ7v1CkPKo7hZO1kvQjz6/0aEopD4TVZXzgpVQINT6LhIUNYzZrsrFF4AtRE8j+Ov85OZziwNJz0XwFMooOom7sDy0/HRd53n9b94x8JfW0tBuMNujvzEIWKdKaTVxg9fdpUsWFKCZdoM9s8xOp/FqhCE2QqBAShYGt8dOI0mDEyp0dH6fVRlmWAqSuWna03EcOvLkRfDLnJ9/iKWNNHQ0VSdrJlGCAVkOxo1dMlXFghMaWglZ5W/WzZOdGa9KurFhc69iV+fQBDUw7QHuvnL14fpny4AElesgB4yODensPTZSQgWNBQpSHiMnQnISPAZKvUZL6GCjioVJgaSWJU+UEBRHU2kxuYQ42MJdlBr776BEOByaXQfiC24sgtmZzIM9s1VtEBQVCfladlyzddDURxjRvdWWG0VVVRRJTHrocN+mXr7OhY6GuUmJoGI7oPMF1hv0dozifDsl0Fro/PJDmZuzWAvpG8ufvVxhGKDFATqEpx4/5toboOpnmaWV7rNj1F1qzz0iQfxxXxALZl8ARhes52UMD+Xoz7kYWB2e8JwisgvdCUtkwjD1hHY6FoKXdvccQU+av3vX/N60XZInYVFwcwvjNshQkW89PLEQgSmFH6t/FO3giSkr/84vpWvw9HIKTyalzuJWeKBOIGA5dRaCZbMOJc+u4WISXs8QFY36YuluJvabWE7ztX75kJoKw5krpMWVBRB80JRavf+Gmam02vSNi6XStGw1vvJ3PeQEgoFkxtXp1AUQUNTkAOH6qhvCHDl4jhzK77vDU1B9h+sxTJtXG6NKxfHSMTzS6RfUQUNjcESgb8qqqhic6iSmA3gJb7OuxIpbYTRt842ewkDst8kWPM0+5+eY+g1nXzKh7QVpO0sQsnJCOf/+glqOicYudy95TMEm4Mc/+ljCwTGge5SK4bR4wWTodnttcrqegpNzZHL1695TygGdTVXnZV2E5Bsh0QpmHjLvhPQIeyCRFFiLxFAiapAY/ldyo9rA9E7t+qhNznPN0d6McXCmSrV2yiQ9JhkXSahoovjk1Hu1CTJuzZf+6EAb2tsR0rB2O15/FmtRIl3o5RJe2eUSNS5AT23p8vWnRTfYmJtti2ZGE+Ryxo8eKaVkw+1YNs2pmmj6+ryQ8wCR3ngwRYmJ1JMjCWXSF9t/ToSDVVUUUVFVEnM3SASRiT/E9jjZd+WcqEjSdtDszbzJqR6aT1cwM530fPSsVKjQKmQS/oZubwP51F4a2mk1HiK+cF5fCsEt9xujUjUy/xctmSBs6TkdrbI/DbrIprqzpPKNpLL17JaUEdKlVik1/EV2lSrsnAI54r6l0qu30EmaRZX8JBiXB5mikNrzg9wPCoZSAsmso5STMwF3SHJZkoaFsdccdwSAnoIIQQvTAxuyf9oxpcnVHShImjIeBh0VRBhXNb8W4INnJ+d5FcPnmK/HuHS+VGklNi2EzHQdZVCwSwbRfF4dDRdoZA3UVTB1MTaWqy3KqQtSSbypNMFAgE3iqLgcpX/IiiKoKk5RFNztYC3iiruFlUSs124XYjDKlijZd+WCMiAHMrBkUXLgb0gMzZQQAhoPT7E0LkDbpsvPAAAi0dJREFUFHMV2oi3Awk3//YWxaxB15OdSy8fO9HItSsTzM/lsBZk5fsMcwMCIzlW28vhmkFyhodzk4eZzDidQy49QTTcg983QSLRjb0inSGESdA/htuV2tyQJRSNEDOWRW0IhHBqNsp9HiHG6RYvoy5ozzRzg2m5v2xKSRHQHZR0b8PGptxXYZHYSBukLWgKdfLFvuvMF/NbOrahLLR4I1Dk2hPplkJbwk+ooCOBuKfISDiDpTifVdYskjaKhCNeHnm8k7GxBLmsQSTqpaExyJ2b00xOpNYU7RYKBj23Z+i5NYOq/ngV9YLz2WUzRQKByorVVVRRxc6iSmI2i1AH5OehmAR3FI4fRYhKrbwqwnUaW+sA/1+AzY63Va+MeFTkRkLScHCM4UuV0kbLaZDSvzdG3ytDeDojBMJegkE3mq4S7YjwnfEE2JKs3NipScHmgwd+iFs1sWzBqYbbfKP3ce6kQxzo/FsUxcSnFdjX8S0GR9+OYfoAQSTUR0fLDzY1TtvWKBp+bvd9EHDzrtP5JVn7cmgVF5cIDIBAEmSKJCvrh3YJ0rGMkKagOOX7/7P351GSXPd9J/q5NyIycs+sytrX3vcV3VgJAuAuUhRlkxqJsp4p2fTDkzxnhrZ8fCS/53k+fm/GpuyjN/IZacaP73AkmpZFUrRFUiS4iJtIgiSABtANoBto9N6175X7EhH3vj8iqyqzKmvpDUt3fHjyEBV5I+JGZHbeb/xWvpK7yOwNChiARM33W3hCsxhp7o4sFeybSWFogag7itoqIaKOwaud2aX4YEzpizY7bLJ9VYXYoe1t5HN+Bd6m6Tda4d7C9WHuFFpDLBYImICAN5JAxGyV/CgYFphh8KoIy4GWwb4mRP4ORB5FxkHbf4sQ07d9OkvCRasGCbJqjZWGou/AVfoOXiM/k+TaqT0UF1qZDW5gcRYg9mSgLczrr82AEMQTNoeP9/KlU9cpbpC10oxmW2oM2/AziwypMfD40M6f8tW5EIaxknGUTIxwaO9/xvUiGNJByq0VJ3PdLq6NPsBifhAQpOMeuoVlomkfHVq+HQuqjys8VM9Muv0idI34FCBNDaYmMlDEzGtutC+FADpLYTyhKFouC+FmoZEu28sCZmUfQcgzSNQsCrZDXzRBxGzd0qFYrPH8MyOodVLm71WkFKTbIkFwbkDAG0wgYraK9vwqY0t/XnkZdkQR1FYNlGDVq9ZqhTBDcAfjGMUma1y0vYCUEG3L07Vzkhf+6hFyU20b77QR/QlIhxFS+nVJtCafq/DSmUmU57A/c42YVebiwgCL1Y19/g/3n12zrez5FWdd18Y0V9LBhQDLLN/QVMWCIHy5A5kBJaHmukjDY/3IWLjA4wypFzFFlcs8vOHYW0FrlgvFacCQzaJGCc1U+QaFk4ZM0SYXqpGNOGTt2hrtlSmFmgTMEgKIuyahpMmHBnete4qLr89smF59r9DRGUMpzcJ8CcOQ9PYl2b4r6GkUEPBGE4iYm2XWhW0m2q2hr5ZhoW4d6MwgjmYQqgT5/w28mTdlelr5AmdpYZQSkB57HnuZU3/52E0fV/QkEMbqIFsoLJb5x8e/gGU4SKEoOmEWqwmkANXS+qH5yehRIlaN/oR/j87PDfG1C++kqjSD/T+hLeUH7t5IKNGyhcM1sK93sn28QN9MmTN720jEX6HmJbGNrnXjkzQmIxyrB/3eGQED/hx//FKMjpRLe9Kjp73ZujRZvokMZAGz8SqwTqVcTxMZqaC7zDWfoUBwtLOH/dvXvzcA2YUbE5J3I9LwU6q7um8iGCogIOC2EoiYm8Vz0LmH4fK3oNqwAE0von/6abhvD8Kb5vaZYUJonPV7NzVQnI8TbVGlFyDZtXFF201ZJ55Ea0XYqGGa/r0ouzYg6O+sMTptrapsCyC5luvhcy9/iI/s/hGZSI4vn38XrrLYMfQNUokR5IY9kFrNAYpeH1Jpos4i5us7MNsF0b2C++MFKu0LLJZfpC3yGCGjG2gdbK2Q3L724q1RGvIlg3xJ4ooqM0KQc/xENiHAz0y+vS6sbc9UYaYC7VG0EIj6Z7lULXr3YMemweeGIfG8uytF+kYQQhCP23R0blKSOSAg4A0hEDG3guOBu2qh1R6U52H6FLTdxkVItkP4MbzslxFSIw2NUgIh1loqQvEybs3EstfGjjjV1rEOW0UtVpCZyJrFrqg0DnL5C7Uvc42xfBeRkNpAdkkcJfnrC4+zM3UNTxnYoQXSyWs33N1Zayh5PYxXHydVXKD9BxbhQxHC95tgQBiN5gRlt4358ncxRIL26HsxRav6HAa3oxLbesHXWsN0AYyoSzhTYdHS+F5JgadvPMh6/QmAqepCxVUkpxRCgX55GrE9jW6L1HsZVOk/3I1lNVue/LgXsRwI7XkK54Y7kb+9MU1Be0ecQr6CaUp6epP09ic3DA4PCAh447hlEeN5HidPnqS/v5+vf/3rXLlyhY9//OPMzc1x4sQJPv/5zxMKhahWq3ziE5/g+eefJ5PJ8MUvfpFt27bdhku4zcQkxAy/8/TiRj/YAtwKeC1M97oCRoTbqhHVLCL8CIsjnRRHv0U0lSc/m6Rv/3VM28Uw/TgFzzG49PQBQvEKw8cuNTV+dGsGr3z7+C1NYzpfoSMTQWqNIQRK+zVSLrlV+ma3c1/PeQDu6z7P85P7uDaVZrMF2VUmufwONIKBnp9uaR6NgbFag8JisvoYYBFKdWDEK4QfMBENNXoEBhFrO2XnKghFofYKSfsEUrT6nG59kXIUXMrB3jRNmqjswet5iHRV6jFNzf2XbgfCg/1zaSzPtyiVa2WULGMooOahz88tj7USIXZ89CDjY1ly2QqGIVlcKFOoN3Bsa49w4GA3164t3lJvoLcjdtji4OGeN3saAQEB63DLq+x/+A//gf3795PL+f2Ffu/3fo9/+k//KR//+Mf57d/+bT772c/yO7/zO3z2s5+lra2Nixcv8oUvfIHf+73f44tf/OItX8BtQ4DYG4Fk/WlUA65Gny1BbdUvt5DQeRiRGkIbdrOQSRuIfTdQsnXLeOBcIrN7N6FED9d/NkJhpsDEuUH6D12jfXCGaiHC9dM7mRrvxNIKO1qlZ+8oypNIQ3Hm6w+wONZxS7PQ8RDPVWr0mgYJKSgpzYTn4bDkQvKxDI/HBl/kv55/9+bHBHqNEGNuhWRidMtWGD/TSFBVKaZq78AjjBSa7qgm8rDV0pgiMMhE31s/qwLkLTbfXB8pYKYqmJmC7ogmasBsFXKORBqCjWxUt4SG3fNJbG/FshIxw2jlB003IaB9exvP/vQ6juO1rO2yMF/m6R9fxXwjiza+gQjhN1/M56prtg8Mpt+cSQUEBGyJW3L8j46O8o1vfIN/9I/+EeD71r///e/zK7/yKwD85m/+Jl/5ylcA+OpXv8pv/uZvAvArv/IrfO9731uumPqWoNeCpIEwhP8yBYQEYndjoTgBMgTpHYhjn4Tek37K9dLTs/SF0HqF1G4NDfk/gcX/N4nYlzj4S3HMkEk5F+Pi04d49gvv4szXH2JhtJNC1AItee37x3j6T9/Hi195mB9/9v0sjnWy0ZO+MFzah6bqGTx65bx14r1xHn3/bkKewr60QPzUBB2np+icKCK0YDA5iVN/8n9mbD9fef3xNedb/YUTQEoKIlIRNhyE2Jq7QgjIu/1cLn2U65UPUVVtSBQdNrRHBDK9fqNN/yURwgTuxGflY0pot/2g2cmy5HJBknMkAm5ewGxhN0P5KdONSCkRw6nmmCYBpm2i+xLUau6mxenc1a7Tu4Se3iSHj/URi4UwDIFhSES9qm5PX4LsYpmL52e4dGGWYmGdoOmAgIA3hVuyxPyTf/JP+Hf/7t+Rz/uVU+fm5kin05imf9iBgQHGxvyKtmNjYwwODvonNU1SqRRzc3N0dDRbBj7zmc/wmc98BoCZmTcus0d0h9YUpBNSoOMG7HgPJLeDFUNEOhCJvpVB7/iX6Jf+D5i9DBnzDtdDU6DmQM2jndcwxEnQnWtGRRv61TgVG6eySQGuqIXYm2H/4Ld47alDqKYF0L+gvvt62f9L+3EqDkfPLyCqni9IPM3gZJF4yeEvQ+/haOcFYqEy3736YFOV3SW6pGRa+T2ANBCXgv0hCyEVxw78cI3RayMS5ihx88cU1SFM2U0mLElafoxQIpaiZGxe0fdOV1E+nony8oJktlxCCHC3UARwI4SGZMUiG1m/H5UnNTOxMn2F5ngf2ZtA2yaM57ClpG1bG9sf38apF8fvOTfREgNDKXbu7kRKwf0PD5HPValWXRJJG9s2Of/qNFMTK9WJR68vsmN3B4ND6Td34gEBAcAtWGK+/vWv09XVxYkTJ27nfHjyySc5deoUp06dorNz7QJ9x9hoLVs4h+h9ANl1pFnAACLSjjz5jxEPpRE7w29QawGNwGHvY2do9WjuZxhvJY+pTtRChE1ev/o4QrbeK1/vgzNxZhLLay7Cb2jIZGskRuDpsWN858rDLQUMQEW4PBiWHAxZ3GcYHDVNTOmh+n+Oy/UbCuj1sEmE38eedB87kpJUyLfQSCRDmW1k7LWNI99IBILB2AD3d/RhSol7q0pBQ08+QndxE3elgPloa4uBaI8gDnXjHOqi65EhIunIPdceYImBoRS793YtB+kKIUimwnR2xQmHLbKLlSYBA36zx8sXZv3+UQEBAW86N22Jefrpp/na177GU089RaVSIZfL8alPfYrFxUVc18U0TUZHR+nv7wegv7+fkZERBgYGcF2XbDZLJvMWKg4156B7Qstpp8tUFCxOw/gzMPBI6311CWEOgHed25HVslXsRBnD8vCc5o8xHzUZ64hy+NIisIlxSApEf8Jf/U1jlRVmBSsW4rWJHJdPj2O7a4udCWD7eBEBjHdHG7Y249hTKOs8yWcOQsW3SujeApX+l4GtLwxKS8peJ9n8PA/3bCfvZKm5k2Srp3BVlpH8MDPV+yl6YQ63aZJ3uJBql93LojOPo2qAoOJpKl6K03N5XlqYasg6unmEhphrLQfrrkfEMUhskoWmleaVM+N0dcfvSREjJLS1RTccMzO9tj/UEvOzRXr7U3diagEBATfATVti/u2//beMjo5y9epVvvCFL/Dud7+bP//zP+dd73oXX/7ylwH43Oc+xy//8i8D8JGPfITPfe5zAHz5y1/m3e9+9xtktdgaerQGVYWu93zRnka7Gn2xAtpDj67KmtEOVH4GC/8Kcv8reBO8kQIGlrogrxUd8ZJDJWJyZk+auVRo/VmZAna1I2L+Cq8jNqGEgxDNIsWT8E0bvvjcdaa0xlvnY5PAwHSxHtfSapCiP/YK5k92o2SB2vGXqD7+NE56lNAPHtjy7atUkhRLncRlhcHYZebL5wCNZXSQif4CYeshLhUfIetGcDdpM3A7GIjuIh7qYX/6GHtTh3F1N8/NSl5eyPPi/ORtETAACLBdSc52SJdD7JpNYjtGU/jSjvkEe2ZT9OU3XqDBz+qauoc6TTcSj9u0d7RKr19hvd8n/9/dW+e3KyDgXua214n5gz/4Az7+8Y/zL//lv+T48eN88pOfBOCTn/wkf//v/3127dpFe3s7X/jCF273qW8ND/SZkh/XkjDQFQUz7kodGNnwZOst+MJFF/AzXN4sNPf/6vd46akHKM6vlPgPO5qT52aZSYfJxyxiJYews7bcHK6GywvoRAhhmwjgwAfPcOE7eyksJnC1RGi4MJjAqdcQmeyI0D9TphSSXO+JkY+ZRCoeg1MlUkUH19YI1ov70FgXhlHti9Te8xO/G6GhoW8ar2bCfAoy6xfjc5wIl659kFKlAykEINm/rUSy28UP0PWvIWnvYDguuFpfn2cqELPgdvTg1AqqU1Gc+bAfax2t8HT4CsIQSCHojya4XFi89ROtOTGkyiGkFmitGcrGMbRgx0KC1zuyaDQdxTCJqoW8040q3yYkkjY7dmUo5KrMzZUolWoYhl/rZXA4vWmtl57eJGMj2TXWGK0h07mxAAoICHhjuC0i5oknnuCJJ54AYMeOHTz77LNrxoTDYf7yL//ydpzuzqGBWRc9u8qtIQzoux+dvQ6xTkTpS6DzvNGWl9UIAdG2Evd99Kf8+LMfgAarg6GgZ34LHZBdhR7JIna009FjEo/l2fbhl/nz5z6EqEmKUROvoUR9LWRweneaatj05ZsUVEMGuXiIPdeytBXLKL1euX4Db74T511fA6shC8n0fEFTd2VJESVsbKfk+haWJS5e/UVKlYx/nPrmV69GSUZLtCdXjmcIQX8UrhY0IBgvC7ojmojh9yi6WZSC8sUUXslavtfRbJRdRZvXOvwO0HdKwIRcSdQxOJ/Jsn82vSxUwp7Bwek2spEqyXIoEDB1pBQcOzGAaUraMzGGtrff8DHiCZvtO9u5cmne31CPRj9wuGdNYcCAgIA3h6Bi71YwI/Dyf0JLA5QHD4RvuKLsnUSaHuF4iUr+Jp8O5ysoFujbt4effu69XOs3yadtsFtfZDm6Kt5CCJQBlwcS7C+NAisBtaZZIhxapFpL4rhxKh0FYrEW/XekhngJU6bIRD+AwMAtz1HzpgFFpZKmUm1jdT8jT8Hl8RDtyeZjSuFbXjzt9256YU7zYKd/mpv97FTJwiubTWJRIgh5klQlRDayuhnobUJAzVJMpSscN7tQ0kU3GAANLWgvhdff/x5DSsF9DwximrfeOmJoWztdPQnmZ0sIKejojAUCJiDgLUQgYraCVwXl+C8A/L5AbyR6g8VXCI1T9dOUtVr64fatEFvCVYRKDvOXF/AcTdXZJCV7HRxLMrM7j5h10Vow3P8D2tMXUdpACo9coZ9EeNS3urS8EEjaJxGYCCFpizzGYvlpqt4EjhfHr5m/dqdKbe11uoplaw1AwgRL3ryAAfBKvoBRaJTQGFogEBhaEnPMOydigHd0DXKys5fcQoWXx8bx3mQr4FuNZMpGSkkyFWZgKI1t376ftnDYom8gCOINCHgrEoiYraBW1eRYcNFpsymTSSsNjkbYd6ZxoBArvXgaF2LPlcxe6UZ5RoOAgWXb9xaFTKwzhmn7xf4y2SozbTamp6lZErVFH4wWkC0MAQa9XadoS19CSg9Zb4KZSowg1m3q6Me4hIxOhF+LHylCtEffhdJV0naV1y+vTTESQGdqbVZT0fXjlj3txwL1RG9cdmoFzoKNV7SQtoeSHqOJIrORKlr4WqytEiJTClMztlakz/QE3YUI6UoIJWA2WmEmVln+uBI1P/uoZLlUGlxuB9IdGEKSbotgmgaeF6T4AliWZO/+bjq7g4aMAQH3IoGIuQn05SrikIE28VNyNH5m07UK7IvekcwFpQQTrw5QyUXZdvICWkmEoVgYzVAt2khD4a1Jj958Hrr+GpnKE075LolUweGBs3O+10TDZCbMlf44yM3EjKC/5zmy+SG6Mi9jyOaFtlHACEIsBUVrNGGjn4R9AqVdjFW9jKSwCRk2hlFFeSGWqtQIwAK6SxHcvEZGXIThn6PNhke6NJ72XUs32q9PuYLi+Ta0K0BJEIrriSLzkSpLJXCUAXPRGvPR2pbsIkLB3tkUppLLsSu9+ShRx2Q8WWL3XHK5YaPQgpxd40pbgaF4kkTIrt9DwbGT/bx8epxK2fU/v3ssRVoaguMnBohErcC1ExBwjxOImJvB0ejTRWgzISyh5PnNIu/k76k2uP7iLkoLCUbO7CTWnqdaCOPWLI7/8k+b4jSWEQrWKTq3PGTp5WmunZ9h29Eexl+YQCypG6BnroInJdf7N3/aDdsLxGPjm4wSdMb+Dp7O4akiltGOKRNo7VHz5hDCbGrK6CkYnbHwvBC7hv+GufH3UnNNMoak3zSQFUHpYgiEQuyeJx71Jy6Eb425GaoTMbQjl++rh/DFyurjia2Hd7eXQxgNAgbAQJCuhLBdieU1v5eohtjrpnn/8O6m40SjIR54eJhSyaFWdXnp9DjKu3eEjBCCeMIOOkkHBAQEIubGaWidPL/KpO+BnnMRHRsXGtvaaVKgi/5BjR6mrz9OKVvyT+OY5Kba6uMU575/FGF44Mom0SKlRm3Ny0G06jHaGyP++tya9wwN/bNlRrsjKNMApevKZ+0icn38nfR1P0ckvLDuuUzZhhAQkh1grLSdEMJAihAV9zoRcxitvXratGSg06Ej5RANPwB9BVTVpDISR1guQmrcgoXRViEc0bcl6NpdDDUJQ1cqhG6tFbdKohbCaGEd02iirolY9Z6BIFMKY0kDz1NUyi522MQ0JUIIYrEQsViIE/cPcvblCSrlzfsfvd0RAnbt6QgETEBAABCImJtDGqBaxCREJMIUW+6K3Gqcv82E8OMQfgJQIEzSO6pI+TOUWhUUqyWl+RQIz18CDQ8pFUJq2oemmb4wsKVLUgKqIYlbaR1rIZVmeLxAMRoi1HWNYkLgemHyhQEaTVCL2Z1kc9vZ0f8tUm3XgLVaR+kKYp06i8pzmS89B/o6mfhBIlbn8v7RsF4u7meEPaK7sr5HSoBA+5YnJLcl6HrVITarkrsVqoaHQq9Jg94oLVp5mksXZhm9vrgcF9Xbn2T33s7l7048YXPi/kHGRrNcvrhWhN4t2LbBvoM9tGc2L+QXEBBwbxCImBulRYVcwO94fSgKxtaqeW7cwduDyt9A+F1Qd6uEkzb7f3k/r371VRCgnNVixkCj6Nw+wdDxy3hVg5e+8RCGbeA1NIRsFeqrgdm0ze7BNLLkoWZKuIZgIWERKXvE6vv3zVUpd70KB16oX4Nkeu4QY5OPsFz8WQg0Bleuf5Cjyc8gzbWZSEqXqHlzhIyOporDWincq9sJ2YeJ9+wiYjUH8q6+r0LQoJ8Et03AAFamTG0qtmx6kQh68hHGE+WbrnM9G6vSWYo0+Z80eo0FphGlFaPXF5ssLBNjOUzTYMcuv21HIV/lhVOjb62u8A2EbINadYsmwTrp9gjlurvMsgwGh9sYHE4HlXIDAgKaCETMaoQBeqMfXOVXPVtNwkALjRSbr3Baa5ZsBmveo74M6wp+L6EV11TPoW4yO01qU18kHL0IWjB1oY8LPzmIWw0Bkvlr3VRyMfLTbUhLsv3x7Vz6/iV0vfLwektAsuQwcnme+WSILtfj8lCSgcki7dna8j4qmUMcP718ECEU2fy2llcigGK5h0SidXzMQuVHtIcfxzLa0SgEBtXxJF4xjKkHCN9Ur6Pbt8CFusp4RQuvEAJ891l7zWbGK6MVuEvi6QZO6RiKS+05hhfjWN7GZemWxI1TayEClWZ0ZJGu7hhzsyVGri3itehn9VZASsHuPR1cvjhHubz1jKq+/hTdPYk7OLOAgIC7gUDErEYv9f3ZIBVYWH7vpEbmXb+62s7Ni455wIKr6Nwos0IkWPPxaAer+seYiexyf6PuvaMkexZ45r+8C4BQzKVS6CSyK8nMzjTfrNSwj3fT9uocicKqOa9cEeGqYjoRohS1yCV84dQ3U8ZouA3urqt+tbgGDNn6mNr0kHL9RUvrKnPl72CIOFKEcWtlojNPovHALiFFct193wguFaBrKEe4ZlItmEx6ion69Qjt3wZ1E5qpGHI517mIpST7ppOY60SDb2SdAfBcxXM/H7nxCbzBKKU5+/JU0zYp/Q7rdkhSqbR+YEilguJ9AQEBm3Nnipq87VmnvooMQe8DEOtsvcuMgy5v8EQsB8A6AkQZcxRuC/O/f1YTor+0NpikdgZUqalBo2FowvEymaFpQNOz9zpHfvMpfpYpcno6y1SuwnVXcXZvO7PpjYvYmUsZLkJgKI1cHSQactaImM7MWaRefc0aw6gQjU5veD4ATxdw1Cxa1JZOTbqnt+XYZnfJnXWdZB3B6XnJzwuKF6kxYbgrMd3Sz7q+acOPAEcqyiFv3S5TdzNaax56ZJj7HhjCMNa6CLt64oQjtyE4PiAg4K4nsMSshzRg8DFwSpC9BiW//D0Tz4Kdbr2PAPIeRFppQwmRD0FoP2b20+yyS+Q8TdLwwzp82SQQIg3xj0HoyNpDeBNAtcVUFbH2PPOjHZihGnFZ5SO7fsp/OffB5TGu0lweSpBZLK8bVFuINqQ1S4FrCkLuyiJrjPThbb/e1PeoTRp0RS8zVdyJ0BosFylcdm//xpazhGLmIUzRA+1lQpkKMtx6YW+Oh7j9sRFLGklrCHuCgoI13sHbdVoBk8kyu2a3uljfQAXmtzhaQ6Xikm6LcPKhIS69PsvCfAnDkPQPphjaduN9jgICAm6eQryTHz/y22/Myf76b2/r4QIRswFiz0cgN4qeesF3My2trZV51nU5WestNApUFqrPgJombvgVWjztP4tLIcDYBql/AkBpvsT0uWm0gs59HcS74uTn4kQsAzPUbIJXnqS4EEcI6N41jik129MTRMwKZXfFLK9MhWivwfySRWblGgSC9mzNt9ZIAUJwpS/OrpH8sktJjvUgpjrxemeRhovWYFS66N/xA3pemKNQ64T9r5FMX2+yFm1EIvQAsdBOhDDQscKGwmfFEiNuOY16vQyy0QK8/EocpCbSV/a9h3dIO+yhjZp0MJVc5T5qJVjuDgGzmmg0xOFjfW/2NAICAt6mBCJmPcwwevEqPP+/g1rdE2cdF4AhIL1BnEvlm6BLTfsbTT0EroI3ycjzDhf/5hJaabTWXP3xVYYeGWTxWpRD7zaRprdcPFd5glrZZmEsw4H3voAd9y01Sktso9YkYjSCd/zqDyiMtTH1eh/lfIR4e5lYd4xI9BIn4mWeuvwIF6p9COkxk7HxDMHwRBG7pihFTK6OfAjpjdKefh20oC00D9LBOPk86fkUOpnzg0a2QNJ+B7HQ9uW/NxUwWqBcgbRuzQWjl9xmqz4qpTTzNcHOvUUmywJ9i72WNsKSkrBrotcEiWuE0HWrUOuTbzWF/62KEJAMYl4CAgJuA4GIaYUwwLDhuT9af4y0fF+DV3fvhAVik5YDWuc2fZ6ujv0RF7/zHlRDCwHlKq7+yK+58txfPsa+J16ifXgaNMxc6ebS0/s59kvP0ta/UiOk5pksVleyOwyh2Nt+jbDlEN42Tce2pXgVw4/L0Aoh4OPt36RcCfPtuSEm8u2UoxleTHfS9FXJ7WIxtxM0HOz/GWEtQGh0e3aTq1tC0hP/9XXvlTvpIdsksqGLthACrUFaN1/MTmtAafJfrGHtkoSPm35KvBTomqY6oliM1e+7sbntQygIuwamB/mIt3GXzgbjiikkPeE43Xaca7mFNW0DNsuUfjsLGIADh3uCYnUBAQG3hUDEtEJ7UN6saJiAzH6YPu3/uTcM9kY/zEtF8DY+arVYT+ld7/1ChDNff7BhjH/Al75+P49+8jt+J2vP4GfP3o9UILRCCcG2TsFHdv+8xRHrRfLq81pcSPPjHz6K0zHF0MGfUtMRzr7+6y0WVoFlFcgrxY0+U0tiGy7EIiUQLVKst5C9viFC1INyi5rqcx7uiCK0zwAJzgVFYV7BL2wxRkVDVzFMVyGCRDDuFFkQJdyorJerWXt9IUdgINmbyPD4tm14rub61YVVn7bg7eg66ulNMDWZX1eAWZYknrDZs7eLaPym8ucDAgIC1hCImJulfXfzQnWuArvC6GT9SV6I+kKtARONiabMZguUEfK24MNYGzPhuQbXnt9JOFFh5PQO5FyKB8QMpYhJ2NN84MPvQJZ6wR0BltxjIcBhSRCdGt/LNy++A9EB0AWv72X30DcY6Pkpo5OP+GfWEpAk49fZMfwtBMqvadK4eG0y/UiDC2nNlWmNDIs7am0w2gTelMab1JQmXeZ2mEweCeFtKEKbkRraKzYmEoWmvWLTO66pTmXJ9RpM7zHRDZk3g/k47dUwuuoiZ+Y587MsiZ44quZC6Nb/Gb6ZLqZkymbfwW56+5Ocfn5sjZA5cryPTEfsTZlbQEDA3U0gYm6W4hRi/6+iZ8/5LiVHo1+tV3M1LcQj/x3oMVALYPSRreUxvZeISu0H8a5DKFLdQvZwi/47SnLl2f1N20wNyZKLkAKvppGJ34Hqc1A7BZhgPwjFPwc8portfPvKw6jGTtUqzKWRD/PA/j/DsgqMTz1ApZpBCI/tQ99Z06V6qyRCB1tuXwrcvbOLsWD2gEl61gEPpveaTO63/HimG8BQErte8U4ikEjoiBLOO4QvFOm84FJql2gDonMK48EMuuygX57CU5oFDYtXF9ECxKEuROzWrBNvmotJwNHj/QghSLdFeeDhYa5cniOfrRKLh9i1p4NINLC8BAQE3BkCEXOzKBd6jsPEUd+lZLiQMcHQiHQYUf02oMDcDdVnSaL82iKAV1+sfa+Dv/gshVNYYZc9j7/E6397pL5dordUVa31GCE92gbnmX3tZ/QefwTCD/uvJZzzuJVTvDi5B69Fx2s7lCOtOpit9lGptgEQjUwj2HqF2JXUZUl79DE2Kk90Jxdjpf3Eq9ROg0tS0n7BZXLfDQgY7ccsC2D7Yrwpo8iTCmULIr1x9HQRAcTm6/coHkIIgbq8AA3dppdiYfTlBcTh7tt0lW8stm1iNhRtjMZCHDzcus5PQEBAwO3m7hcxXSbMujStuQI/iyjrcQNrcQMG9D2AEBKO/RYs/gno6yynKwtYdtm4rwL+si3rwSfa1XjnS7AvupwB07h29x8coW1whtnLg8xPPMDcxfwm81mvhojmkU98DzPkW2NYfAri/wCsvcsjavbf5cLYNEXXRjeJC8W2we/RlrzMlFAkuqa4r/OnADhOdMNqvK2Ymj1CPn+EdMTDTSrMbnlTgmVJEG1l16WxXl28zFZgogiLroC0pHhyg0yyVgjoKIbpLoSx9Mq+ntBMxyr056Jg1mNi6o0pkQKx0xd/5NfW+PG31962GUerg5IDAgIC3kjuehEjukPQYaEvV6Ci/QWm04KBEJwvQ+EGVYw0QXpw7TuoK99B7LChM1QPOr2BmIq06T/Zr9pFa0BDrRAj2vcuikUBtBAxAiJpQSQxhVs1yU2lWW3hiCRL2PHKyjm0A/nPQvpf4WIzURphvjpLLv4A2QWjHt/iH6Mz8zLp5BWk9FZCiOvHCYVKW75OraFcSdMRO8KRbfhul3Vu+VYW8RtZ55e6Pl/KwkxVYLgGmZJNm2tQsBxmY1VaGJ82ZD5Sob1sI13t98rSgploGU9oQkpSy5Qp7DXpKtjUPIHoTSDs+j8zKZosMcvIOxsDdCeJ3qIbLCAgIOBWuOtFDI5GtJmI43G/Poisp+t6erkp4pZo3wthwLkMM0vmAKDTurmsGUus61W5emo3V184yP2fHCKzs8TUK9N4tVU9ZjQor8yRX3yWaiHCc198J55roJWBEAphKPY8cabFoq9RtdO8VopQU1U0mqgNB7e7lCs1sgUDR1l0ZV656ZiX1cQjVbqXsr21QMs3zurgaci7EK5Z7JxP+n2PEBhKMBer3nDRf8+A8x1ZIo6B5UlKIZeIazA8G8ORivPRCvqgzft2H+WFH63qLN0Vg8lCc8yTqG9/GyIEDG9ve7OnERAQcA9z14sYPVGDpOEHROQVWAIVFYiy9i0zW2Hff4fc8X5Y/H+inm1oeHiD3oim/WZdyKxN5/Ucg8XxDOFUmHinJpqeJbO9xvQFY40FQ2hFdqKdVO88D/7GDxk5vYPsRDuRdAHr0OtketZaTDSKijOHo7qa+vaYBjx4oMT8zGu8NtONaZRv8uJWzVFALLSvYcOdD0LVGsZLMF4WKAUVD/YvxjH0ynktZdx01yKhNDu/nafaZmGlooQMC50t4M4X4ENRTnb0kgyF6e6JMzVZWAlYHk6jKy5kK3UzEZC0EcOpW7/o28iSBWtlg19Zt1Z18TzlN3DUsH1nO+2Zt6cACwgIuDu460UMWQ99tgQlteLtMcWqZoLrIWDnLyB3/gKoElqVoNE44QI1DeEW2UIbxTgo6iYCD50wEPXAUs+R5GdSzI90sP/9L8Dil0EZHHiXy9CRBKf/+iHcSghpuBz8wAtkhqfRnkAIRShSZefDr1ItRHj1RwdZxGUHaz9g5cDlqwZqaK1PRwiBlSqxI/rtLdybrSEwiTbE4LwRnFsUzFdBLbXTVBDyms1eYc8g7BmUhXfDZVmEBqMG0SkHprLLYshI25yY78DMK16dnWJguI1ioUo+X29uKQVifye67EDZhYiJeIs1OjRNwd4D3Tg1D9s20UAsFiIaC6G1plio4TgeiYTdFNAbEBAQcDsYGRnhE5/4BFNTUwghePLJJ/nUpz617vi7X8QAFOsL9tJqU9usJKoBbbsQD/3zFSEibIQw0QbQ4NnRVyqwJ+JbGORSptEW3CUxA/1aGd1p43VEqbkRxs8NMvrSdiLJEuHIPOD6AbQSEp1ZDr3/eU5/7WG0Fpz9zn0c/uCp5cq7niN46amTzF/vRktF5aTDtKvptUIIfOuRWzOYutDP4myccK9GrOrzpNB4qrjJzbwxNIps9VnaIo/c1uOuR8GhScAAqHU+ix3zCS6156iY6oaETMvPti2MsSdDraao1RSlosPMVJ5Yi8JuImLBmyheIlGLcslp+Z5S0NW9UunZcTxmpwvMzRVpb48ST2zcCT0gICDgVjBNkz/8wz/kvvvuI5/Pc+LECd73vvdx4MCB1uPf4Pm9PdAeLF6CWgHs+g+6MCD8OAx/Cy43uGkWfUuP6A+h2+q3c9MFUULNADvO5TPbuD62v2mnci7GmW88wLaTr7P9/ov+HoamrX8OK1zFqdhoBWe/fYJ3/qNvIQ2NNBUDh68xN9KJ6pyDiMPTU+8gXk5xQr4EGiZeHWL2ajci7BF+wGwSW0qDqxQVd/w2F4xVVNyreOoohrwzrgc/Pd1vpnm1AGFTU3Jh6UKU1ORth0TVQtLoUpLsmU0yE6syGS+vDfJtLoq8/J/bjQTSqqCcFWuW2NEGRvMBPE+Ty66TkfQGIaWgtz9JeyaKU/NIpSNEYyF+9pMrVMprY54SyRWRsjBf4qXT4/X7oLmMf6zdezvftoHIAQEBb216e3vp7fXLNCQSCfbv38/Y2FggYm4YYYBTQBsWCANhWBD5IHJYo7xv+0EXSw+zEogaoEGPuYjBjZ6yJcLKIB7+F1D8IsnYKIxBc5q0QLkmV5/bS9+BEeyYvxBqLZCWCxW7/jdkJ9to659HCLDjZXQmh/fECywsvJORyUOE8jXkhQhmQyqsrkDhKzUiH7CQKf+cOQeuZnN02htVcbk5BCE8XcbgzoiYdKiNVKidC7kr7E8rtAalNeezMF/zr+9ausCuuSR23a0ktGAxXCNn17BdA6l9EbT8EWiwXIlnaNAaJcCUkoQV4r54P6NdZcrTBV/IWBLxFnWtDG9vY3h7+xrRsWdvF6+8NIFq+F5IKdi1pxMApRQvn5lANWVTaSbGc3R0xoJYmICAgJtiZmaGkydPLv/95JNP8uSTT7Yce/XqVV588UUefPDBdY8XiJh1EegXPwP5Uf+/Ow8hjvwDRPTDyIMfhANlVG0CKt9CeGNQBT3Tiej/ABh/DWqq5TGx9kPs434jIOdlCvOD+AJmrXQQUjE/0knvvlG0hqvP76Sajy6/rzyJqNf791zJwnQ3R//BPkzxAP/f7+dxPIUKGcgW8T/enKb6nAtPhDg/B7bxHJ32BcRNh7u2xjYHSYcfQdzBr9pi1mQmdBnL0H4tnvp6faBN8/wslD2BJzXnO7JEHYOQZ1CyXGrmBun1Aj6x7zC1osu1Sg7H0sSxWHg1zyVvBm8oCXELM1dFGxIl3nodj6yQwcRYjquX54lGQ+zc00EqHWF+rojWcOhoLyPXFimXasQTNtt3ZpbdRQvz5ZadKJWnmRjLBSImICDgpujs7OTUqVObjisUCnzsYx/jj/7oj0gmk+uOC0RMK4Thu5Ry11e2zbyC/vm/g8f+XwhhgIgjw7shvHtlt776f6j9UP4rv8Q/Hpg7IPZrIFMgGm+5H6Oynu1Da4EZclBKMHJmO1dP7aFxqdRKkptOkejK4lYtuo98nHA0w0KxhiYHgGdKJjIReubKGI1rkgH2Ccl0foKwe5lk5DpS3FTlv3UxRZJ0+FGkuHNWirGCZK46y4FwXcA0IIDeqOZyvv6XgFLIo4TX4kjN7Ey0kYpEIAKdJNBa89MfX8FzGlojZKKoTHT5XG81nJq3bCwsFmu8fHocYLmDtNawZ38XvX1rfyB8/bLU+2v1e0GBu4CAgDuH4zh87GMf4zd+4zf46Ec/uuHYQMSsx+of6qXO1vOvQ2aTbBtpQOxX/NdG2A8ijfVrsWglKGWjLPzkACMvbWdt0Ibg6nN7SQ11Eh/6MEbIT9WN2WbT9K8MxKmFJP3TZUxPIYHuj0e5bvwlynVJJ50bKiK3MQYCi6i1i4R9hNu5vGtdt1nVD1l2TC4WPDrWiTWVAsIGLC3GYVGjolcPXtu5ciCa4IMDu5pG5XJVPPf2irw3muUKxg0uotdfnSbdFiGyKtC4rT3SlIK/hDQE3b3rPxUFBAQE3Apaaz75yU+yf/9+fvd3f3fT8bc7/OEuQYFeKy6U8lCvfxV16Vvo6matAOo4F6HwnyH/f0L1RdANC2Hk/QwcLSLN1eeq99RRBhd/cpiRMztbCJj64SshUjs/vixgAEKm5L7hNqRXP5cQjHXHOHUwQy5mgYZ5+/soXcEwbqeAAfCwzT4S9lGEuLnWAq1wFVyYk8yWoeSC68Gzcx4gyDutK/l6ChbqcbUGim32eMtj70tpDqU1h1LwcJdiZzLHpfxZyq4fwF2reVy/Mt+0+N8taK2Zmlz7XTYMyf6D3Ugplu+tNASZjhgdnYErKSAg4M7w9NNP8/nPf57vf//7HDt2jGPHjvHUU0+tOz6wxLREt7SkC+0i5s+jFi8hL3wNHvxniLad/iOucwYqPwFdg9BxCL8Dyt+Dyg9Y7qPkvAbVZyHxf/VjYkSI/gffi+f+hMlzJsXFBGiBYZm4la1Vy42ki1D5MUSeaNr+gYO9zLw8xbUIKCmIyCx9aoKYZWAPpMg5k2sv8LZgEJKZ25q9MlaCyzk/BXwiJ5FozIY2D1UlmCxpeiIrCUKegqqCqUq9Bg8GNb024FoCtgHpEKxUE9SU3ALns69wIHGMZ386guO8va0w66E1KK/1tXV1J0imwkxN5HFdRaYjRiodDjKTAgIC7hiPPvroDbmsAxHTiqUQjgYd0Zg7JJULuH7g77s+jSj9FVR/zrJYKU/4f6u55oNQA/cSOK/6TRgLf4ZwzjN81GDoiEe10kex/HEgxN9+7VUu9cUoRUwsVzE4WaJ3ttzknJGmy55Hz+Lma0j7MaRcsdZIKfjIo9v53ldepviOU6TTl9FaUj2iqeXjSFfdkU9fIIlYw7fteI6CyznRXPcFQW1V48GLeUHOgf6oxpAwU4bRkkDVq/SaOFRbiBiNn1jWCq0Vp05fxnHemplHALv3dzI1niefq7SKw90UKQUdnfF13w+HLYa3t9/CDAMCAgLuHIGIacWgjWg30deq6HkPlG4d2VHNQeECOD+lWaw4oGZpHQ9Sg9rLdTFz3h+L79IJR8YIp37EmfOPcnZHClUP/nAsg6t9cRxDsH0uh5SKaFuRHQ+9RmZohmoxzJn/dJrjf/8YsqFWSag7zuz91+hJ+40cl6r0qWQW9O15mg4Z/dS8CUBjyQyp8ENIGb4tx9Zak6vV3RlrFujV8xdMV2C60rqbd0RWGat1Nm2VaLojEDKWzgdeycQrWAhTYaVrVHO35VLuGNcvz3PfA4MIBC+dHqNQrw68Gts2iSdCLMyXl9OqpRR09/rWlq1TAV4FpvA/gwFgL8FPSUBAwJtB8MuzmhCIbss3me8MQ8SBkVUFy5YWVctFZ59DRA2aRQz4gqFVHIsEGYXqz1gpNLOEC7XnOF06iVq1FitDMNYd5X2Hf8rg/pWsKeUJpi/2kBvPMfXKFJn9nVy/usDcbJGq0vTGF/2qv40zkBqtNKLRvHRTmLSFH0EIC1AIcZu+TnXBYjgWVUf59VtukYIb9691qRO30AzGNf31jHWnYFC+kgS3rmiEpjLCbRN7d4pq1ePVV6Y4fnKAYycGOPfKJAtzJYQQGKZk775OMp3URWyS7GKVyfEcGujuSZBui6xzZA+YAEpACujCd7f9BKiyoiqvAQvAO3hr5mgFBATczQQippG0hLQF4zV03EDHJHrGaf5pbrQKVBW8/H30kRRizVog6pVkV283wH4QKn+7ziQ85kqV1pGqAuzeHJ4rMUyF6xi4FYsrz+5FOYqJs1NcXizjON6yayFeeTc1FcFte37NsW4FKSJ0xj5cr/8ibp+AAag3IJxyPS4UG5THzaIFneUwA6UYHdsT7Bls53L+PCW3iBCC0qhNbXqp/o5Y3qf+H7d27jeA7GIZ11VYlsHR4/04jofnZbHtKwhxHl98SECQSh8glc7iCxQDGAJ20iy4i8DT+ELGq4+LAsP4wrvxniggjy9kArdTQEDAG0sgYpaICygoyFX9BCIJGGKtsWT1mqZAj5cQO5tN8lpL9IUKbDcbC/GivUPI4l8CHqsNIX6l2RhtoQny1Z61c9SC83/9IAP7LhNNF1kYyzD52iCe43+MtYTdJGAAhLYIzT+Mm3oJ5MrF3GpsZszaj8D0a+bcAYSApH3LpiL/WBraSjbahblLBSptafalD1PxyuRyJc7NzG+09y2f/42gMRDOsmaxrOdpbnu+VBvnJZqV+AV8AfJAw9jTLMd3Le9bAK5Cyxo7GsgRiJiAgIA3mkDELFFYpU4U9VgYyfIPvqR5XViiqECkQZeod4JEX3FhroKeB5KGv2/OQxx6Edx66fvG07kCpSQv/NVx0mWT0R0K1RCoawB7bZMDH36Qc38Vp1ZsVlfSkpCy0dVWi4xCVjtRkdYpxjeGP+uQ2XXHBMwS7q0YQTTL7rKeQoSo63/VldJcv7bAoSO9GCrE9PXFmwqIfSMRomXx3GVicRtrue2BBs7Q+otKw5glFDANfBdoA7YDi+vsU8b/Jq7+jgm4Qy0lAgICAjYiEDGbosBOg1ME3brzL1EDkv8j6CpQA9kHk7/jv6eBbP1HPybB1qxeYLSCwlyCl77xINVihCSK/VcWuTIQp2RbREyHR3YP8o5dHQghOPobxzj9+ReX+9poTzH8yBDFZJjyzNou1MrMo0Izt+VubE/9X6ipKp42ttatewssWREaj+UpGLuFhtqWJ+gohWmr2Nhes9iqlB2mJvO8dnbqTa4+26LD5CqEgPaOGAvzpVV9jCASqRGJKnbu6W3YWqS1tWQzKvguplbtMpYw6nNuPL4AwkDHTZwzICAg4Na450WMGA6hEwacLa8f/pAcRBz4NfTZ/wjzo80aRIIYHACjHT3zCvrcn0NxeuX9uISUCY4Gr7V7REiolcNUiyuBNW05l7ZzCwjTpXvXLDu7Dy4v8sneBI/+s0eZvzSPW3Vp29aGnbCZnysyP1dqauqnrDkqg38Bcmt1ZxppjukJ0RX7MBWlgdBtLZCnNZQ8TcTwOzcJAVMVmChvdpL6/WwRfKQkdBUjTV2rqR87mbR57exU0316cxAIoRqsLGuvN2SbHDrSw+xMkYmxHBpNPC7p7b9AJFKsFxQcAXbgZwnBzYmYJRStrS0S6MePiznDirWmC7i9lZkDAgICtsq9LWKiQE8IKQXKFlBpvajpudd49bsLZK+9h6NHnyJSnvF/66MGYmcHout30DNn0af+GFRD3MnesC9gltxQ68Soeo7B/EirJ1mBdi0mz/cwP/IcD/33D2LVy8NLQ9Kxx99Ha82Vn1zl2o+vodvCsC2NMCRaKKqdPwBxcwLG9UJUKm0k40U6ou9Dro1evkV8ETI3H+K1mkckpAkbkHegtjo9a519gbq/peEtAZ7UTMXKdJUiGA0ZRqYpscMbdRl/Y5FSc/jYKPl8mCsXM2gt0FoihG+Z8qvmSrq6E3R1J+p7PYtvcVGsKOor+C6dy7dhVh5g42fcNQb27gEs/Ewkv1pyUPQ7ICDgzeTeFjE29WBbDbtteLnScphTNZh8aRI0/Pxv30+so8je94aIbxtgtGqSXbyAdItkeg/RPX4GqRV0mpAyEUZ9AW3waDQaDpQncCoW4+c2KBCnBW7VZeyFcba9Y+24kWdGuPqjqyhHwVQRpouonhrVJ74PVuGmbo1ScOHKR9Da4LGjWUzjzsQ8KEdQmAtjREvoksmC6aHkViwka0VOTyFMxVDkwg5aaOZSVYYzaUILAsfxSKXCJJI2xULtLWCFWUFKxdDwAgODCxSLIabGu0H00T+QJhJdLbhqwCxrY148YKnu0K1iAe8GJvFTrJP4FpfGe/7WLQAYEBBw73Bvi5gs6FOFlZIuUQFl3fRUr7TJ6PSe5W12vExmcILsqGKmQ5PXfjM8zwoz1XuEUrqTXdZPILx+3yDlCUqLccyQy8zlHq4+twevtrF1QLmK7JXz8FAcV8SoegUiZhtSGFz98TVfwCyhwd12BsybEzAAUkI8Nsae/gSm6ELVBMLUiC08eGutqHl+vIlltGO0LH6nCUlFfiFCqmzRVmpDCY3UgulYmYlE2R/WsnbdKveRhmTVorfgCy2d1WgTTp4YolpymPdKFAs1ZqaLzMwUmtpXvflokim/DpGUkEjUSOwdA+JAZ4vxDuu7bmpsHNC7FQz84F4D330UEBAQ8Nbl3hYxjdZ4D1/AhATUBBg2KJfZxSGuTxwEoGvXGAfe9yIA0lAoLjAvhriuTwICbZjkY71UZBsRsX6pV8PUvPrd4+RnUqxekKQpUC3ScoT0iKaucGbi64w5GiEMBJK9bR/BKa1duJRdvaUwBSFgqO9ZQtYDSKMLDL2lLB7HW2S+/F10vYGmRpEIHSFuH2oYpVmownQF5h2HeKJETzG6HIDbWYxQNRSLkeqaon/Lk6sjtUAqGMytWIoEAunB+bNTVMpOc+PGt4QBZsUV1p4ptwjpUfhWlQF8c2EjUVrHrIAfYFu6yTmZ9fMOALtv8hgBAQEBbyyBQ7sRjR+AG26H47+NeOLfMFJ8HxqJEXI48N7TGKbCMBVCgCE8OrjCYfHX9IszGFQRQlMmvemp9r/3NEbIRRr+Yi8tiZ2wefh/fJhEbwIhV4kbQ1PYfZExx0Xh4ekarq7w2sJXMA6szTwyxnpvw4LtUXDOLP+1Waqv1pr58vdQuoLGReMCinztZaru5PK48ZLg3KJksmxRMzTz0RrnO7JUDX9hNhD0FSKEvHW+nhoGrDgH05081NbHobk2Qg0ZSFIKkqkw5ZLzhneeNkxBV3ecTEcUKddTkSvbM50FZMvLFPhuo9WM01zDpZGbFTAx4GHgfcBhbkn9BgQEBLyB3NuWmFYooLIACxch2sG2R7fxypfPkBmcQbcwCwgBISp06Qu0iRFe0+8mRCs3ju8u0pigPeIdVR75xE8Yv/hhSgsJkgMpeo50Y4ZMjv/9Y5z76qvMXfCznKLpAnvefZqf22VC5hCWzODqPBXnGp6uoe47j3y9G6dtGvfwa+hEAS1vJUMFwqqHtDqOSRyjHKFmV0DqDbOSHG8G3TIN3aNYex3b7CYkPa4WLNzGcv7CL/03GS8znPWbERpKUrFau0YE0DFuk0mFOXysj1y8wsXzMxTyVSzLYHBbG5PjOTxXgeOBKRHGG6PXpRAcONyDEILR64tcujCD2sDD47kCpWghZDR+YG0e37Uk8IvSvXgHZl3Bby0QEBAQ8PYiEDGt0B5c+ib68rfI9D/Mjv5p8qKrXj2tNVIoTF2lm9eJMY8uelBSEJEQjyGiv8ToK91c+JsRUt2LgAfmMId/5RjWquBNK2Jx9ONH8Gb+DcpZwAo7FJRJxvtFpIwihYXSDkn7OLOlb1Nmmm0fr3BF/QgMtVKQ9SYfqKUOk1T7iekdSAx0GaxqmGJyETYIulXaXfekXl3cTJYkTqtFXUAh5I/RaHJ2a2uDUNBZCiNcWFwoMz6aZWAozckHh1bmoTTXfz6CvjC3bI3S3THEtvQ6cUoaITRaC0K2S61689lLSmkKhRqJhE3fQIqp2QVyC44/D12/0AYmxtP09udYazZTwNn6eBM4gd948U6wfhfrgICAgLcygYhZD+3668rYzxnsUSj1OsYmGTqG8OjyLqLPl6DQYAlJJJjv2s6F77yOcjQLI/5TrzDyvPSlFznxWw+0Pl78EYzyNwCY5xCGjC9XyZXCQmtFOvwIc6VvcUV+rdk5eLMCRkQImZ0smq+R4zJtkXcilMQuxglVItSi67ssLKMT3TKw1CDvDnN+UtYjOVpPzvIkGo0SmvFkw3nq67tQ0FOM0F2vp6OUZuTaAvlcBStk0NObZHIiz8gLY6gL89CYgTRV9HXd9rZVZ9VIQ3Pg0DjJVIVyKcSLp3xBZFkuvf1Z4okK+VyEibEUrrtxVo7WYNTdSFIKDhzp4pXxc7hFA69q4MxGmppKlooRZmcG6e4ZheXq0EvfncaArWe4MwE9Eth3B44bEBAQcOcJRMxmqBogkGj06yXYU6+VIlnzVK+1hqsK8l7zepObQ039F5RzX/N4D3KjWSrj/4lwugvUIlh70MZ+WLwCugPsQwj3Zeb00Joy/0JILNmG/zHeeC2YJZbK2MStY8Tt/eh6DRClqzgqi2WkqCRyhAuJDY9jSIukfZJc9VT9iBqlTaoqzVhlB2oDZSU19FbCEPMwEh4PWHEmvCrFxRqGF6ajGkG02L9ScZmcyAMwWm8hoK5nmwUM+H9PFdHD6Xq8kUZKTTjiUClbvPpKL0oJIlHfAhSNVbnv/utIqTEMTUdnkeFtc5x6dphKOUQyZVMo1NZU0Q2Hzaa06IgZI52KkY/mUFohLUV1Mooffizo6onT2bUTX0jMAjPAKGsFy625B1uTBPbTOgsqICAg4K1PIGIA4n1Q2KCvkJ0CtwSLNfSZImJvBKJybdl9Bczk1+6vXdrs14HjrLZCtA3MYIdegIovJXT1FJQc9Gtu/UFcMHbwYZyQxlhXA9z8E3pEQEwK8vQTs/fWs558sSQw0A11Rxy7vOnxotZuLJlhrnyBvFuh4A6T94bZrK7IbiPMrg6rfj/9sQklmTxVQPVGEG2bm5aWg45r6yz4SqMvzsPudjq783R0FHj9tR6UWjFhlYp+NtCe/VOY9QBuAMPwXU67905z9qVB9h3o5vq1RaYn/c9bCDAMyeFjfWvE7c7kPiZKo8xWpoj0lWnfponTSyTUhbnc88gExti47P/t4j6g7w04T0BAQMCdJRAxsV6QoY3H1PIQTkN5zu9WHWmuAaO1BlejL7YulgcghEJIsapGiWb/e87Ua6+o+jgXHQY6FYzV0MCr5Z9giePEQvuQYuUj8+uxzHAzT+meNsg7O8nYNjF5EW3uQ4rmWBAhJEsByQjwzI2tPVUPzi0Icm4H0NHYOhPwLT67jQhD0sZAMKNqnPPKlFGMa4cBbCztu5QMD+afWcDLuYiBGwzKjYcgW2393lwJnQgxKxLMTidY69ryrTTxeHVNELOU0N5eYtfeTmJxm/0Huxne1kZ2sUwoZNKWaZ2RJIWkPzZIf2we39Ii8cVKHHgQ/x6f4Y0RMBaBgAkICLhbuMdFjEA88Cm/XcBGaA8qixA2IG6sSX8GYNHzX+ucR6d3Y0Ys3HIFrfyn73hHFiu8NoBVGAI6TPRYDceAmoRa7WVssxtTpuvOLQ+tHRYrP72xSwYcFeZq+e+ikczUBJbcxcmOdRb9pUVe0xRzo+uBqkvF7zwFz8wIdIMoWNJr+0SYUV1lvxEjIy3MujroliHapcUPnSx57fE9J0tKGFgu9H0jj3AAS0JiE5G5esbDafQr02tdSkvXMVGA3o1dY7WaiWW1+GykQf/ASiZPNBYiGtvK/C7hW1oaG4Bmge9sYd/bSSBgAgIC7h7ubRETSkE1D4l+yI9uPFZ7EDJbem6EEOjwOu4OaYG0MI5+ggePZrj+gy8xdy2OHauw/eR5pLGOK6i+zpmeLyMUHnOlb2PKNGgDhIfrLeIria1H8VpyJ5eK96OXUr4F1HSCqbKiP+a1iLsRLTKdNO12jcFoibIyGCtEeXkBhDbQYq3b6LyukELSIS2MxkJ1QmBqGJQhLitfROUcl/6zLkIJsATiQOey1SueqLB95yyuKzl/rtkN1DTneAi9NwOvtqqzgq+4NkSQXQgTDjsYRrMtSYjBppFKKbKLFYQUpFLhFtlPWfwso/lNznknWGrWtdTUMcxKk8iAgICAtz/3toipLaKf+ffQedg3KWxWj74iwFjtJKn3XsqussIIAzr2QftexOBjCDuBDez+hUfYnf+PUC8E1wrtafSUH4sigaFFuNZuoPFw1aI/SGmkpnVF23UxKOmHWwbYTlYS9Mf0cpxPU7xPw/CUVaEzUsU2NJahsXCJpfLMXJljNLR+pde4NFFojFXnNoSgz7RYFIqkFeNoooPpa9OU93qQsrHMKnZoFssOceS+SaTUXLzQgdrkwkU6jLYN38e1mnSrNgjNXHy9i1i85ruVpKzXcUnTmMkzO5OjWDhLV08WgFzWJJF0kNID2vGr7U5seq47xyP4dWaW+h/1ENS3DAgIuJu4t0UMgFeDqTMQSkC1Vb2OBmQMwo+jqz9G1ANetcYPGJ1cVeRNSMSRf4AIr0rptXZA6p9B+YegJqiW0kj9GkK4CKmRUsGCB9P14xk2+4zjZEN5FmoXG44vfAGzlV4A/uSJWNtw1glrsSRoBEuesvXqqWQdm7wTQiMIGx67kgVChqKzO4ZY8GXK2r38ebbKLvK0Znt7Nw90rmTILPaWqWZL7B76W3oyr6G1xEgfQRhteJ5gfKSNzXLIhRDonW3w2tyKW0kAhkQMbV7YzfMMXnhuiESyyrbtITq6emgsCFep1AjZz9LWXl221oTDboNR7M2wvDQyALTVXwEBAQF3JzctYkZGRvjEJz7B1NQUQgiefPJJPvWpTzE/P8+v/dqvcfXqVbZt28aXvvQl2tra0FrzqU99iqeeeopoNMqf/dmfcd99921+ojcEDUNPwPx5mL9QbzAIIP16MUKCMBGHfwsRPQRmD1R+CKqIU0gjzr2G0ZBqq5TAsfqJNAgYrTQTZyYYe34c5Sl6Dj9MtD3CK//tLNoboH1oGjtWJTedYfdDPaS7nwXA6XkHlxZ6yeb+o+8NuCkMLJkmaZ/ANuFaYa1UKzlbKS3jj1iy5JQ9g9dzCQ6ms+hwBI1c25yxvteQEaagPRIYyy6lJWtPW1vzQts3kKIW/wHdbeeZmN3PtcmT1Jwo0ViNvoEFxAZFB5fQWiNSYTjSjZ7IQ9mFlI3oiSOsJWvaZlcsKBYihOwBVt/87OIImY4KRoNmuwGv3h0kA+wkSJsOCAi4F7hpEWOaJn/4h3/IfffdRz6f58SJE7zvfe/jz/7sz3jPe97D7//+7/PpT3+aT3/60/zBH/wB3/zmN7lw4QIXLlzgmWee4Xd+53d45plnbue13Dzag9mzEErCoU9AzzEEGn31BzD3GsS6ENvfh0jUu/raD/gv4PRfPksGwVDvOZSSSKEolNO8euEhHn7fikvmlf92ltnXZ5e7TV+evYzW2ndFIZm72uNPBc2rr8/Q8f5hDGEz9ZpBrZhFDRVbz32zlVML2ux3Y4e6EEIQMWF7QnO5IRNcAEPxm1mEBY4nyTkG35tKrT1APfh3yI3QHrIoK48F7dBej8fJaY/dfYOYZvPXsKcnjrJe5sr4/YxOH1227pSKNpde79rCvBpcYVELdvgiSQjBwFCcHbteZHI8weuvdbORkJGGINMRI5laqx5te65JwLw1eADYyv0JCAgIWCGRrPCu951/Y072L27v4W5axPT29tLb2wtAIpFg//79jI2N8dWvfpUf/vCHAPzmb/4mTzzxBH/wB3/AV7/6VT7xiU8ghOChhx5icXGRiYmJ5WO86SzUXTWzZ2HsaXjwnyH3fAT4yIa7lebK5GtHGJ3eSzy6QM2JUKqkEFLgVT3MsEl+qtAkYACUuzYeRqNxHjlFZXiUhazn12npEohKFxg32dxPS3BsMMVyqZb+qCZjw1w9Iakj7Cde3RRCM1GwEMpbSVVafg+2xWs8HKoxO29StOCsW+KAGSMjTJLSpFYqoWIxZEPzIM/zyBa6GZleW1dHa4lheCil0LrxfL5lRUr/vjbGzIh650pDCrbt6MIwttHbf5WRa+2Uy60zi1Jpm/6BNrp6GkvyO8BrwBip9M0XF7wzSHwrTEBAQMC9w22Jibl69SovvvgiDz74IFNTU8vCpKenh6kpv/bF2NgYg4MrmR0DAwOMjY2tETGf+cxn+MxnPgPAzMza7sx3HK8K8xfQ3/rH6EgGdv8ScuCRdYeH02GK00Vcz2Yx37O8XVoSI+Qrg8VrC3TvusbQsYtY0RoLoxku/2w/5VxzGwPVN4U3PAqWH4yq8XyPVmT8lhoLlxfzmLIdI7JSvC1iwsBt+PS1FsxOJvy+SS3maBma7t48bZkS33mlm0cSSUzEsqUkl8tRq9WWvxtKKS6cn2Nu5r1sdNHJVJl8LuL3PELQ3ZPFshRWyGV2Ok52cVWLCCFQWjM5nmNweDdaO3T35rh+tb1lllM0atPVE2+IDdLAT4ECvqXnhm/VHcQAtrNZQUEfF78ysMZ3OQVhcQEBAW9fbvkXrFAo8LGPfYw/+qM/IplMNr0nhFgnQHR9nnzySZ588kkATp48eavTu0m072IqTcMrn0c5JeT297YcufNdO3jlv51tsrJIS7Lt0eHlejKm/g57Hj+HWRcnXbvGyQxP88xfPEG1GF1OUvK2X18WMLcLIQWRHsOvPutJrKqNVBLXcnFD1XV1gm4IGBYNMSxNf3swnTU572lUi9RqS2gOtVWQEizT44EBh9wq4aa1plKpUK1WsW2bl05PsDBXAqLrXpNSgnwuQldPjt6+LPGEH1zreaIuYFrvqzWUSiXgWQxDMbRNUcjbzM4sdYleYWoqT1dPnPbM0nyn8bN87kT/ohtBAtvwhUgOCOHHwOzYwr4TwGlWrlUDx4C3iDU0ICAg4Aa5JRHjOA4f+9jH+I3f+A0++tGPAtDd3b3sJpqYmKCry/fR9/f3MzIysrzv6Ogo/f39t3L628NSx+f18Grw+lfRw+9CyLULdee+Tvb/0j4u/s0lqoUqpm2y7Z3DDD3sNxEsLyzQtf0shtkgciRgegyfuECu/ARTzy8ipfQr5N5C9+lWFyeFjW30YDgWRt5jQT5NVcwQcjtIl4/jpoyW3bnXpFmzJF40EkXY9Di9EGKiqleydrVGipXQmEPtFbbF/Swrw9SEwxVyudYzrdVq1Kqaxfklt9n6N0FridYwOZ6mkAvT3ZvDMBQz0wkW5qPr7isNQTI1CfXMMsOA7t4c83NRlGr+bJWnmZzI055R+GnKc9yZ/kVbxcRP2+7Gzzw6gP9lyQJVoIaf0r0eFeBF1qb1v4ifwXTTUeMBAQEBbxo3LWK01nzyk59k//79/O7v/u7y9o985CN87nOf4/d///f53Oc+xy//8i8vb//jP/5jPv7xj/PMM8+QSqXe/HiYLgnzavPeicoBp+D3UGpBz+Eeug91oz2NMJqtT6WpK1gpCWbz4iENTWr/VV4u/inW/ig77L+DGX4Ppxc+23oONyhutIaQkaEt8hgCCYUy183/isYFoanqWQpcoLv0IUQsuenxPA0VD2wpiJgKpWvM1qzmYF4haLdqHGqvsT1Zo81uiAFSglot1Cp5CaU0pmmxMFfdJGO8noIuBMrzC7kVCmEKF8IN7/vHA9GUKSUEhEIGXd3Npf2FWD0ffwJSaoa3vY7vPloqGPdmsZRNNYMvps4DJ4BX8K1DS/Pbht/QsdUXZaN6NRP47qiAgICAtxc3LWKefvppPv/5z3P48GGOHTsGwL/5N/+G3//93+dXf/VX+exnP8vw8DBf+tKXAPjQhz7EU089xa5du4hGo/zpn/7pbbmAW8KVYPl9j4D1rTJCghVr8UbDECEQZou+OeEMwmgRxKs1RaVQeFRVjlcrX+Bw9NcxkHirF8ytiJeGeXvaZMF9hP2RTqTwF/gZ8wdNzRwRGo3LvPczMnygxfz8eBeAa0UYKQiE8Euu9Ecl7baJo9fGksxXJXtiFRoaOdeFiaRc6gLt+nqsfk1KgVODfM7BMDYvxNY/uEBvX56J8SQj11YCWe2wJp1hOWOoXNIszvlNGYWAzq4423e1YRjnmo7XnimggVi8wp59U6TSFT9Fviaxw2+m5aWRxu+DV389y1rL0DUggV+UL8xy36vl/Vp9uVWL4wQEBAS8PbhpEfPoo482xU008r3vfW/NNiEEf/Inf3Kzp7szzK/+YRf4T70NP+pGCLa/HyFv7lalh4dYeKmbVO9Uk0vJAy7VGv7WNUazf8H7kyZFT3G27DJ3ow//pX6i+hiRzjT99cV8ySpUZbrlLjVmmtxGqiapjMRxc37Wznx7gRG76teGqd+qibJEY2AKhbtGyJj8+MwQ9++aoz3jp4UXCzbzc7uZnIBaFdLtEArXY1QKkFsESYH2zPpxMEvYtiIccZqCcUM2tHfW3XR1whHIdMKuPUNEIo2LeQY/nsTHMOCdj1/0C/1JXe9GrZFh7y0WvLuaVsLDw28kaeB/WIPAIfzvdRdwocV+kqCmTEBAwNuVezw1YZUI0xri3eCWoZoFw4YdH0Ds+sWbPoMQgsjQb7Nw9f+kvX8EDTjC4+WKy6LXfP6SqmGIEIuevnEBA0TZR6Itja5oiDW7tQQWmhYNDYW5EqiroHg+jXaXeu7AhOGuaVPgacFU2UIuFYJpaBJpKoldCPPy6QGk9LOhPE/AshVIMDsNq/1jM9MFpqcKG16fEJqunhxaw9Rksr4NkmmxpgCelL5QMtb0pjoK/IBG64Y0lgrvNZ5rw6m8xVkSKqP4gb978dsODNS3Lb1vAP00ViIOCAgIeDtxj4uYFlQWEO//3/w4GGn6wba3SKQtRaTtn1KeX8RxFvlJ6X9FrbZiaYFR6eaiOc1rtZuLv3C8C+T+rI3Er4YQ8eZVOBbaS6F2juYncYOotWdl/wW7XnxvJcjVla2tbUrD/kSVS/NxSqaHAOJVi+FsfLm9wHoNGn1W139Zb5xGGhqB5sDhCUzT4+xLfbiOgZSCvoEUIbtEtbbShTsUqpJOL2DbVYTI4seJSHwhpWgV3/L2Fi3r4QFXgD349/sQfibSKL6IHAA6Nti/jB/UHKu/AgICAt5aBCJmNYblF7zrONAkYHR+DH31u1CagcwBxPDjiE3iZFYTaU8zPWcxNnUfXR3PY0g/onipy0HemOO1qrq57CQBXrGC6YHKa4z25rfjocN4qkTZveIX0cMjbA6RsI8RkjY1VcMtK9DNX4moY1IIre1JYAmB8Xo/exF4QgECQ99+JSCl5tCRUdray/WgXdi7f5qf/SSOchTOhTlqSQ+r2wIB0WiRvr6x5YBdraeApWBeg3sv/sNlxeol8EXLRsIFfJF3GpjEF3+qvs8JtlaLJiAgIOCNIRAxq6kW0M//7xDJwMO/B4CePQdn/tTvo6QVzF9EX/0evPNfIezNM3uWUFrzl6euU6zeR6Uao6fzBSyzTLHUjeeZtLddvvl5ewbGFb9gXPUFD7NPIqzG9GhJOvIwCXUcT+UwZAJDRpBIOuwuJsqjeKFJtIgi9EoV2/5clAuZLIp6pk89o/qQjOHiN3U01sTFrDar3Ly4MUxFpqO8/LeU9W2deeZeE0xfWUBLg94P9SAs6OmZbIqNabaw3GsCBnx3kuLGuldfwBcwjVarWeAscOS2zi4gICDgVghEzBqUX7W3MI7+7j/zRctq94NyoJpDX/w64uDf83sgaZBy48V6KlsBV7PdNEmVD1O+eohrrkdRaxKxq7cgYgQyn8S47NemcccVpb91iLzT8hdxayXA15BhDOlnLCntb8/VFtFoRHIePdkPnoGoP3FHXNid1UwkilRIYruS3mIEoa11y+sIoRkYmmP0ejta3/yTuxCavv7FNdulVOzcNcPBQ74lKz8hOf+dcWJ7ksjd96JQ2QgX+D7wDrbuErrGWpebwndDHeY2FjIKCAgIuCUCEbMReqMCMgr32vf4ueHgjZ0EzyKRDLHvYC/xROuiY07V5ahlIgEpBHGh6TAkF/Us/dueag4MuYEgjag5gDFzBLehaq5zXuFcqCLbBYmPWX7vpKXL0r6tJFeQLCxk2D7kZy5FQgPMDH6J0Oh7MGpDoEEVFxDxH7B9/heQ3orVyaPRsdAcpKu1YORahuHtc1y7cvOZL1bIY2jb4prtQkA44i5bXJJ9ipOfKKF1afN+mC3q1NzdKPxCeGeA9dtnNLPe915xm6sxBgQEBNwSd7+IiXb57QPWZbOSvetjaHj48o+Yif6UzopCzinyP+6jev8/xO5cWzwsN1VYFjA0/P9Q93MocTOrq0HYHETKGLV9L8DEbuRI14rXRMK2w6+xaB5ANbgTXA9OX4wwNW9yqD9PV7hCVUmytTDxhZM4T2u0Nwb4dyd07iHE0QhE1pvH6nn7f89MJTBND9e9EWvMyiLp1AycmolhqOXsI1UPGWpyGUl/l00MYf7Ye3b9XWC19FyfdhrT0FdIcmNuqYCAgIA7y93/i3T4t9Z/T5qQ3g6Z9aqcbozAFzJdRReEn4ic0OMYp/49urKwZvzCXGlZuCyhRBWVOL/l1dXEYCgxiClTdMf+Lqnwg8RDR0lHHiP2gTCh+wVGj8DaKen4SJmhg5fwGlw6noJyVTK7aPCJh6f45WNj9MdKbI8XGI7lKI51UGuqqwJC1avc3eDdKZVsbNvZfGgDhqHYd2CCeKKC1oIXnx9GiGG0DuG6FrlstOVU7l1xslVu5PM7iP98s3RTBb74OXy7JxUQEBBwS9z9lpjaYuvtZgzxrv8FpIk+90W/mvtNspS/AXW7jnLxrv4t+e7HKZfLWJZFqVTDdT1WiyU3efaGzvVA9wOcXThHOvwoQoSWM6gEEstoxzxewzxhI1Ds5Hkm1G40fk8mrWFyzuSVK2Ee2ZlloL2CafgtBb45muBSPoTcrXG3C2Kziu0/ryKXrDolB2Kh9Se2DpWKtWabEI2esxXLixAKO+zS3Zujs6vAz57eQa1qoNRBpDyEaUK67TrwMm9+I8a3GzG2nlmUAB7HT89exLfAbCdIsw4ICHircfeLmJc+33q7W0QXZ+CVz0NhnFtdFKWG8bTkQsagNysxGUDPzqK1Zl6coXRtP2JVgz5lLeCkXrghI9CpmXMgIkRCiTU1bKQwESi6eJ0Ml5lRSb6/eJD5Wt09UzCoZW0issKD2xew6mX+fz4d4XI+hKcFHgJMKHZIxo5YDL7oW1L0TBHReSOLmCYUcqjV1gofKQXbdrQzPV2gUirhOH5rgI7OAnv2TyOlX3yupyfL+Fg7C3MlMp1RfPGyVOMk4MYYuMHxEfwmkwEBAQFvXe5+EeOV13/vwtegOAVqsw6QPkv1aVv54FwBC7agaEsudUpC+hsMeb9GjQUWahexxf7m/kb2KJX+r4C4MXdLTWWRYv35akIsOLu5rm3yzmk87bD0MVtxRSyR47d7/grTeoSlzsUvzUdwV9V40aZgYdhk4EUHIQWkNutyvHRxSzFGmlptrRUGfCtMV0+CoW3twFU879xydd8lDEMTiToI4eG4VaAIjPHmNmJ8uyK5cRETEBAQ8Nbn7hcxG5G9Bt7aUvzrIWQIr20bauF1pFoRMwpQAsbSvrleC0WNeYriChUxiZZVWCUSKr1fB3ljAmYJpYu4qogpk02tBdCaUCVGuBIjIvo41PURSpbkS5fP4WlVF2Ae47UO+qpTmJEBEAY11doUpCVgCOhNIPoSm8xKELJrxGNV5ufjrBduJaVgxy6TcPgMkKdSMQmF9JqYFtcVZLMRtBak2xaBLPdmnZdbReJbVNaNyg4ICAh423Jvi5hanpbZSdLyt62x0GisY09C5buoc99FL7qgYTEqeLnXxDUa0oyFQzZ8CaEkSkwjjQratQCBFlUwNrAQbYHFyk/oiH6w3oRTIBBIzyRUjqDRaK2ZmpoiHA7zmJ3mnFtkyq1S1WG+Ov9OxDy8v3eBPW0ugzGHqwWL1X6tSM1E3t/vW2I2QUqPnbvn6Ogs8NKL/WQXV5o5GoYgnrCRUpDpLNE/cA6tfctLKLQSI7MkZLSCatVgbibKth2zhMMSWvR9CtiMNuAYQSxLQEDA3crdn520IRrfjtK4SAu/c3XnIb8B5NI2GYLdv4QItyES78bYl0I+EGfqeITntoUo2qtvpYGUESJ2N48mLI7v/gaWUcGQVVTi9Vueuauyy3MTgJ2PkppVWE52eYzWmnK5TNjTLLi1ZXeYoy1q2uJb4+3ML1zlnekLhIS3/GWQgFQwlI9vScCA7/rp6s4BYFm+KBTCt7wcPtbHgcM9FAtVOjquNLmOllKlxaqPwKvBOx6/xPD2Bfyo6/xN3KV7FYnvKjxJIGACAgLuZu5tSwz4RUbCGajMAQLS2xFH/yFEO2H6ZfTEc2DYiMF3INI7/H2MDCT+b4jif6FbLGLUyqxqSI1AEA3toE9cICEERnSeR478GZdz7Zxn4gaCeZcGNp7AIGJtXw7sjean2X7xS5huFdC4ZoqJ3r+DY/s9cma1g1vf31CCkCtxDIVnGDw9309/uMp7e4eYrShmKiXawhG6ylFmZvJ+u4GWrGwfHJpn+645pPTFSHvHAIgakYhF/0CaSNTilTMTuK6LHV7rQlvtShICEumtxSkFZPAbO+bwxZ5T3zZA8M87ICDgbif4lZMWYs9HoOcEoBFmQwBr91FE91F0fhyqi+haARGK++9ZOyH1LzF0jofj8zw/858pubMorTBEmHTknUhhkxHXWPYyCY9LYvwGcmsM2sKPUXTOU/PGEZhoFLbRR8o+6Y9wKux67ZsYDa4vy5ljYPS/cHX7P0ZLE6deorcvH6WzGEYLjdCCxXAVY7iLIz0eExNVjAp0Y2FUNF3dMWxlMHJtse6yWsu2HbMMbctiGEuxKgZCHKB/oIP+VXGkc7NFLMvdcj2XoO5LKxqjsAygD7+XkcBPi+5/k+YVEBAQ8OYQiBghoecEwlzbKkBX8+jn/gMUxkAYoFz09vch9n7UD6gVAkSKhJ3iiYH/iUJthnOLLyJFrCHgdmU1XvT0jScHC4kp49Q8iIeOEbEGMeSKi6B99iKiLjJK0Qwz3ftwrQiJxXHs8nnKsQO0S5P2YpiOYhiJWA4yTlds4vNxxrRFtbriVnNdl/HxcQYHB+ntS/LMT6+1nFo4LJBSofVSnZc+YKjFyBpCag4dG7/Rqw9YRgKPAdP4bQG6gPSbOaGAgICAN527XsSs1+lFA0LaiIf+eUsBA6Bf/I+Quw66ISvm6nchOQh9D6wZHw91kgkPsVibX942q7cR9V7hctUh6+kbzK/xyFdP49XjX1w1jxR7lt8tunBRDTDXv4fesMdQW8i/WinJJ/vAc3khN4fjGewspjBW3QmJoDIXpxptfZdGR0fZtWsX6bYwiwuVpveGt8/R3buw3A4AQOsxhEgAO/zoXPcS6AUwJxkc7CQWqwUWlptC4LuH4vVXQEBAQADcAyKmamSwvbnWQuaR/zsy1bp+hq5kYeFis4AB8Groy99BtBAxrnLJrqoQfDnvUqB209VNXLXAksAIV9OUzSJhM07VE7w4J/CMNqSA/jbdFKatDQslTOIhGC9ppG4dw62UYL3aK1prCoUCR473c/7cFFOTBQAsy2B4+yL5XIhKxSKZrNRruiiUuoDUccj9CegKflq0YrB3DzB4k3fhXmcQP+4lICAgIKCRu17EjJkPsMP7ZtM2DXhI5Kk/RnklyOxF7P0YIt6zMsgt+S6kVh19HX8xL7uLXFh4ipnKq9gyTkf0fgTRFZdRTZPTL20SxGsQMrqpea1dLaZMETaHKFbP0aHeyehilStGEUdTDyYWJKzWbiopBZ1hGC9B0XJI1Kx6LtMK4bCLEJJ1wl6oVqskEgkOHO5l30GNUgrXcTn1TIlqxaCzu0AkUkNWDUIhDyEcyH0GdI7G4F+zegEd7SfogHwjGMAJfNdRQEBAwN3PP/yH/5Cvf/3rdHV18corr2w6/q5PsR6o/mjNNr+dnUJWZsApwuSL6Kf/Z3SpoXNvtMtvELlmZwO6jlBxs/xo7H9hpPBTyu4ci7VrXF78KtnqGQCUdpirfmfT+Qks2iPvIhY6zNreNgYJ+ygJ+zA9yV8DqemUIeZrgqwjWBIE3gZmHrf+3niyhBI0ZBtppFTsPTBJMplDiNYHCYVW2gZIKTBNg1fPTlMqhti2Y549e6dJpqrYtrcshHRsG2vFiodQFbRe6ZukFOuKp7sbA+ik+Z+fqG8bwg/Q3Qu8i0DABAQE3Ev81m/9Ft/61re2PP6uFzEhii2f/VcV2a+7ib698r404NAn/PowS6OlBaE4Ytcvcjn7PVxVQTe4YhQOhdorKF1jsfxTpDIZ9D7Obud/IKZ2rKna65+5Qs2bIhE6QtzaD/VmjQKTlP0QYXMAgcQUJl1dXYSFZI9srr5acMFpIQg8BeOlet8kqRhNFqhEa0jbJdJW4djJKdozJTo754lGy6wu+mcYBvF4cwyG43hkFytYIY/+wUUMc2WfpZovmEkIb1t7090cuVyCbDZJoRAjl9usCvDdzIn6qw9ftDxYfx0BjgO7WWoLERAQEHCv8Nhjj9He3r7l8Xe9O6llRd5WaA8WLjRtkn0nUaEYXPsBVHPQeQAx/G4E15gt/oRWYbpSGDjeHLgVBrxfRWKhcCiKqyBaz8NTBTA6sa1BlHZx1AJdtccJiXY81yWsokgzyuvlHDok2abD2ELwkleqX5ng5QU40q6xpN8pQGm4XoSFqmAgFyVTCqOExkQgYg7mYImiuZ0U/Ugp6OpymZqaolQqARCNRunu7kbKZp3reQohBMlkBa0EGM3X5AfuSrD7oHK54R0DQh3Mj2dwHN+609MzcQ8G+hrAHvx/et31V0BAQMC9wczMDCdPnlz++8knn+TJJ5+86ePd9SKmHNtFpHhhC5EYAmIrC4ouz6NP//9g4ZK/Mke7EF1HEd7TUP4eUVEl1/I4mk67j3A+iqi7hxQOArGulDJlClcVsGQKM3wEgYEOW8i8iaXCXHDLXFCLK9eg4YSR4IQR5xWvSA1N1ROMLEp2tHkgBFJo+qLgzlu0l20kAlm3BHlFi/K1GOqAx5IxzrIsBgYGlmvCiHXUhW2bWCGDatVsKcqUqltkmvaXYLUBcjkd2x8rmtoN3N0I/Myi3fjWl4CAgIB7j87OTk6dOnXbjnfXixidGMQrXcfQ1TU2maa1U1qInR/099EK/bNPoysLCK38nQrjqJ/8z4i0RHSY7EhJZlyvyRYjkLTZu4lafbgkEHWBYBDBIIbbQvYIQpTd6yTsowhhrtTnlYpyMoczn+CiKjfnDwk45eZ5sNTGo5E0IqzBcqgl8vWL8q/SNmBnj0thfpVK0AI3Z6OLklqo1hT3sp548Y85jxBZ9uw1efmMTaVsEY3VaDTW+CKlnrJtD4CqQqgTQj0gBPF4nsXFNCDJ5VIkk/mmNO27C4FfPfcwQfn/gICAgNvPXS9iYqWLzKVPomSIWPE8nhEnlzhMz9RXMXRD5tGh30Cktvn/PXMWVcsj9epgVwWLCp1zSccNDu+0OFPVDaGymnxtmonyFUI6Q34eXAeskKAz9V4mQ1/zXVDLi7YgE3k/CLUseJYRvpiaMcp461Tgn6y5ZEoWPQOCUrjSMvFHmB4y7KEqaz/q/HyZcvYqMxZMC8WuZDv70x2YdVXiOA6Li4u4bo3OzksYRhEhFO0dEtse5swLgxw8MkYiWUVr37IyNZFicLjsG3hi+9acM5OZp1iM47omlUqEhYU0bW2LCLG2k/VbFxM/XqUdvzv0An6XbfDdQ3uBJWH4trmogICAgLcdd72IofMQmUvfxjVsquE+LK9A3+R/XWWFCSH6Hlz+U5fn0Gpt1+TlfRTogiI3G0YnGrtRa2pqlqnct7Bn3o9RGgAE1YpG5Ifo7fsV5uwfUhWTABikqHrjhIyu5T5Iq8+oN7BSaKHxPAiVxqmFq3ihtYGyGsBYm3lkSDDqzR3ba5rTTpaRYo7T85N8fMchauUy4+PjaK1pb59FyhWLiZQe+w5N8NKLAzx/aoiI7WJaHrVqiJMPlfHT0pdeq85rKIaHr5HPJyiX/QBlpQSm+VazxrSKpdpGUK8lICAg4M7x67/+6/zwhz9kdnaWgYEB/vW//td88pOfXHf83S9iht8Nl76F6ZUwixfXvi8MkAb6h/8C3f8QYteHceIdmz4/C6VJZnOQsNa8p0WOSs9XsBYeILTwICDQWpObD1Ptm1oe57FIvnaaqLkT00gjxaqPQ2jSXhiDYpPbKoJknxWhp8MCpUgsnCPkVZkYPAmyOU1bC6iVDczlNdm3eKQzK7EoGk2XtBhRNRaqFc7NTxHPlpbjY1KpHFI2L+jt7WVOPHiV77zUQ8ExKQnF+07OEw4vwiZ1iaXUpFI5UqnWUUVvHK2aa4JvRjqBX+J/Ht/asgvf8hIQEBAQcKf4i7/4ixsaf/eLmPFnWa8irZ/OrMEt+68rf4OeeQXxyD9nISppKynWMxAowFld1mUJAQgXp+0ZzNxBpBcHBKqSaBEMqyi5l4ionZhyRchIBD3RErn5drqExbR28ICQFrwzlMRC+PErhmC+43FihdfQTg1lRTCkn52kNVzIwVxHlsOqg2hZAC7xpN8yqlIG0wKMlaBjVyuu5xbYp9aKs9XEY1W8zjGuVduRwOvFCh3xG2us8MYjgGFgB2ADNWAEuILfAToBHAQ6CDKHAgICAt7a3P0i5srfrP+ekNAYF6NcKE5jzV7g/NABOmdfY3DBJeKujWzQAkbS66mYJSQqMoos1GNDhLPOOMVc6TtEzB30xh8jbMTR+gLX88/jhMPsrR5lUA0yVnXowsLUAiFXZqSlRTG+n8WrU8x1Q3sEap5fIybvCqQB8b4IR5IZrl27ztyMplJecZjYYYGT1uw1IjhaE1llEcrnE6TTC00BvH4QTJWPtP2Yz0z9Mq42WV8svhUw8MXLbqBRoEXwU573tNopICAgIOAtzN0vYpz8+u/pFhGzXhW9eIVdqb/H6eT/waXMPMmy5v7REoaSgInE5dVuk1xkE6eTFqD8AE+Bg4ycIa4TbJvK05mr4knB1TbJ9XYDhKLsXsQWR5gp/Zz5ygU87YuerDVOSh3kuHyifuAW59UeEdXDRCnLWGX1e7A/3YFthahVwlTKFdBLThRNNAb3mXGk8GWI4fqF7lzXvz/z8+0k43MIi5XYHSEQMoSVOMDu7CiXa9t4oLPMnWSp2q9sFT7UMoZF4lfAPUgQYBsQEBBw93H3i5gNFy+TNcGnhk0xHELFrhO98h4cB6pmgZ+FMrQ5WSyKeAejTLjfr/cH2vjcstIDuGS8y+yvXUBeUahqDVmPT9k37ZGuaF7q960D2eoI85VLywLGH+WSky+zLbENXe2mXI6uOZMWBniSo8UUqTaICUkVzSW3jJ2PELd8MbUwV2ta68NRCEf8lgJQb3ygwfM8pJRordHapLRYING5Kk1YGMhQG33RPJmki3GHdYIQUCpaRKKqLmSWLiQNVIDSqj0UcB3Yxz3xVQ8ICAi4x7j7f9nD7VCaav2eYYHnsbQYKuB0n2Bafwc5J9G9GmopwmMfQ+oo1VgHOwr/Ca64FHYcYKH2843PrUK4nd9hz8IMw+U8dD4O1/8W2dAfwNTQm1Nc6NSUTYNsbgrPrK49loCKeZ2ou63FiTR2JIoZcsm0iWVLRRjBoVCEUJ9bvzqJt6rRUjTW2rIhhKCnpwchBK7rEosM4acSr7mJDKdjJMPZTVKkt1g5eQO0hpmZBJNjXTz0aBhfgHbgF5Fbz20ogCr3wlc9ICAg4F7jru+dxI5foOVlhuLw6P8DUsMgTJAmV/ramU4YKO3g6iqerqGseSJ7f8SD7xjmwXdsJ/nA3yNRLbP96rMYamPTg2GWSIUmGMocRbzzX0N5BlqkbmsgVVbgSqqvRcFbO1+BJGxBT89F+vpGkbK5zF5bWzuZLmONINFa4jgGWj8PQDK1qh/PeoHLSjExMcH4+DjFYhEt+lrWHBYo0mELuWHBunb8/kA3RmMvqKU6NFcvZSiXNUr146c8L/V2Sm1wpKAHUUBAQMDdyF3/eCoG34GeegHmzvsCQpqARJz4HxDxXnj0f0LX8qA116f/PcotNu2v8ZirnscMeQhhQfseeM+/pzN/Feb+A35GywoSi47IXiwjRm/0GN3RwwghKZVKlGsh0kjEqgBYgUXVi2L/zSNQsXH3Nfdw8sdAb6wLKTXxeInt2y8zOdlLsegv4vl8nvVSm5WG+UoWIa+zc2+GM6cmUEqjNZSKYEf0sjup6drrKqJQKFAuG2zfFkIaq+OI5AZ9AwR+pdqHgBwwus64CH5V2xKQB5JAFwvzPyfd5n8eiwsRzr7ch9YSyzJazHcvMLfqHhj4qdGbBWAHBAQEBLwduftFjDTg/k/B/Ov+K5SAvvsR1kp8h6gXifNaWEkABAJPO5j1J3ohJFZyB0flJzg9+5/8NgV4SGFhywQVdxHbSBEP9SKExPM8xsbGMFLHSS2+6LcyqKMRuGYS8fLfQc75EbnWT+7HecdzmCGrfj6PB7pPYjTUgDEMTSYzuyxilFJYlkW12uyKcq0a5XiOYjECjKEZY/fJYQrjFvn8PPFEllhCUizF/D5Gfn44q2OJlFLkC4dJpc7iu3Hq19AkYJbK7C8FU/fhZ/1I/LiVPmByZV9fmgH3tbzvtdoJfvyDaZRq7JQt2LajVb2WFL5YehW/eq6NL2AGWx47ICAgIODtz10vYqDeDyiz139tQHf0MKOFZ9Z0p46YGWxjbTXcvvgJ0vY2xgrPkncmmSy+SNlboOzNk3PGGS+e4uHef4qo+K4O14gw0fsxuqafwvBKoDWVSB8TnR/GmFsRNub1AczJPvb8TjuhkEFHeASjhUfMstzl60skEliWxfj42LIbRgtFOZ4DAaohJmWidp0Du48SNmwKzlWuFSJUbL+3VMSTGLk06FVF87SmUjFJpd6Hb/F4jrWWHwNfkIRozXFgihWLzCDQtc5Y6OlNopXm8sU5ajUPy5Js25Ghf3A911Eb8Mi6xwsICAgIuLu4J0TMVtmT/jDT5bM4XgGFh9AghcnRjt9Yd5+olWF32wf58dinUU2ZTgpPVzk79yUORD6JUcuSmfke0z2/yLVtv43p5lHSRBlRlKuoTs8s72lul8TeE2bWraFdTdZJsiORx2yomqs1VCphNBo7FCKZTCKlpLe3k5mZMRzHwrPLCKHRq6wqGs18ZZY2O8OFbGLZLqKBsqEwYkUihWTTPkKIeqNIgR9M+yjwPL4LSOALl/UFjNaabDbL4mIFpdqJxWJkMhlMc+O4ot7+FD19SbTWCCE2aFAZEBAQEHCvEYgYwFUu1wuXWazNkQ5/AKf4ErJ4gVSpylAOInPfQp/479ddQLXWZGvXW763WL1KNBNFzD9NrHiBcHmMSqQf1/JFglA1KuMubsEXQEaXJPY+C2GBqsuLvGNyIRtnf1u+fj6/W/TUXJpqrEimvQ1Zj+iNx9uIx0Hr08xUPEaLkVYzRuExWR5bU55OI3CtGkp4yAZrjBCCZLJR2CSAJ/BFjAaibJTOPj09TS6XW46zyWazFAoFtm3bhmFsHLMSiJeAgICAO8dU2eT/80rHmz2Nm+KeFzFaay7kzlF2i2g0Uoaw4yeQkSPseOm/YjklmD0HCxehfXfLYwghMEUYV6+uMgemDGPbNrJ8DYmmd/zLZFPHKST2IrUinjtL595fIhaOU5oqIo8palZz0TihBJVahFGl6E6XKdUsxssRStEKErkcK+M4DnNzc5TLZUxzJ4m2EH45/ebMIYkkHWpnpHCl5fVIYRCO29TyvrCKRCJ0d3evIzbW1qxZjeM4TQJmCaUU2WyW9vagJ1FAQEBAwI1zz4uYklek4pbQjQu9EGghmenaR9/YC+DVYO61ZRGj8+PoiWdBKUTvCURqmOHkO7mS+yGqoUidFBbbEo8DYETbyHu9zHa+B0+GEWiS2dPE869xJfcu6IoQ7Y5SjDTUYtFgF2NYNd9tVMynOJ+rUo0XMGshYuUYUhnMZxdRaVhYWEAp37biOA6VSpV0e4asnl+26kgkbXaGmJkgasYpec3ZWP5pFf3dA5g9S4HFt2YFqVarCCHWiBitNaVSKRAxAQEBAQE3xT0vYqreWusJgDZMKtE2/w9pQT2bSV3+Dpz/b6D8Inn6yt+gt72bvfs+SsVdZKL0IhILpR36YifY3fYhAErDH2a6GEJLXxhoIJc6Rj68A21G/LBbrYkUUpTiWVTIJVSOYNVsRP1/CLAcG1kQGE6onknkV9edm5tbew1aoxYku4cOMF+dQaFptztIWCm/mF20v759xakkECSNNkxh3ZJ4UUpRKBTwPA/DMNYImCUsa/NGkwEBAQEBAa2450VMxIi2rPcmPIdYfrr+B/D/b+/+Y9o6zz2Af9/jYwO2wUnAxGBI+GHyiwSWBsLV/bG7sttkSyaqJWxN12n3Nouo1l6t09RGu3/c6d6pS3YrVdukbrpat66Z1MAf0aRUzUpzm3bV1KZNE1JpN+y2ETUJYCCAzS+Df53z3D+MTwzYxCbMxvbziVDC8TmH98mb2k/P+77PW94Mmp9YSGCiasOoAaD/bYjy/dhb+jh2ho5gLjQGk74Uebq7c0jcVAySFidMJOmh5m9atEpZQKBgtgizFjf0/gKIJYX6BATkYF7C8amqCikko7xgKzweD6YmZzBv8KOoqAiKoqAqfzvuBIcwG5qBIEDvK4Dql/CZ+zNUVlYuTOZNjs/nw+Dg4KLEJdaTGCEENm7cmPT9GWOMMSDHkhgiAkZ6QP2XgNAcYNuH/Kp/QpHegung5N0hJVIhqQqKPbcAvQnigSchDIWg/rdj31gNgkZ6ICxbkS9bkC8vXwIcDMbbwXr50w5BAnAbIdZoM6LBwcVF5ubn5zE1NaU9aZGlAhQqi5MVRVEwPDyMyspKeL1eqKqKgoICGAwGEBEmJyfhdruhKAoMBgOsVitMJhOICC6XSxvWimYwGBAIBCCEgE6nw+bNm1eVJDHGGGNAriUx/3cOuPUOoCwUhJsdAQ1dRvXf/TtGAncw7huFSiqKDJtglwT0DzwJbKgJF8wDACEh5gocIQCx8gqbvLw8eL1zy4rbxh6xEaBpIwIFQN5fsWJ+5MmIosSu9Ov3+9HX17fomMVigU6ng9vt1q4PBAJwuVyw2+2QJCnm/YgIOp0OtbW1UFUVsiyvixVHqqpibGwMMzMzICKYTCZYrVYe5mKMsQyQM0kM+aaA/rcANaqWixoE5j0QQ5dhr2qF3bR15ZvY9gK9XcuPCx1EefOKl1qtVni9t8JVcRc+u6P/vOh2AijaAMx7AVVdyJFE/PMX7oaVd+xenaVDQJOTk3HPGx8fh9VqjTl0FDlHp9Pdc0l1qhARBgcH4ff7l2yxMI/q6mpt2TpjjLH1KXfepSf7FvZNWkINAHf+nNAtRJ4FaPjn8ERfnSH8u6QHdhwN78O0gry8PNg2lyPgDycmoXijS5GfJQCjObzDdCRxufcu0ekVCASQnx/70dHyOjPp5/P5FiUwEaqqYnp6Ok2tYowxlqiceRIDQ9HibZEjhAQUxJ5cSqSCbv0xPASlBoGyJojaQ0DrfwGjH4dXKG3+HERBYkuELRvM2FXowNidWczPBRFUlq8oWg+ih3nirSqKRa8Pr2gqKyuDy+XSrhVCID8/HxbLSjtNr070z0jW0n2mou/p88VetcYYY2z9yJ0kZmMtkFcEzI1jUfE3IUNsbY15CV1/CbjzcbhODAA4/yc8gfcf/gNiyz+uqhk6nQRbWfiJRH//DAKB2JtOJkIVKiAIQpW05darIYRAaWkpJCm827Zer4fZbMatW7eSuk9hYXh/KZPJhOrqakxPTyMUCsFoNMJkMq3pHBifz4c7d+7A5/NpT3msVmtSQ0DxJhULIZCXl/gKMMYYY+mRM8NJQgiIlmeAQjsgGQA5H5ALgMbjEEUVy86n2WFg9PrdBAYIz6fxTQKuK2vSppKS+GWei4qKYLPZYr6mIgBvoQfeDW54LR54LR6E5HuMT8WQn5+PoqIiVFZWwmKxoLCwEJs2bYKiKBgfH4fZbF6UeAghVpzwumHDBu3Psixj06ZNKC0tXXaf+xUMBjEwMKA9LSEiTE9PY3h4OKn7FBQUxIxnPQ59McYYWy53nsQAEMYSiM//J8g7CgTngaIKiFjzZABg8rOF1UhLKH7Q+F8gKv8etLC6CUoQwrYX2OhI6sM63iTZyPBLzAJ2IMwXTUOVVK19pFMxb56CaWrjov2OVhIZ9on+EJ+bm8PQ0BACfsKUBwj4BfQGgq08HyazAWazGQUFBbh9+zZCodCioZxkn4LcD4/HE7f6byAQSHjZthAClZWVGB0dxezsLIB7bbHAGGNsPcmpJCZCmDbf+6T8jYi9nFoGjFaot94FejsBUgAi0O0/AmX7gIbjCSUyqqpibm4u5mtEBLfbjVAotOw1RQ5C1SFGgqUCpgCkOVPMGi3LwsvPX5TAEBFGRkYQCBDGRu5OHwr4BQb6/ajYUgCbzQwA2Lp1q7YsWQiBwsLClD258Pl8mJmZifmaEALBYDCp2jM6nQ7l5eX3NbeGMcZYeuTMcFLSincABtPyZEHSAWUPhBMYNQiQCoDCtWeGrwETf9FOJSLMBKYxOjOMGe80gsEgRkdH4XQ6cft27F2vI2IlMABAkrp0P8eFdknIKzTec/KsEAImkwnl5eXLfp6iKJiZiiQw0ZN7gaGBKYSC4fovbrcb09PTUFUViqJgamoKAwMDCU8CVlU1oURrKbfbjYGBgbh1bYho1cXzeKdsxhjLPDn5JCYRQkjA35wE9fw3MDMQTmb0ZojPnQBmXKB4Q02ujyBKdsGnzOOTyf9FSA0BFK7Ca5ramNAEXEmS4n7ISyE59gMiSAhMB+Hxepa/JgRKSkpgMpni1mmJDAUF/eG7xbrH/HwQBYKWDecQEQKBAGZmZlZ8IuPz+Rae9oTnGZlMJthstoSGbkKhECYmJuImSpHkLJEidcFgEHNzc9o1PHTEGGOZiZOYlaghiC2fBwkJtKEGM8jD2JQTAd848srbQFIJSJcffhpDCnTqPBS9GfTpJ/AZZxHSByErBoAEdP7EK8Cu9JRCp8oomLKAhIqQPgiJdCChhJ+e+CUAKpY+YCMijI2NYWxsLOpY+EtVwvlZpB5NiQ0Q4m6ioKrh1wAFQ67biDftJTIcNTIyknCcXq93WUXg1SIizM7O4tNPP13V9ZFEJvKUR5ZlmM1mGAwGhEIhBINByLIMSZK0bRgiCWFBQQGEEFBVFX6/H5IkIS8vD0IIbfKw3++Hz+eDwWDQzk8kptVcB9zdgJOIYDQauQIxYywrpTyJ6e7uxtNPPw1FUXDixAn84Ac/SHUT7omIQH9+BRj6EIBASDbjlr0dIdkEgQLIcEDJBwCCoLuf6gqMCxXpCPlzJgBmRFfSTXwZdPTThuXX6NRwt8lL9juKGgCKed3d+BbOF4BOXnxckhZfG/2QIptHW5YOUYVCobgTr5eKTAqORQihbcWg7VUly6isrIQsx//PT1VVDA0NLapXk8h1QDg5dLlci44VFxdj06bE6hkxxlimSOmcGEVR8NRTT+GNN95Ab28vOjs70dvbm8omJMZ1BXB9FJ7zogYwUtIKVS6CDgZI0ENAWvjS3d0TIPKFcLISfj3692QyABH1FevV2L/udZ12vVjpS8R9jSWPiLQEiYhARNrcqJVMTEzA5/Np1yR6naqqWqHB6K/I/RhjLJukNIm5cuUKHA4HampqYDAYcOzYMZw/fz6VTUgI3b67SaQqZPiMVeGEhbE1EhmSimd6ejrm/J97Xef1emMej9TSYYyxbJLSJGZoaAiVlZXa9xUVFRgaGlp0zq9+9Ss0NTWhqalp0RyOlFKiVwbxAi6Wesls95DodatZEcYYY+vZuvuE7ujowNWrV3H16lVYrdb0NMLeEq7qC0CiAPSBO6CY65oZW528vLwViwOazeZVXWc0GmMej9TzYYyxbJLSJMZut2NgYED7fnBwEHa7PZVNSIjY8oXw9gS68P45ttGLEKofKsKl/WnhV+TPmSayMomlXmSib7wtJSJKSkogy7I2GTjR62RZRklJybLtIkwmU9wEhzHGMlVKVyc1Nzfj5s2bcDqdsNvt6OrqwtmzZ1PZhIQInR74238DRq+Dxm4gL38DaspsGJ2bgWd2GH6ahEEtgYEsCxNqKfYWBfclVpYRb3bt0nPvPbE3kshEr1TSPvdi7vadezN79Xo99Hq9VtQPCC/F9vv9ICKtQF5+fr62AikUCml7TEmSBLPZDKPRiJmZGW2ptMViuWdtGlmWUVVVhZmZGczPzyd8HQBs3LgRRqNRK0gYaQMX82OMZZuUJjGyLOPFF1/EwYMHoSgKjh8/jvr6+lQ2IWFC0gFlTRBlTQDCj6zshaWwb65Nb8NYRoreHDNRkiTBYrHcswpzLHl5eekbjmWMsRRJeZ2YQ4cO4dChQ6n+sYwxxhjLMutuYi9jjDHGWCI4iWGMMcZYRuIkhjHGGGMZiZMYxhhjjGUkTmIYY4wxlpE4iWGMMcZYRuIkhjHGGGMZiZMYxhhjjGUkTmIYY4wxlpE4iWGMMcZYRuIkhjHGGGMZiZMYxhhjjGUkTmIYY4wxlpE4iWGMMcZYRuIkhjHGGGMZiZMYxhhjjGUkTmIYY4wxtm50d3dj+/btcDgc+MlPfrLiuZzEMMYYY2xdUBQFTz31FN544w309vais7MTvb29cc/nJIYxxhhj68KVK1fgcDhQU1MDg8GAY8eO4fz583HPl1PYtqT19/ejqalpTe85NjYGq9W6pvdcj3IlTiB3Ys2VOAGONRvlSpzA/cfa39+/do1JQH3lVpz9l39Nyc+an59f9Lne0dGBjo4O7fuhoSFUVlZq31dUVODDDz+Me791ncSMj4+v+T2bmppw9erVNb/vepMrcQK5E2uuxAlwrNkoV+IEMi/W7u7udDdh1Xg4iTHGGGPrgt1ux8DAgPb94OAg7HZ73PM5iWGMMcbYutDc3IybN2/C6XQiEAigq6sLbW1tcc9f18NJfw3RY2/ZLFfiBHIn1lyJE+BYs1GuxAnkVqxrTZZlvPjiizh48CAURcHx48dRX18f93xBRJTC9jHGGGOMrQkeTmKMMcZYRuIkhjHGGGMZKWeSmGTKGGeCgYEBPPjgg9i1axfq6+vx85//HADgdrvx0EMPoa6uDg899BA8Hg8AgIjw3e9+Fw6HAw0NDejp6Uln85OmKAr27t2Lr3zlKwAAp9OJlpYWOBwOPPLIIwgEAgAAv9+PRx55BA6HAy0tLSmvt3C/Jicn0d7ejh07dmDnzp24fPlyVvbpT3/6U9TX12P37t149NFH4fP5sqZPjx8/jtLSUuzevVs7tpo+PHPmDOrq6lBXV4czZ86kPI5ExIr12WefxY4dO9DQ0ICvfvWrmJyc1F47ffo0HA4Htm/fjjfffFM7vt7fn2PFGfHCCy9ACKGVBMn0Ps04lANCoRDV1NRQX18f+f1+amhooBs3bqS7WffF5XLRtWvXiIhoenqa6urq6MaNG/Tss8/S6dOniYjo9OnTdPLkSSIiunDhAn3pS18iVVXp8uXLtH///rS1fTVeeOEFevTRR+nw4cNERPS1r32NOjs7iYjoiSeeoF/+8pdERPSLX/yCnnjiCSIi6uzspK9//evpafAqfetb36KXXnqJiIj8fj95PJ6s69PBwUGqqqqiubk5Igr35W9/+9us6dN3332Xrl27RvX19dqxZPtwYmKCqquraWJigtxuN1VXV5Pb7U59MPcQK9Y333yTgsEgERGdPHlSi/XGjRvU0NBAPp+PPvvsM6qpqaFQKJQR78+x4iQiun37Nh04cIC2bNlCY2NjRJT5fZppciKJef/99+nAgQPa96dOnaJTp06lsUVrr62tjS5evEjbtm0jl8tFROFEZ9u2bURE1NHRQWfPntXOjz5vvRsYGKDW1la6dOkSHT58mFRVpeLiYu2NMrp/Dxw4QO+//z4REQWDQSouLiZVVdPW9mRMTk5SVVXVsvZmW58ODg5SRUUFTUxMUDAYpMOHD1N3d3dW9anT6Vz0gZdsH549e5Y6Ojq040vPW0+Wxhrt97//PX3jG98gouXvu5F+zZT351hxHj16lD7++GPaunWrlsRkQ59mkpwYTopVxnhoaCiNLVpb/f39uH79OlpaWjA6OoqysjIAgM1mw+joKIDM/jv43ve+h+effx6SFP7nOjExgQ0bNkCWwxUComOJjlOWZVgsFkxMTKSn4UlyOp2wWq14/PHHsXfvXpw4cQJerzfr+tRut+OZZ57Bli1bUFZWBovFgn379mVln0Yk24eZ2rdLvfzyy/jyl78MIPtiPX/+POx2OxobGxcdz7Y417ucSGKy2ezsLI4ePYqf/exnKCoqWvSaEAJCiDS1bG28/vrrKC0txb59+9LdlL+6UCiEnp4efOc738H169dhMpmWzQ/Ihj71eDw4f/48nE4nXC4XvF5vRpc9T1Y29GEifvzjH0OWZTz22GPpbsqam5ubw6lTp/CjH/0o3U3JeTmRxCRbxjhTBINBHD16FI899hiOHDkCANi8eTOGh4cBAMPDwygtLQWQuX8H7733Hl577TVUVVXh2LFjePvtt/H0009jcnISoVAIwOJYouMMhUKYmppCcXFx2tqfjIqKClRUVKClpQUA0N7ejp6enqzr07feegvV1dWwWq3Q6/U4cuQI3nvvvazs04hk+zBT+zbilVdeweuvv45XX31VS9iyKda+vj44nU40NjaiqqoKg4ODeOCBBzAyMpJVcWaCnEhiki1jnAmICN/+9rexc+dOfP/739eOt7W1abPez5w5g4cfflg7/rvf/Q5EhA8++AAWi0V7vL2enT59GoODg+jv70dXVxdaW1vx6quv4sEHH8S5c+cALI8zEv+5c+fQ2tqaMf/Xa7PZUFlZiU8++QQAcOnSJezatSvr+nTLli344IMPMDc3ByLS4szGPo1Itg8PHjyIixcvwuPxwOPx4OLFizh48GA6Q0hYd3c3nn/+ebz22mswGo3a8ba2NnR1dcHv98PpdOLmzZvYv39/Rr4/79mzB3fu3EF/fz/6+/tRUVGBnp4e2Gy2rOzTdS2dE3JS6cKFC1RXV0c1NTX03HPPpbs59+1Pf/oTAaA9e/ZQY2MjNTY20oULF2h8fJxaW1vJ4XDQF7/4RZqYmCAiIlVV6cknn6SamhravXs3ffTRR2mOIHnvvPOOtjqpr6+Pmpubqba2ltrb28nn8xER0fz8PLW3t1NtbS01NzdTX19fOpuctOvXr9O+fftoz5499PDDD5Pb7c7KPv3hD39I27dvp/r6evrmN79JPp8va/r02LFjZLPZSJZlstvt9Otf/3pVffib3/yGamtrqba2ll5++eV0hbOiWLHW1tZSRUWF9r4UWVlGRPTcc89RTU0Nbdu2jf7whz9ox9f7+3OsOKNFT+zN9D7NNLztAGOMMcYyUk4MJzHGGGMs+3ASwxhjjLGMxEkMY4wxxjISJzGMMcYYy0icxDDGGGMsI3ESwxhjjLGMxEkMY4wxxjLS/wOcMkhPbcYsNgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "network_with_classifier = Autoencoder(data)\n",
-    "network_with_classifier.build_encoder()\n",
-    "network_with_classifier.build_decoder()\n",
-    "network_with_classifier.build_classifier()\n",
-    "network_with_classifier.build_autoencoder(use_latent_classifier=True)\n",
-    "network_with_classifier.fit_data( use_latent_classifier=True)\n",
-    "network_with_classifier.dir()\n",
-    "network_with_classifier.plot_latent_dimension(\"Autoencoder with classifier\")\n",
-    "network_with_classifier.plot_score(\"Autoencoder with classifier\")\n"
+    "import hls4ml\n",
+    "config = hls4ml.utils.config_from_keras_model(network.autoencoder, granularity='model')\n",
+    "print(\"-----------------------------------\")\n",
+    "print(\"Configuration\")\n",
+    "#plotting.print_dict(config)\n",
+    "print(\"-----------------------------------\")\n",
+    "hls_model = hls4ml.converters.convert_from_keras_model(network.autoencoder,\n",
+    "                                                       hls_config=config,\n",
+    "                                                       output_dir='model_1/hls4ml_prj',\n",
+    "                                                       part='xcu250-figd2104-2L-e')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## In this part a test is provided."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def test_network_empty(network):\n",
-    "    error = 'When you create a network instance all networks should be None and addable by hand later.'\n",
-    "    assert network.encoder is None, error\n",
-    "    assert network.decoder is None, error\n",
-    "    assert network.latent_classifier is None, error\n",
-    "    assert network.autoencoder is None, error\n",
-    "\n",
-    "def test_tensor_and_functional(network):\n",
-    "    assert (isinstance(network.encoder, KerasTensor) or isinstance(network.encoder, tf.Tensor)), \"\"\"\n",
-    "    After building the encoder it must be a keras tensor.\"\"\"\n",
-    "    assert (isinstance(network.decoder, KerasTensor) or isinstance(network.decoder, tf.Tensor)), \"\"\"\n",
-    "    After building the decoder it must be a keras tensor.\"\"\"\n",
-    "    assert (isinstance(network.latent_classifier, KerasTensor) or \n",
-    "    isinstance(network.latent_classifier, tf.Tensor)), \"\"\"\n",
-    "    After building the latent_classifier it must be a keras tensor.\"\"\"\n",
-    "    assert isinstance(network.autoencoder, Functional), \"\"\"\n",
-    "    After building the encoder it must be a keras Functional.\"\"\"\n",
-    "\n",
-    "def test_shape(network):\n",
-    "    assert network.decoder.shape[-1] == 784, \"\"\"\n",
-    "    After building the decoder it musthave shape (None, 784).\"\"\"\n",
-    "    assert network.encoder.shape[-1] == 2, \"\"\"\n",
-    "    After building the encoder it must have shape (None,2), but is\n",
-    "    \"\"\" + str(network.encoder.shape)\n",
-    "    assert network.latent_classifier.shape[-1] == 10, \"\"\"\n",
-    "    After building the latent_classifier it must be a keras tensor.\"\"\"\n",
-    "    assert network.autoencoder.input_shape == network.autoencoder.output_shape\n",
-    "\n",
-    "def test_classifier(network, dataset):\n",
-    "    assert network.autoencoder.input_shape in network.autoencoder.output_shape\n",
-    "    assert (None, 10) in network.autoencoder.output_shape\n",
-    "    prediction = network.autoencoder.predict(dataset.x_test)\n",
-    "    assert (np.all(prediction[1] <= 1) and np.all(prediction[1] >= 0)) and (\n",
-    "        np.all(prediction[0] <= 1) and np.all(prediction[0] >= 0))"
+    "# Compile, predict"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"autoencoder\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "encoder_input (InputLayer)   [(None, 784)]             0         \n",
-      "_________________________________________________________________\n",
-      "dense_161 (Dense)            (None, 128)               100480    \n",
-      "_________________________________________________________________\n",
-      "dense_162 (Dense)            (None, 64)                8256      \n",
-      "_________________________________________________________________\n",
-      "encoder_output (Dense)       (None, 2)                 130       \n",
-      "_________________________________________________________________\n",
-      "dense_163 (Dense)            (None, 64)                192       \n",
-      "_________________________________________________________________\n",
-      "dense_164 (Dense)            (None, 128)               8320      \n",
-      "_________________________________________________________________\n",
-      "ecoder_output (Dense)        (None, 784)               101136    \n",
-      "=================================================================\n",
-      "Total params: 218,514\n",
-      "Trainable params: 218,514\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "Model: \"functional_9\"\n",
-      "__________________________________________________________________________________________________\n",
-      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
-      "==================================================================================================\n",
-      "encoder_input (InputLayer)      [(None, 784)]        0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_161 (Dense)               (None, 128)          100480      encoder_input[0][0]              \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_162 (Dense)               (None, 64)           8256        dense_161[0][0]                  \n",
-      "__________________________________________________________________________________________________\n",
-      "encoder_output (Dense)          (None, 2)            130         dense_162[0][0]                  \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_165 (Dense)               (None, 128)          384         encoder_output[0][0]             \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_163 (Dense)               (None, 64)           192         encoder_output[0][0]             \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_166 (Dense)               (None, 64)           8256        dense_165[0][0]                  \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_164 (Dense)               (None, 128)          8320        dense_163[0][0]                  \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_167 (Dense)               (None, 32)           2080        dense_166[0][0]                  \n",
-      "__________________________________________________________________________________________________\n",
-      "ecoder_output (Dense)           (None, 784)          101136      dense_164[0][0]                  \n",
-      "__________________________________________________________________________________________________\n",
-      "classifier_output (Dense)       (None, 10)           330         dense_167[0][0]                  \n",
-      "==================================================================================================\n",
-      "Total params: 229,564\n",
-      "Trainable params: 229,564\n",
-      "Non-trainable params: 0\n",
-      "__________________________________________________________________________________________________\n",
-      "3/3 [==============================] - 0s 135ms/step - loss: 1.7434 - ecoder_output_loss: 0.2311 - classifier_output_loss: 2.2990 - ecoder_output_accuracy: 0.0000e+00 - classifier_output_accuracy: 0.1367 - val_loss: 1.6963 - val_ecoder_output_loss: 0.2298 - val_classifier_output_loss: 2.2882 - val_ecoder_output_accuracy: 0.0000e+00 - val_classifier_output_accuracy: 0.1800\n"
+      "Writing HLS project\n",
+      "Done\n"
+     ]
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "Could not find module 'c:\\Users\\loren\\Desktop\\CODE\\Electronics Project\\DL-model-for-FPGA\\model_1\\hls4ml_prj\\firmware\\myproject-3D7f2580.so' (or one of its dependencies). Try using the full path with constructor syntax.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-14-43549258be49>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhls_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mascontiguousarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0my_hls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhls_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\MLinPhysics\\lib\\site-packages\\hls4ml\\model\\hls_model.py\u001b[0m in \u001b[0;36mcompile\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    544\u001b[0m                 \u001b[0mdlclose_func\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_int\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    545\u001b[0m                 \u001b[0mdlclose_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_top_function_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 546\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_top_function_lib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcdll\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLoadLibrary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlib_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    547\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    548\u001b[0m             \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurr_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\MLinPhysics\\lib\\ctypes\\__init__.py\u001b[0m in \u001b[0;36mLoadLibrary\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mLoadLibrary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dlltype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m \u001b[0mcdll\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLibraryLoader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCDLL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\miniconda3\\envs\\MLinPhysics\\lib\\ctypes\\__init__.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, name, mode, handle, use_errno, use_last_error, winmode)\u001b[0m\n\u001b[0;32m    371\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    372\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 373\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_dlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    374\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    375\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: Could not find module 'c:\\Users\\loren\\Desktop\\CODE\\Electronics Project\\DL-model-for-FPGA\\model_1\\hls4ml_prj\\firmware\\myproject-3D7f2580.so' (or one of its dependencies). Try using the full path with constructor syntax."
      ]
     }
    ],
    "source": [
-    "test_data = mnist.MNISTData(data_fraction=1/100)\n",
-    "test_network = Autoencoder(test_data)\n",
-    "\n",
-    "test_network_empty(test_network)\n",
-    "\n",
-    "test_network.build_encoder()\n",
-    "test_network.build_decoder()\n",
-    "test_network.build_classifier()\n",
-    "test_network.build_autoencoder()\n",
-    "\n",
-    "test_tensor_and_functional(test_network)\n",
-    "test_shape(test_network)\n",
-    "\n",
-    "test_network.build_autoencoder(use_latent_classifier=True)\n",
-    "test_network.fit_data(epochs=1, use_latent_classifier=True)\n",
-    "\n",
-    "test_classifier(test_network, test_data)"
+    "hls_model.compile()\n",
+    "data.x_test = np.ascontiguousarray(data.x_test)\n",
+    "y_hls = hls_model.predict(data.x_test)"
    ]
   },
   {
diff --git a/DCGAN_mnist.ipynb b/DCGAN_mnist.ipynb
deleted file mode 100644
index 9fa0710..0000000
--- a/DCGAN_mnist.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9506,"status":"ok","timestamp":1634733803309,"user":{"displayName":"Lorenzo Valente","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiTh0enxhVzBGrVgHIc03P7l-WgzoGzF2_H_aZg=s64","userId":"09158965273549322331"},"user_tz":-120},"id":"3HYjFWp2EZ-o","outputId":"739cf358-27f7-42e6-e8ba-ca685944e9e4"},"outputs":[{"name":"stdout","output_type":"stream","text":["1 Physical GPUs, 1 Logical GPUs\n"]}],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import tensorflow as tf\n","from tensorflow.python.keras import Input, Sequential\n","from tensorflow.python.keras.engine.functional import Functional\n","from tensorflow.python.keras.models import Model\n","\n","gpus = tf.config.experimental.list_physical_devices('GPU')\n","if gpus:\n","    try:\n","        # Currently, memory growth needs to be the same across GPUs\n","        for gpu in gpus:\n","            tf.config.experimental.set_memory_growth(gpu, True)\n","        logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n","        print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n","    except RuntimeError as e:\n","        # Memory growth must be set before GPUs have been initialized\n","        print(e)\n","\n","from tensorflow.keras.layers import (Dense, Lambda, Conv2D, Conv2DTranspose, LeakyReLU, \n","                                    BatchNormalization,ZeroPadding2D, Activation ,Flatten, Reshape, Cropping2D, Dropout)\n","from tensorflow.python.keras.utils.np_utils import to_categorical\n","from tensorflow.keras import backend as K\n","\n","import glob\n","import imageio\n","import os\n","import PIL\n","import time\n","\n","from IPython import display\n","from tensorflow.keras.optimizers import Adam, RMSprop\n","\n","import tensorflow_docs.vis.embed as embed"]},{"cell_type":"markdown","metadata":{"id":"OTvTNmxTuVWf"},"source":["## Import the MNIST data class and call it."]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":539,"status":"ok","timestamp":1634733807791,"user":{"displayName":"Lorenzo Valente","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiTh0enxhVzBGrVgHIc03P7l-WgzoGzF2_H_aZg=s64","userId":"09158965273549322331"},"user_tz":-120},"id":"r8AkzCKKVpZd","outputId":"bdaf3f2a-7ad5-4d98-fc57-a7864388e6c1"},"outputs":[],"source":["import MNIST_dataset as mnist\n","data = mnist.MNISTData(gan=True)"]},{"cell_type":"markdown","metadata":{"id":"Po5D_nEZuVWg"},"source":["## Deep Convolutional Generative Adversarial Network with TensorFlow\n","\n","The aim of this exercise is to implement a DCGAN architecture and to test it on the MNIST dataset.\n","\n","Code partially adapted from [TensorFlow Documentation](https://www.tensorflow.org/tutorials/generative/dcgan)."]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":422,"status":"ok","timestamp":1634733984239,"user":{"displayName":"Lorenzo Valente","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiTh0enxhVzBGrVgHIc03P7l-WgzoGzF2_H_aZg=s64","userId":"09158965273549322331"},"user_tz":-120},"id":"dn-3dHC4zfW0"},"outputs":[],"source":["class DCGAN():\n","    \"\"\"Deep Convolutional Generative Adversarial Network\"\"\"\n","    \n","    batch_size = 256\n","\n","    def __init__(self, data: mnist.MNISTData):\n","\n","        self.x_train = data.x_train\n","        self.buffer_size = self.x_train.shape[0]\n","\n","        #Preparing dataset for the custom training\n","        self.train_dataset = tf.data.Dataset.from_tensor_slices(self.x_train)\n","        self.train_dataset = self.train_dataset.shuffle(self.buffer_size).batch(self.batch_size)\n","        \n","        self.latent_dim = 100\n","\n","        self.generator = None\n","        self.g_optimizer = None        \n","        self.discriminator = None\n","        self.d_optimizer = None\n","\n","        # Instantiate a loss function, i.e. function to compute the cross entropy loss\n","        self.loss_fn = tf.keras.losses.BinaryCrossentropy()\n","        #store geneterator and discriminator loss\n","        self.loss_record = np.array([None, None])\n","        \n","    def make_generator_model(self, optimizer = Adam(learning_rate=1e-4)):\n","        \"\"\"Building the generator model.\"\"\"\n","\n","        self.g_optimizer = optimizer\n","        self.generator = Sequential(\n","            [\n","                Input(shape=(self.latent_dim,)),\n","                # Starting with 100 dimension and then we scale it up to reach the proper shape\n","                # We want to generate 100 coefficients to reshape into a 7x7x256 map\n","                # Remember: only the stides should affect my output size   \n","                Dense(7 * 7 * 256, use_bias=False),\n","                BatchNormalization(momentum=0.8),\n","                LeakyReLU(alpha=0.2),\n","                Reshape((7, 7, 256)),\n","\n","                #inverse of convolutional layer\n","                Conv2DTranspose(128, kernel_size=5, padding='same', use_bias=False),\n","                BatchNormalization(momentum=0.8),\n","                LeakyReLU(alpha=0.2),\n","        \n","                Conv2DTranspose(64, kernel_size=5, strides=(2, 2), padding='same', use_bias=False),\n","                BatchNormalization(momentum=0.8),\n","                LeakyReLU(alpha=0.2),\n","\n","                Conv2DTranspose(1, kernel_size=5, strides=(2, 2), padding='same', use_bias=False),\n","                Activation(\"tanh\"),\n","            ],\n","            name=\"generator\",\n","        )\n","        #assert self.generator.output_shape == (None, 28, 28, 1)\n","\n","    def make_discriminator_model(self, optimizer = Adam(learning_rate=5e-5)):\n","        \"\"\"Building the discriminator model which is an image recognition model.\"\"\"\n","\n","        self.d_optimizer = optimizer\n","        self.discriminator = Sequential(\n","            [\n","                Input(shape=(28, 28, 1)),\n","                Conv2D(64, kernel_size=5, strides=(2, 2), padding='same'),\n","                LeakyReLU(alpha=0.2),\n","                Dropout(0.25),\n","\n","                Conv2D(128, kernel_size=5, strides=(2, 2), padding='same'),\n","                LeakyReLU(alpha=0.2),\n","                Dropout(0.25),\n","\n","                # Flatten all together and map into a single dense layer which gives us True or False\n","                Flatten(),\n","                Dense(1),\n","                Activation('sigmoid'),\n","            ],\n","            name=\"discriminator\"\n","        )\n","                   \n","    def discriminator_loss(self, real_output, fake_output):\n","        real_loss = self.loss_fn(tf.ones_like(real_output), real_output)\n","        fake_loss = self.loss_fn(tf.zeros_like(fake_output), fake_output)\n","        total_loss = real_loss + fake_loss\n","        return total_loss\n","    \n","    def generator_loss(self, fake_output):\n","        return self.loss_fn(tf.ones_like(fake_output), fake_output)\n","\n","    def train_dcgan(self, epochs=500, save_interval = 10):\n","        \"\"\"Training loop.\"\"\"\n","        seed = tf.random.normal([16, self.latent_dim])\n","        start_loop = time.time()\n","        for epoch in range(epochs):\n","            start_epoch = time.time()\n","\n","            for image_batch in self.train_dataset:\n","                gen_loss, disc_loss = self.train_step(tf.cast(image_batch,float))\n","\n","            print('Time for epoch {} is {} sec'.format(epoch + 1, time.time() - start_epoch))\n","            print('Generation loss: {} Discriminator loss: {} '.format(gen_loss, disc_loss))\n","            self.loss_record = np.vstack([self.loss_record, np.array([gen_loss, disc_loss])])\n","        \n","            # If at save interval, than save and disply the generated image samples\n","            if epoch % save_interval == 0:\n","                self.generate_and_save_images(epoch + 1, seed)\n","\n","        print(\"\\nTotal time taken: %.2fs\" % (time.time() - start_loop))\n","        print(\"\\n\\n\\n\")\n","\n","    # The function to be \"compiled\".\n","    @tf.function\n","    def train_step(self, images):\n","        \"\"\"GAN training step.\"\"\"\n","        \n","        # Sample normally distributed points in the latent space\n","        random_latent_vec = tf.random.normal([self.batch_size, self.latent_dim])\n","    \n","        with tf.GradientTape() as g_tape, tf.GradientTape() as d_tape:\n","            gen_imgs = self.generator(random_latent_vec, training=True)\n","\n","            real_output = self.discriminator(images, training=True)\n","            fake_output = self.discriminator(gen_imgs, training=True)\n","\n","            g_loss = self.generator_loss(fake_output)\n","            d_loss = self.discriminator_loss(real_output, fake_output)\n","\n","        gradients_of_generator = g_tape.gradient(g_loss, self.generator.trainable_variables)\n","        gradients_of_discriminator = d_tape.gradient(d_loss, self.discriminator.trainable_variables)\n","\n","        self.g_optimizer.apply_gradients(zip(gradients_of_generator, self.generator.trainable_variables))\n","        self.d_optimizer.apply_gradients(zip(gradients_of_discriminator, self.discriminator.trainable_variables))\n","        \n","        return g_loss, d_loss\n","    \n","    def generate_and_save_images(self, epoch, test_input, save=True):\n","\n","        pred = self.generator(test_input, training=False)\n","\n","        fig = plt.figure(figsize=(4, 4))\n","\n","        for i in range(pred.shape[0]):\n","            plt.subplot(4, 4, i+1)\n","            plt.imshow(pred[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n","            plt.axis('off')\n","        if save:\n","            plt.savefig('./images/GAN/training_checkpoints/image_at_epoch_{:04d}.png'.format(epoch))\n","        plt.show()\n","\n","    def gif(self):\n","        \"\"\"Creation of the gif with generated images over epochs.\"\"\"\n","\n","        with imageio.get_writer('./images/GAN/dcgan.gif', mode='I') as writer:\n","            filenames = glob.glob('./images/GAN/training_checkpoints/image*.png')\n","            filenames = sorted(filenames)\n","            for filename in filenames:\n","                image = imageio.imread(filename)\n","                writer.append_data(image)\n","            image = imageio.imread(filename)\n","            writer.append_data(image)\n","            embed.embed_file('./images/GAN/dcgan.gif')\n","    \n","    def plot_losses(self):\n","        \"\"\"Plotting the Generator and Discriminator losses\"\"\"\n","        plt.plot(self.loss_record[:])\n","        plt.ylabel('Loss')\n","        plt.xlabel('Epoch')\n","        plt.legend(['Generator Loss', 'Discriminator Loss'], loc='best')\n","        plt.title('Generator and Discriminator losses')\n","        plt.savefig('./images/GAN/g_d_losses.png')\n","\n","        plt.show()\n","    \n","    def dir(self):\n","        \"\"\"Creation of the folders path to store the results.\"\"\"\n","        dir = os.path.join(\"images\")\n","        if not os.path.exists(dir):\n","            os.mkdir(dir)\n","        dir2 = os.path.join(\"./images/GAN\")\n","        if not os.path.exists(dir2):\n","            os.mkdir(dir2)\n","        dir3 = os.path.join(\"./images/GAN/training_checkpoints\")\n","        if not os.path.exists(dir3):\n","            os.mkdir(dir3)\n","\n"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":12381822,"status":"ok","timestamp":1634746368286,"user":{"displayName":"Lorenzo Valente","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiTh0enxhVzBGrVgHIc03P7l-WgzoGzF2_H_aZg=s64","userId":"09158965273549322331"},"user_tz":-120},"id":"xhuln5q4zfW5","outputId":"b714f61d-3aa3-479f-c49b-d827444a1d40"},"outputs":[{"name":"stdout","output_type":"stream","text":["WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.\n","WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/keras/backend.py:4994: UserWarning: \"`binary_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n","  '\"`binary_crossentropy` received `from_logits=True`, but the `output`'\n"]},{"name":"stdout","output_type":"stream","text":["Time for epoch 1 is 26.450862169265747 sec\n","Generation loss: 0.6366156935691833 Discriminator loss: 1.1922187805175781 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 2 is 24.409884452819824 sec\n","Generation loss: 0.6576040983200073 Discriminator loss: 1.3898584842681885 \n","Time for epoch 3 is 24.470492124557495 sec\n","Generation loss: 0.6284387111663818 Discriminator loss: 1.4050722122192383 \n","Time for epoch 4 is 24.50720453262329 sec\n","Generation loss: 0.6730262041091919 Discriminator loss: 1.4278290271759033 \n","Time for epoch 5 is 24.463303089141846 sec\n","Generation loss: 0.714038074016571 Discriminator loss: 1.2998613119125366 \n","Time for epoch 6 is 24.572487831115723 sec\n","Generation loss: 0.7276344299316406 Discriminator loss: 1.3961673974990845 \n","Time for epoch 7 is 24.55902647972107 sec\n","Generation loss: 0.6860306859016418 Discriminator loss: 1.3639447689056396 \n","Time for epoch 8 is 24.567805528640747 sec\n","Generation loss: 0.6917208433151245 Discriminator loss: 1.4011050462722778 \n","Time for epoch 9 is 24.47160315513611 sec\n","Generation loss: 0.7330641746520996 Discriminator loss: 1.3493083715438843 \n","Time for epoch 10 is 24.528580904006958 sec\n","Generation loss: 0.6210813522338867 Discriminator loss: 1.4764294624328613 \n","Time for epoch 11 is 24.42134189605713 sec\n","Generation loss: 0.8054171800613403 Discriminator loss: 1.3403205871582031 \n","Time for epoch 12 is 24.46204900741577 sec\n","Generation loss: 0.6575912833213806 Discriminator loss: 1.4256553649902344 \n","Time for epoch 13 is 24.431004285812378 sec\n","Generation loss: 0.6863359212875366 Discriminator loss: 1.4545680284500122 \n","Time for epoch 14 is 24.446370840072632 sec\n","Generation loss: 0.8410559892654419 Discriminator loss: 1.2718136310577393 \n","Time for epoch 15 is 24.42324733734131 sec\n","Generation loss: 0.627433180809021 Discriminator loss: 1.5119622945785522 \n","Time for epoch 16 is 24.50329041481018 sec\n","Generation loss: 0.6928431987762451 Discriminator loss: 1.4767615795135498 \n","Time for epoch 17 is 24.41785979270935 sec\n","Generation loss: 0.7549816966056824 Discriminator loss: 1.3961987495422363 \n","Time for epoch 18 is 24.450830221176147 sec\n","Generation loss: 0.6857573390007019 Discriminator loss: 1.3818509578704834 \n","Time for epoch 19 is 24.451706409454346 sec\n","Generation loss: 0.8230560421943665 Discriminator loss: 1.3155536651611328 \n","Time for epoch 20 is 24.564064502716064 sec\n","Generation loss: 0.680307149887085 Discriminator loss: 1.548678994178772 \n","Time for epoch 21 is 24.465370893478394 sec\n","Generation loss: 0.8955198526382446 Discriminator loss: 1.143937110900879 \n","Time for epoch 22 is 24.520278930664062 sec\n","Generation loss: 0.8295205235481262 Discriminator loss: 1.3077208995819092 \n","Time for epoch 23 is 24.440029621124268 sec\n","Generation loss: 0.8238087892532349 Discriminator loss: 1.2081270217895508 \n","Time for epoch 24 is 24.437941074371338 sec\n","Generation loss: 0.7688300609588623 Discriminator loss: 1.2213187217712402 \n","Time for epoch 25 is 24.416293144226074 sec\n","Generation loss: 0.9471256732940674 Discriminator loss: 1.0859076976776123 \n","Time for epoch 26 is 24.452570915222168 sec\n","Generation loss: 0.8170927166938782 Discriminator loss: 1.3389065265655518 \n","Time for epoch 27 is 24.44278573989868 sec\n","Generation loss: 0.863640308380127 Discriminator loss: 1.0648125410079956 \n","Time for epoch 28 is 24.45478892326355 sec\n","Generation loss: 0.951090395450592 Discriminator loss: 1.2565603256225586 \n","Time for epoch 29 is 24.492414236068726 sec\n","Generation loss: 0.9176203608512878 Discriminator loss: 1.2229998111724854 \n","Time for epoch 30 is 24.47725248336792 sec\n","Generation loss: 0.8209613561630249 Discriminator loss: 1.3088703155517578 \n","Time for epoch 31 is 24.47218656539917 sec\n","Generation loss: 0.7911497354507446 Discriminator loss: 1.2767138481140137 \n","Time for epoch 32 is 24.50469422340393 sec\n","Generation loss: 0.8392816781997681 Discriminator loss: 1.2660982608795166 \n","Time for epoch 33 is 24.501696348190308 sec\n","Generation loss: 0.7192486524581909 Discriminator loss: 1.375556230545044 \n","Time for epoch 34 is 24.473691701889038 sec\n","Generation loss: 0.9169098138809204 Discriminator loss: 1.1363909244537354 \n","Time for epoch 35 is 24.493930101394653 sec\n","Generation loss: 0.8706681132316589 Discriminator loss: 1.2954375743865967 \n","Time for epoch 36 is 24.548444509506226 sec\n","Generation loss: 0.8688363432884216 Discriminator loss: 1.324819564819336 \n","Time for epoch 37 is 24.453603982925415 sec\n","Generation loss: 0.8712670207023621 Discriminator loss: 1.2468122243881226 \n","Time for epoch 38 is 24.49369788169861 sec\n","Generation loss: 0.7446900010108948 Discriminator loss: 1.2830829620361328 \n","Time for epoch 39 is 24.480379581451416 sec\n","Generation loss: 0.7126196622848511 Discriminator loss: 1.4155895709991455 \n","Time for epoch 40 is 24.477718353271484 sec\n","Generation loss: 0.8912059664726257 Discriminator loss: 1.243844747543335 \n","Time for epoch 41 is 24.488926649093628 sec\n","Generation loss: 0.8653951287269592 Discriminator loss: 1.2965898513793945 \n","Time for epoch 42 is 24.528430223464966 sec\n","Generation loss: 0.8006578087806702 Discriminator loss: 1.4546713829040527 \n","Time for epoch 43 is 24.502280950546265 sec\n","Generation loss: 0.83897864818573 Discriminator loss: 1.1274473667144775 \n","Time for epoch 44 is 24.5607328414917 sec\n","Generation loss: 0.8395034670829773 Discriminator loss: 1.1615264415740967 \n","Time for epoch 45 is 24.51969599723816 sec\n","Generation loss: 0.8328734040260315 Discriminator loss: 1.2910741567611694 \n","Time for epoch 46 is 24.53221607208252 sec\n","Generation loss: 0.7234864234924316 Discriminator loss: 1.378736972808838 \n","Time for epoch 47 is 24.523576974868774 sec\n","Generation loss: 0.872243344783783 Discriminator loss: 1.2519474029541016 \n","Time for epoch 48 is 24.514273405075073 sec\n","Generation loss: 0.8093393445014954 Discriminator loss: 1.3802053928375244 \n","Time for epoch 49 is 24.505065202713013 sec\n","Generation loss: 0.9364572763442993 Discriminator loss: 1.154221773147583 \n","Time for epoch 50 is 24.561475038528442 sec\n","Generation loss: 0.7520143985748291 Discriminator loss: 1.3797776699066162 \n","Time for epoch 51 is 24.47486114501953 sec\n","Generation loss: 0.8110509514808655 Discriminator loss: 1.3725917339324951 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 52 is 24.486167192459106 sec\n","Generation loss: 0.7736532092094421 Discriminator loss: 1.2180368900299072 \n","Time for epoch 53 is 24.47185230255127 sec\n","Generation loss: 0.9121396541595459 Discriminator loss: 1.3220003843307495 \n","Time for epoch 54 is 24.55121159553528 sec\n","Generation loss: 0.8581744432449341 Discriminator loss: 1.2585810422897339 \n","Time for epoch 55 is 24.556457996368408 sec\n","Generation loss: 0.662219762802124 Discriminator loss: 1.4453840255737305 \n","Time for epoch 56 is 24.527060747146606 sec\n","Generation loss: 0.8173899054527283 Discriminator loss: 1.2765244245529175 \n","Time for epoch 57 is 24.5465989112854 sec\n","Generation loss: 0.7790800929069519 Discriminator loss: 1.3372139930725098 \n","Time for epoch 58 is 24.60479235649109 sec\n","Generation loss: 0.8257735371589661 Discriminator loss: 1.2952001094818115 \n","Time for epoch 59 is 24.547473192214966 sec\n","Generation loss: 0.8493360877037048 Discriminator loss: 1.1661858558654785 \n","Time for epoch 60 is 24.576788187026978 sec\n","Generation loss: 0.8832699656486511 Discriminator loss: 1.2206695079803467 \n","Time for epoch 61 is 24.55813717842102 sec\n","Generation loss: 0.8357189297676086 Discriminator loss: 1.2205986976623535 \n","Time for epoch 62 is 24.575931549072266 sec\n","Generation loss: 0.8181374073028564 Discriminator loss: 1.3559867143630981 \n","Time for epoch 63 is 24.456367015838623 sec\n","Generation loss: 0.8231266736984253 Discriminator loss: 1.3730783462524414 \n","Time for epoch 64 is 24.460695028305054 sec\n","Generation loss: 0.7493121027946472 Discriminator loss: 1.325368881225586 \n","Time for epoch 65 is 24.470929384231567 sec\n","Generation loss: 0.7957437038421631 Discriminator loss: 1.3288171291351318 \n","Time for epoch 66 is 24.485208749771118 sec\n","Generation loss: 0.9042544364929199 Discriminator loss: 1.1452734470367432 \n","Time for epoch 67 is 24.498939037322998 sec\n","Generation loss: 0.9438794255256653 Discriminator loss: 1.1559624671936035 \n","Time for epoch 68 is 24.54212999343872 sec\n","Generation loss: 0.8356125354766846 Discriminator loss: 1.2017712593078613 \n","Time for epoch 69 is 24.544431447982788 sec\n","Generation loss: 0.8871216177940369 Discriminator loss: 1.2223198413848877 \n","Time for epoch 70 is 24.496403455734253 sec\n","Generation loss: 0.8854236602783203 Discriminator loss: 1.2237704992294312 \n","Time for epoch 71 is 24.471338987350464 sec\n","Generation loss: 0.846505880355835 Discriminator loss: 1.2530910968780518 \n","Time for epoch 72 is 24.465507745742798 sec\n","Generation loss: 0.8422317504882812 Discriminator loss: 1.2428882122039795 \n","Time for epoch 73 is 24.452288150787354 sec\n","Generation loss: 0.8061031103134155 Discriminator loss: 1.3880395889282227 \n","Time for epoch 74 is 24.473034858703613 sec\n","Generation loss: 0.8312557935714722 Discriminator loss: 1.2842676639556885 \n","Time for epoch 75 is 24.481554985046387 sec\n","Generation loss: 0.8558223843574524 Discriminator loss: 1.2871735095977783 \n","Time for epoch 76 is 24.566497325897217 sec\n","Generation loss: 0.8526219725608826 Discriminator loss: 1.1809269189834595 \n","Time for epoch 77 is 24.43671703338623 sec\n","Generation loss: 0.8394092917442322 Discriminator loss: 1.2922694683074951 \n","Time for epoch 78 is 24.491974115371704 sec\n","Generation loss: 0.95078444480896 Discriminator loss: 1.1594797372817993 \n","Time for epoch 79 is 24.47572612762451 sec\n","Generation loss: 0.8878360986709595 Discriminator loss: 1.2361571788787842 \n","Time for epoch 80 is 24.516211986541748 sec\n","Generation loss: 0.72800213098526 Discriminator loss: 1.3264518976211548 \n","Time for epoch 81 is 24.493724822998047 sec\n","Generation loss: 0.7830042243003845 Discriminator loss: 1.415696382522583 \n","Time for epoch 82 is 24.492127418518066 sec\n","Generation loss: 0.7625980377197266 Discriminator loss: 1.293431043624878 \n","Time for epoch 83 is 24.498852014541626 sec\n","Generation loss: 0.8062779903411865 Discriminator loss: 1.3872792720794678 \n","Time for epoch 84 is 24.515668392181396 sec\n","Generation loss: 0.8485809564590454 Discriminator loss: 1.2329823970794678 \n","Time for epoch 85 is 24.498804330825806 sec\n","Generation loss: 0.8576357364654541 Discriminator loss: 1.3183085918426514 \n","Time for epoch 86 is 24.553882122039795 sec\n","Generation loss: 0.753516674041748 Discriminator loss: 1.3238728046417236 \n","Time for epoch 87 is 24.52589750289917 sec\n","Generation loss: 0.872867226600647 Discriminator loss: 1.3546581268310547 \n","Time for epoch 88 is 24.594083309173584 sec\n","Generation loss: 0.8305497169494629 Discriminator loss: 1.2861971855163574 \n","Time for epoch 89 is 24.615764617919922 sec\n","Generation loss: 0.7953904867172241 Discriminator loss: 1.282273292541504 \n","Time for epoch 90 is 24.552072048187256 sec\n","Generation loss: 0.8461853265762329 Discriminator loss: 1.353467345237732 \n","Time for epoch 91 is 24.554160833358765 sec\n","Generation loss: 0.7782129645347595 Discriminator loss: 1.3229399919509888 \n","Time for epoch 92 is 24.596633434295654 sec\n","Generation loss: 0.8446157574653625 Discriminator loss: 1.225553035736084 \n","Time for epoch 93 is 24.4610333442688 sec\n","Generation loss: 0.8914373517036438 Discriminator loss: 1.2669591903686523 \n","Time for epoch 94 is 24.556176900863647 sec\n","Generation loss: 0.7193474769592285 Discriminator loss: 1.3266916275024414 \n","Time for epoch 95 is 24.504446983337402 sec\n","Generation loss: 0.88443922996521 Discriminator loss: 1.2754838466644287 \n","Time for epoch 96 is 24.51007390022278 sec\n","Generation loss: 0.7889787554740906 Discriminator loss: 1.32290518283844 \n","Time for epoch 97 is 24.485230445861816 sec\n","Generation loss: 0.8849781155586243 Discriminator loss: 1.2793748378753662 \n","Time for epoch 98 is 24.531017541885376 sec\n","Generation loss: 0.7837223410606384 Discriminator loss: 1.2207145690917969 \n","Time for epoch 99 is 24.480562925338745 sec\n","Generation loss: 0.8166278600692749 Discriminator loss: 1.3461030721664429 \n","Time for epoch 100 is 24.551206588745117 sec\n","Generation loss: 0.7596080303192139 Discriminator loss: 1.391331672668457 \n","Time for epoch 101 is 24.538390636444092 sec\n","Generation loss: 0.8998469710350037 Discriminator loss: 1.2445542812347412 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 102 is 24.502715349197388 sec\n","Generation loss: 0.8005692362785339 Discriminator loss: 1.182018756866455 \n","Time for epoch 103 is 24.485464334487915 sec\n","Generation loss: 0.8927517533302307 Discriminator loss: 1.2109969854354858 \n","Time for epoch 104 is 24.479087829589844 sec\n","Generation loss: 0.6974508762359619 Discriminator loss: 1.3409206867218018 \n","Time for epoch 105 is 24.57222604751587 sec\n","Generation loss: 0.8040576577186584 Discriminator loss: 1.2687710523605347 \n","Time for epoch 106 is 24.509342670440674 sec\n","Generation loss: 0.7856907844543457 Discriminator loss: 1.3286076784133911 \n","Time for epoch 107 is 24.483514308929443 sec\n","Generation loss: 0.7316707372665405 Discriminator loss: 1.3262298107147217 \n","Time for epoch 108 is 24.53660821914673 sec\n","Generation loss: 0.7736356258392334 Discriminator loss: 1.317482352256775 \n","Time for epoch 109 is 24.550642251968384 sec\n","Generation loss: 0.7704164981842041 Discriminator loss: 1.318222165107727 \n","Time for epoch 110 is 24.517646551132202 sec\n","Generation loss: 0.812583327293396 Discriminator loss: 1.320859670639038 \n","Time for epoch 111 is 24.507976055145264 sec\n","Generation loss: 0.8200472593307495 Discriminator loss: 1.3358898162841797 \n","Time for epoch 112 is 24.532400131225586 sec\n","Generation loss: 0.8271750807762146 Discriminator loss: 1.3439831733703613 \n","Time for epoch 113 is 24.504364728927612 sec\n","Generation loss: 0.8110525012016296 Discriminator loss: 1.3458489179611206 \n","Time for epoch 114 is 24.44308590888977 sec\n","Generation loss: 0.7098498940467834 Discriminator loss: 1.4907715320587158 \n","Time for epoch 115 is 24.43238377571106 sec\n","Generation loss: 0.8026055693626404 Discriminator loss: 1.279024600982666 \n","Time for epoch 116 is 24.51678729057312 sec\n","Generation loss: 0.8643169403076172 Discriminator loss: 1.2572153806686401 \n","Time for epoch 117 is 24.52071976661682 sec\n","Generation loss: 0.7802205085754395 Discriminator loss: 1.2823268175125122 \n","Time for epoch 118 is 24.505831956863403 sec\n","Generation loss: 0.8488163352012634 Discriminator loss: 1.2731623649597168 \n","Time for epoch 119 is 24.485270500183105 sec\n","Generation loss: 0.7755672335624695 Discriminator loss: 1.3570281267166138 \n","Time for epoch 120 is 24.49784278869629 sec\n","Generation loss: 0.801318347454071 Discriminator loss: 1.2956156730651855 \n","Time for epoch 121 is 24.47947382926941 sec\n","Generation loss: 0.8276892900466919 Discriminator loss: 1.2432935237884521 \n","Time for epoch 122 is 24.49770164489746 sec\n","Generation loss: 0.8009998798370361 Discriminator loss: 1.2891498804092407 \n","Time for epoch 123 is 24.518500566482544 sec\n","Generation loss: 0.8200147747993469 Discriminator loss: 1.3293137550354004 \n","Time for epoch 124 is 24.579817295074463 sec\n","Generation loss: 0.7940900921821594 Discriminator loss: 1.3206712007522583 \n","Time for epoch 125 is 24.494513273239136 sec\n","Generation loss: 0.7516800165176392 Discriminator loss: 1.3906577825546265 \n","Time for epoch 126 is 24.50945234298706 sec\n","Generation loss: 0.7728562951087952 Discriminator loss: 1.3514935970306396 \n","Time for epoch 127 is 24.479074001312256 sec\n","Generation loss: 0.7934020161628723 Discriminator loss: 1.279815673828125 \n","Time for epoch 128 is 24.586872100830078 sec\n","Generation loss: 0.8050888776779175 Discriminator loss: 1.4975972175598145 \n","Time for epoch 129 is 24.513098001480103 sec\n","Generation loss: 0.7322999835014343 Discriminator loss: 1.343855857849121 \n","Time for epoch 130 is 24.520161151885986 sec\n","Generation loss: 0.8197007179260254 Discriminator loss: 1.3780863285064697 \n","Time for epoch 131 is 24.545872688293457 sec\n","Generation loss: 0.7557879686355591 Discriminator loss: 1.3005921840667725 \n","Time for epoch 132 is 24.522643566131592 sec\n","Generation loss: 0.7453899383544922 Discriminator loss: 1.363008737564087 \n","Time for epoch 133 is 24.47096610069275 sec\n","Generation loss: 0.7552394866943359 Discriminator loss: 1.3511334657669067 \n","Time for epoch 134 is 24.53265404701233 sec\n","Generation loss: 0.8322384357452393 Discriminator loss: 1.2887063026428223 \n","Time for epoch 135 is 24.50874137878418 sec\n","Generation loss: 0.8067687749862671 Discriminator loss: 1.3448865413665771 \n","Time for epoch 136 is 24.55396294593811 sec\n","Generation loss: 0.7995004653930664 Discriminator loss: 1.3727037906646729 \n","Time for epoch 137 is 24.508660554885864 sec\n","Generation loss: 0.8081310987472534 Discriminator loss: 1.2767174243927002 \n","Time for epoch 138 is 24.446423768997192 sec\n","Generation loss: 0.7905147075653076 Discriminator loss: 1.309269666671753 \n","Time for epoch 139 is 24.434043645858765 sec\n","Generation loss: 0.7536188960075378 Discriminator loss: 1.3181878328323364 \n","Time for epoch 140 is 24.464985847473145 sec\n","Generation loss: 0.862647294998169 Discriminator loss: 1.297605037689209 \n","Time for epoch 141 is 24.456090450286865 sec\n","Generation loss: 0.8541561961174011 Discriminator loss: 1.14168381690979 \n","Time for epoch 142 is 24.488046884536743 sec\n","Generation loss: 0.8558837175369263 Discriminator loss: 1.272765040397644 \n","Time for epoch 143 is 24.48058772087097 sec\n","Generation loss: 0.8219076991081238 Discriminator loss: 1.190406084060669 \n","Time for epoch 144 is 24.549383640289307 sec\n","Generation loss: 0.8440346121788025 Discriminator loss: 1.250351905822754 \n","Time for epoch 145 is 24.48525071144104 sec\n","Generation loss: 0.7252489328384399 Discriminator loss: 1.3693554401397705 \n","Time for epoch 146 is 24.403528928756714 sec\n","Generation loss: 0.7868527173995972 Discriminator loss: 1.3168983459472656 \n","Time for epoch 147 is 24.477487564086914 sec\n","Generation loss: 0.7710825800895691 Discriminator loss: 1.2974132299423218 \n","Time for epoch 148 is 24.521302461624146 sec\n","Generation loss: 0.8325066566467285 Discriminator loss: 1.305307388305664 \n","Time for epoch 149 is 24.480483055114746 sec\n","Generation loss: 0.7784134745597839 Discriminator loss: 1.3025765419006348 \n","Time for epoch 150 is 24.470463037490845 sec\n","Generation loss: 0.7677368521690369 Discriminator loss: 1.3213069438934326 \n","Time for epoch 151 is 24.48709988594055 sec\n","Generation loss: 0.8070467114448547 Discriminator loss: 1.3239383697509766 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 152 is 24.496061325073242 sec\n","Generation loss: 0.7439669370651245 Discriminator loss: 1.304840087890625 \n","Time for epoch 153 is 24.550009727478027 sec\n","Generation loss: 0.8081341981887817 Discriminator loss: 1.2167924642562866 \n","Time for epoch 154 is 24.497434616088867 sec\n","Generation loss: 0.7575810551643372 Discriminator loss: 1.289161205291748 \n","Time for epoch 155 is 24.539353847503662 sec\n","Generation loss: 0.8438602089881897 Discriminator loss: 1.2899065017700195 \n","Time for epoch 156 is 24.567981243133545 sec\n","Generation loss: 0.8591282963752747 Discriminator loss: 1.1968958377838135 \n","Time for epoch 157 is 24.448797702789307 sec\n","Generation loss: 0.8476308584213257 Discriminator loss: 1.2093795537948608 \n","Time for epoch 158 is 24.541842222213745 sec\n","Generation loss: 0.7501559257507324 Discriminator loss: 1.3892323970794678 \n","Time for epoch 159 is 24.46267056465149 sec\n","Generation loss: 0.8057841062545776 Discriminator loss: 1.320380449295044 \n","Time for epoch 160 is 24.487852334976196 sec\n","Generation loss: 0.7609071731567383 Discriminator loss: 1.3282837867736816 \n","Time for epoch 161 is 24.460469484329224 sec\n","Generation loss: 0.7432620525360107 Discriminator loss: 1.3309228420257568 \n","Time for epoch 162 is 24.49966835975647 sec\n","Generation loss: 0.7922467589378357 Discriminator loss: 1.3085691928863525 \n","Time for epoch 163 is 24.45621371269226 sec\n","Generation loss: 0.7808468341827393 Discriminator loss: 1.2867486476898193 \n","Time for epoch 164 is 24.46988296508789 sec\n","Generation loss: 0.7800207138061523 Discriminator loss: 1.3023037910461426 \n","Time for epoch 165 is 24.450903177261353 sec\n","Generation loss: 0.7340496182441711 Discriminator loss: 1.311169147491455 \n","Time for epoch 166 is 24.583796501159668 sec\n","Generation loss: 0.7280556559562683 Discriminator loss: 1.2696386575698853 \n","Time for epoch 167 is 24.441932439804077 sec\n","Generation loss: 0.7603515982627869 Discriminator loss: 1.3308804035186768 \n","Time for epoch 168 is 24.528534650802612 sec\n","Generation loss: 0.7579612731933594 Discriminator loss: 1.3616752624511719 \n","Time for epoch 169 is 24.466986656188965 sec\n","Generation loss: 0.8343456387519836 Discriminator loss: 1.270925521850586 \n","Time for epoch 170 is 24.515615463256836 sec\n","Generation loss: 0.8008803725242615 Discriminator loss: 1.32772696018219 \n","Time for epoch 171 is 24.500274896621704 sec\n","Generation loss: 0.7808522582054138 Discriminator loss: 1.3067717552185059 \n","Time for epoch 172 is 24.612038612365723 sec\n","Generation loss: 0.7957351803779602 Discriminator loss: 1.291454553604126 \n","Time for epoch 173 is 24.48570203781128 sec\n","Generation loss: 0.7972090244293213 Discriminator loss: 1.3377704620361328 \n","Time for epoch 174 is 24.490978479385376 sec\n","Generation loss: 0.8027175664901733 Discriminator loss: 1.3280333280563354 \n","Time for epoch 175 is 24.485212326049805 sec\n","Generation loss: 0.751059889793396 Discriminator loss: 1.359420657157898 \n","Time for epoch 176 is 24.496843099594116 sec\n","Generation loss: 0.7502275109291077 Discriminator loss: 1.369147539138794 \n","Time for epoch 177 is 24.457528114318848 sec\n","Generation loss: 0.7952739596366882 Discriminator loss: 1.3539133071899414 \n","Time for epoch 178 is 24.51815366744995 sec\n","Generation loss: 0.817068874835968 Discriminator loss: 1.372879981994629 \n","Time for epoch 179 is 24.627511024475098 sec\n","Generation loss: 0.8427351117134094 Discriminator loss: 1.3629956245422363 \n","Time for epoch 180 is 24.5127272605896 sec\n","Generation loss: 0.7076509594917297 Discriminator loss: 1.3558818101882935 \n","Time for epoch 181 is 24.503958463668823 sec\n","Generation loss: 0.851314902305603 Discriminator loss: 1.2736668586730957 \n","Time for epoch 182 is 24.532111644744873 sec\n","Generation loss: 0.7692895531654358 Discriminator loss: 1.3009556531906128 \n","Time for epoch 183 is 24.503074407577515 sec\n","Generation loss: 0.7465656399726868 Discriminator loss: 1.3322923183441162 \n","Time for epoch 184 is 24.509631156921387 sec\n","Generation loss: 0.7555790543556213 Discriminator loss: 1.3308987617492676 \n","Time for epoch 185 is 24.448325395584106 sec\n","Generation loss: 0.7969484329223633 Discriminator loss: 1.3565884828567505 \n","Time for epoch 186 is 24.463475227355957 sec\n","Generation loss: 0.8155555725097656 Discriminator loss: 1.3420491218566895 \n","Time for epoch 187 is 24.458629846572876 sec\n","Generation loss: 0.7959660291671753 Discriminator loss: 1.3450920581817627 \n","Time for epoch 188 is 24.518321752548218 sec\n","Generation loss: 0.8282254338264465 Discriminator loss: 1.3018996715545654 \n","Time for epoch 189 is 24.471835374832153 sec\n","Generation loss: 0.746714174747467 Discriminator loss: 1.312167763710022 \n","Time for epoch 190 is 24.491933822631836 sec\n","Generation loss: 0.8397387862205505 Discriminator loss: 1.2564241886138916 \n","Time for epoch 191 is 24.444560289382935 sec\n","Generation loss: 0.7649890184402466 Discriminator loss: 1.3655805587768555 \n","Time for epoch 192 is 24.549025535583496 sec\n","Generation loss: 0.7975021600723267 Discriminator loss: 1.3065714836120605 \n","Time for epoch 193 is 24.44478154182434 sec\n","Generation loss: 0.8067450523376465 Discriminator loss: 1.3107542991638184 \n","Time for epoch 194 is 24.507072925567627 sec\n","Generation loss: 0.8059261441230774 Discriminator loss: 1.3354778289794922 \n","Time for epoch 195 is 24.46766471862793 sec\n","Generation loss: 0.7888267636299133 Discriminator loss: 1.277311086654663 \n","Time for epoch 196 is 24.44878649711609 sec\n","Generation loss: 0.7670572996139526 Discriminator loss: 1.3385026454925537 \n","Time for epoch 197 is 24.428608417510986 sec\n","Generation loss: 0.747901201248169 Discriminator loss: 1.308769941329956 \n","Time for epoch 198 is 24.46136236190796 sec\n","Generation loss: 0.6836181282997131 Discriminator loss: 1.411446213722229 \n","Time for epoch 199 is 24.500900745391846 sec\n","Generation loss: 0.6998243927955627 Discriminator loss: 1.370356559753418 \n","Time for epoch 200 is 24.530999422073364 sec\n","Generation loss: 0.8226482272148132 Discriminator loss: 1.2270855903625488 \n","Time for epoch 201 is 24.50392436981201 sec\n","Generation loss: 0.8113864660263062 Discriminator loss: 1.3372513055801392 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 202 is 24.512405157089233 sec\n","Generation loss: 0.802540123462677 Discriminator loss: 1.3153631687164307 \n","Time for epoch 203 is 24.490154504776 sec\n","Generation loss: 0.7805524468421936 Discriminator loss: 1.3142651319503784 \n","Time for epoch 204 is 24.533459901809692 sec\n","Generation loss: 0.7791545987129211 Discriminator loss: 1.3198546171188354 \n","Time for epoch 205 is 24.50806212425232 sec\n","Generation loss: 0.7696168422698975 Discriminator loss: 1.3223118782043457 \n","Time for epoch 206 is 24.507156133651733 sec\n","Generation loss: 0.791472852230072 Discriminator loss: 1.30120050907135 \n","Time for epoch 207 is 24.428053617477417 sec\n","Generation loss: 0.7492176294326782 Discriminator loss: 1.394336462020874 \n","Time for epoch 208 is 24.48801040649414 sec\n","Generation loss: 0.7904000282287598 Discriminator loss: 1.4169443845748901 \n","Time for epoch 209 is 24.47591996192932 sec\n","Generation loss: 0.7985962629318237 Discriminator loss: 1.3406105041503906 \n","Time for epoch 210 is 24.51773715019226 sec\n","Generation loss: 0.7463551759719849 Discriminator loss: 1.3288578987121582 \n","Time for epoch 211 is 24.508028745651245 sec\n","Generation loss: 0.8011969923973083 Discriminator loss: 1.3648004531860352 \n","Time for epoch 212 is 24.495120525360107 sec\n","Generation loss: 0.7929852604866028 Discriminator loss: 1.3227864503860474 \n","Time for epoch 213 is 24.470157861709595 sec\n","Generation loss: 0.7320003509521484 Discriminator loss: 1.3548126220703125 \n","Time for epoch 214 is 24.48240613937378 sec\n","Generation loss: 0.813105583190918 Discriminator loss: 1.2991182804107666 \n","Time for epoch 215 is 24.438751220703125 sec\n","Generation loss: 0.8007931709289551 Discriminator loss: 1.3559205532073975 \n","Time for epoch 216 is 24.447020769119263 sec\n","Generation loss: 0.8002642393112183 Discriminator loss: 1.289638638496399 \n","Time for epoch 217 is 24.409700632095337 sec\n","Generation loss: 0.7703308463096619 Discriminator loss: 1.357222080230713 \n","Time for epoch 218 is 24.466859579086304 sec\n","Generation loss: 0.8298947215080261 Discriminator loss: 1.2890467643737793 \n","Time for epoch 219 is 24.414762496948242 sec\n","Generation loss: 0.7729052901268005 Discriminator loss: 1.3043663501739502 \n","Time for epoch 220 is 24.471320867538452 sec\n","Generation loss: 0.763283371925354 Discriminator loss: 1.4123293161392212 \n","Time for epoch 221 is 24.457862615585327 sec\n","Generation loss: 0.7866536378860474 Discriminator loss: 1.312860131263733 \n","Time for epoch 222 is 24.493226528167725 sec\n","Generation loss: 0.7790175676345825 Discriminator loss: 1.3144127130508423 \n","Time for epoch 223 is 24.420541286468506 sec\n","Generation loss: 0.7747334837913513 Discriminator loss: 1.353714942932129 \n","Time for epoch 224 is 24.449775457382202 sec\n","Generation loss: 0.7396383285522461 Discriminator loss: 1.3171172142028809 \n","Time for epoch 225 is 24.451947689056396 sec\n","Generation loss: 0.7583518028259277 Discriminator loss: 1.3022632598876953 \n","Time for epoch 226 is 24.531347274780273 sec\n","Generation loss: 0.7713398933410645 Discriminator loss: 1.3095911741256714 \n","Time for epoch 227 is 24.4526207447052 sec\n","Generation loss: 0.790347695350647 Discriminator loss: 1.375927209854126 \n","Time for epoch 228 is 24.472281217575073 sec\n","Generation loss: 0.7920627593994141 Discriminator loss: 1.3440945148468018 \n","Time for epoch 229 is 24.457204818725586 sec\n","Generation loss: 0.7758897542953491 Discriminator loss: 1.363959789276123 \n","Time for epoch 230 is 24.514477014541626 sec\n","Generation loss: 0.837209939956665 Discriminator loss: 1.263372778892517 \n","Time for epoch 231 is 24.456986665725708 sec\n","Generation loss: 0.7862489223480225 Discriminator loss: 1.290061593055725 \n","Time for epoch 232 is 24.516018390655518 sec\n","Generation loss: 0.8054782748222351 Discriminator loss: 1.309919834136963 \n","Time for epoch 233 is 24.471595764160156 sec\n","Generation loss: 0.7712109684944153 Discriminator loss: 1.3463685512542725 \n","Time for epoch 234 is 24.523065328598022 sec\n","Generation loss: 0.7707845568656921 Discriminator loss: 1.3655365705490112 \n","Time for epoch 235 is 24.467938661575317 sec\n","Generation loss: 0.7755374908447266 Discriminator loss: 1.3224191665649414 \n","Time for epoch 236 is 24.49181818962097 sec\n","Generation loss: 0.809245228767395 Discriminator loss: 1.3066089153289795 \n","Time for epoch 237 is 24.465851545333862 sec\n","Generation loss: 0.8323447704315186 Discriminator loss: 1.238724708557129 \n","Time for epoch 238 is 24.51685070991516 sec\n","Generation loss: 0.8584349155426025 Discriminator loss: 1.248380422592163 \n","Time for epoch 239 is 24.445963144302368 sec\n","Generation loss: 0.7745246291160583 Discriminator loss: 1.3592050075531006 \n","Time for epoch 240 is 24.50289535522461 sec\n","Generation loss: 0.814599335193634 Discriminator loss: 1.2502022981643677 \n","Time for epoch 241 is 24.466047048568726 sec\n","Generation loss: 0.7765647768974304 Discriminator loss: 1.31475830078125 \n","Time for epoch 242 is 24.516192197799683 sec\n","Generation loss: 0.7735565900802612 Discriminator loss: 1.3170218467712402 \n","Time for epoch 243 is 24.496569633483887 sec\n","Generation loss: 0.7849228978157043 Discriminator loss: 1.3413493633270264 \n","Time for epoch 244 is 24.461281299591064 sec\n","Generation loss: 0.7807320356369019 Discriminator loss: 1.283350944519043 \n","Time for epoch 245 is 24.470725774765015 sec\n","Generation loss: 0.7910820245742798 Discriminator loss: 1.3141552209854126 \n","Time for epoch 246 is 24.539135456085205 sec\n","Generation loss: 0.7449169158935547 Discriminator loss: 1.3648264408111572 \n","Time for epoch 247 is 24.484192371368408 sec\n","Generation loss: 0.7661564350128174 Discriminator loss: 1.3213138580322266 \n","Time for epoch 248 is 24.48186731338501 sec\n","Generation loss: 0.7555742263793945 Discriminator loss: 1.3628568649291992 \n","Time for epoch 249 is 24.488481760025024 sec\n","Generation loss: 0.7783385515213013 Discriminator loss: 1.2760968208312988 \n","Time for epoch 250 is 24.560344219207764 sec\n","Generation loss: 0.777840793132782 Discriminator loss: 1.3282067775726318 \n","Time for epoch 251 is 24.55423092842102 sec\n","Generation loss: 0.7024417519569397 Discriminator loss: 1.354637861251831 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 252 is 24.46016216278076 sec\n","Generation loss: 0.8115921020507812 Discriminator loss: 1.3527393341064453 \n","Time for epoch 253 is 24.43576407432556 sec\n","Generation loss: 0.7850292325019836 Discriminator loss: 1.362483263015747 \n","Time for epoch 254 is 24.4618923664093 sec\n","Generation loss: 0.8040763139724731 Discriminator loss: 1.332005262374878 \n","Time for epoch 255 is 41.05716276168823 sec\n","Generation loss: 0.7590336799621582 Discriminator loss: 1.3223884105682373 \n","Time for epoch 256 is 24.563459873199463 sec\n","Generation loss: 0.7952825427055359 Discriminator loss: 1.309762716293335 \n","Time for epoch 257 is 24.424793481826782 sec\n","Generation loss: 0.772674560546875 Discriminator loss: 1.2845969200134277 \n","Time for epoch 258 is 24.511099815368652 sec\n","Generation loss: 0.7385253310203552 Discriminator loss: 1.3553811311721802 \n","Time for epoch 259 is 24.54507279396057 sec\n","Generation loss: 0.7645640969276428 Discriminator loss: 1.3315093517303467 \n","Time for epoch 260 is 24.585317611694336 sec\n","Generation loss: 0.817487895488739 Discriminator loss: 1.3443188667297363 \n","Time for epoch 261 is 24.473543643951416 sec\n","Generation loss: 0.8335644006729126 Discriminator loss: 1.2557804584503174 \n","Time for epoch 262 is 24.50747299194336 sec\n","Generation loss: 0.8303669691085815 Discriminator loss: 1.2376296520233154 \n","Time for epoch 263 is 24.547510385513306 sec\n","Generation loss: 0.770108699798584 Discriminator loss: 1.320708990097046 \n","Time for epoch 264 is 24.533612728118896 sec\n","Generation loss: 0.76296067237854 Discriminator loss: 1.3615069389343262 \n","Time for epoch 265 is 24.499951124191284 sec\n","Generation loss: 0.7540722489356995 Discriminator loss: 1.3524861335754395 \n","Time for epoch 266 is 24.46340012550354 sec\n","Generation loss: 0.7727736830711365 Discriminator loss: 1.3234102725982666 \n","Time for epoch 267 is 24.47652816772461 sec\n","Generation loss: 0.7752012610435486 Discriminator loss: 1.359471082687378 \n","Time for epoch 268 is 24.46798610687256 sec\n","Generation loss: 0.7746005058288574 Discriminator loss: 1.3786046504974365 \n","Time for epoch 269 is 24.498811721801758 sec\n","Generation loss: 0.8060409426689148 Discriminator loss: 1.3177299499511719 \n","Time for epoch 270 is 24.495225429534912 sec\n","Generation loss: 0.7776809930801392 Discriminator loss: 1.3516254425048828 \n","Time for epoch 271 is 24.409459590911865 sec\n","Generation loss: 0.8144035339355469 Discriminator loss: 1.3289990425109863 \n","Time for epoch 272 is 24.465409755706787 sec\n","Generation loss: 0.748294472694397 Discriminator loss: 1.3093011379241943 \n","Time for epoch 273 is 24.47139072418213 sec\n","Generation loss: 0.7525874376296997 Discriminator loss: 1.310394525527954 \n","Time for epoch 274 is 24.47266411781311 sec\n","Generation loss: 0.7860990166664124 Discriminator loss: 1.3620214462280273 \n","Time for epoch 275 is 24.4711332321167 sec\n","Generation loss: 0.7532426118850708 Discriminator loss: 1.318610668182373 \n","Time for epoch 276 is 24.47342872619629 sec\n","Generation loss: 0.7862684726715088 Discriminator loss: 1.2617887258529663 \n","Time for epoch 277 is 24.458362579345703 sec\n","Generation loss: 0.8334124088287354 Discriminator loss: 1.267406940460205 \n","Time for epoch 278 is 24.47628355026245 sec\n","Generation loss: 0.7573502063751221 Discriminator loss: 1.3130419254302979 \n","Time for epoch 279 is 24.44850492477417 sec\n","Generation loss: 0.7390453815460205 Discriminator loss: 1.3832273483276367 \n","Time for epoch 280 is 24.487720489501953 sec\n","Generation loss: 0.838293194770813 Discriminator loss: 1.2945127487182617 \n","Time for epoch 281 is 24.465192317962646 sec\n","Generation loss: 0.7707160115242004 Discriminator loss: 1.3234375715255737 \n","Time for epoch 282 is 24.488004207611084 sec\n","Generation loss: 0.7797941565513611 Discriminator loss: 1.291435956954956 \n","Time for epoch 283 is 24.446231842041016 sec\n","Generation loss: 0.7996888160705566 Discriminator loss: 1.3550766706466675 \n","Time for epoch 284 is 24.524283170700073 sec\n","Generation loss: 0.7808994054794312 Discriminator loss: 1.415310025215149 \n","Time for epoch 285 is 24.46858859062195 sec\n","Generation loss: 0.8215920925140381 Discriminator loss: 1.3168928623199463 \n","Time for epoch 286 is 24.501280546188354 sec\n","Generation loss: 0.7535834908485413 Discriminator loss: 1.3207511901855469 \n","Time for epoch 287 is 24.441818714141846 sec\n","Generation loss: 0.7662899494171143 Discriminator loss: 1.2799787521362305 \n","Time for epoch 288 is 24.49834394454956 sec\n","Generation loss: 0.7284977436065674 Discriminator loss: 1.3215293884277344 \n","Time for epoch 289 is 24.464258670806885 sec\n","Generation loss: 0.7491204142570496 Discriminator loss: 1.348465919494629 \n","Time for epoch 290 is 24.560245037078857 sec\n","Generation loss: 0.7669966220855713 Discriminator loss: 1.3280974626541138 \n","Time for epoch 291 is 24.516597986221313 sec\n","Generation loss: 0.7133610248565674 Discriminator loss: 1.3382322788238525 \n","Time for epoch 292 is 24.544875621795654 sec\n","Generation loss: 0.7562480568885803 Discriminator loss: 1.310820460319519 \n","Time for epoch 293 is 24.479979991912842 sec\n","Generation loss: 0.7403244972229004 Discriminator loss: 1.3055341243743896 \n","Time for epoch 294 is 24.463081121444702 sec\n","Generation loss: 0.7420634031295776 Discriminator loss: 1.3117544651031494 \n","Time for epoch 295 is 24.498846769332886 sec\n","Generation loss: 0.7877453565597534 Discriminator loss: 1.3319807052612305 \n","Time for epoch 296 is 24.512381553649902 sec\n","Generation loss: 0.7824467420578003 Discriminator loss: 1.3273329734802246 \n","Time for epoch 297 is 24.470025777816772 sec\n","Generation loss: 0.7598770260810852 Discriminator loss: 1.4018886089324951 \n","Time for epoch 298 is 24.563324213027954 sec\n","Generation loss: 0.7052342891693115 Discriminator loss: 1.3734445571899414 \n","Time for epoch 299 is 24.517759084701538 sec\n","Generation loss: 0.7684036493301392 Discriminator loss: 1.330989956855774 \n","Time for epoch 300 is 24.559585332870483 sec\n","Generation loss: 0.7676312923431396 Discriminator loss: 1.3242571353912354 \n","Time for epoch 301 is 24.48384189605713 sec\n","Generation loss: 0.803469181060791 Discriminator loss: 1.335890769958496 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 302 is 24.513395309448242 sec\n","Generation loss: 0.7263918519020081 Discriminator loss: 1.3236773014068604 \n","Time for epoch 303 is 24.51258397102356 sec\n","Generation loss: 0.7892774343490601 Discriminator loss: 1.284407615661621 \n","Time for epoch 304 is 24.54680848121643 sec\n","Generation loss: 0.8021569848060608 Discriminator loss: 1.322896957397461 \n","Time for epoch 305 is 24.487064361572266 sec\n","Generation loss: 0.8137226104736328 Discriminator loss: 1.2913296222686768 \n","Time for epoch 306 is 24.58954167366028 sec\n","Generation loss: 0.766356348991394 Discriminator loss: 1.3354449272155762 \n","Time for epoch 307 is 24.549226760864258 sec\n","Generation loss: 0.7207299470901489 Discriminator loss: 1.3581972122192383 \n","Time for epoch 308 is 24.540919065475464 sec\n","Generation loss: 0.7826105356216431 Discriminator loss: 1.3327255249023438 \n","Time for epoch 309 is 24.52858066558838 sec\n","Generation loss: 0.751992404460907 Discriminator loss: 1.3245291709899902 \n","Time for epoch 310 is 24.529850721359253 sec\n","Generation loss: 0.7810148596763611 Discriminator loss: 1.315626621246338 \n","Time for epoch 311 is 24.472437620162964 sec\n","Generation loss: 0.7220227718353271 Discriminator loss: 1.3453238010406494 \n","Time for epoch 312 is 24.50229263305664 sec\n","Generation loss: 0.7503315210342407 Discriminator loss: 1.3102400302886963 \n","Time for epoch 313 is 24.48906660079956 sec\n","Generation loss: 0.7751749753952026 Discriminator loss: 1.3256489038467407 \n","Time for epoch 314 is 24.550065279006958 sec\n","Generation loss: 0.7219992876052856 Discriminator loss: 1.3314728736877441 \n","Time for epoch 315 is 24.46906065940857 sec\n","Generation loss: 0.7505029439926147 Discriminator loss: 1.337733268737793 \n","Time for epoch 316 is 24.539453983306885 sec\n","Generation loss: 0.7562379837036133 Discriminator loss: 1.369011402130127 \n","Time for epoch 317 is 24.497405529022217 sec\n","Generation loss: 0.7190556526184082 Discriminator loss: 1.3612711429595947 \n","Time for epoch 318 is 24.52399492263794 sec\n","Generation loss: 0.7109450697898865 Discriminator loss: 1.345491647720337 \n","Time for epoch 319 is 24.50186586380005 sec\n","Generation loss: 0.7440180778503418 Discriminator loss: 1.3728199005126953 \n","Time for epoch 320 is 24.512211084365845 sec\n","Generation loss: 0.7275053858757019 Discriminator loss: 1.3458867073059082 \n","Time for epoch 321 is 24.49853253364563 sec\n","Generation loss: 0.7224164605140686 Discriminator loss: 1.3734877109527588 \n","Time for epoch 322 is 24.515668153762817 sec\n","Generation loss: 0.7450123429298401 Discriminator loss: 1.365458369255066 \n","Time for epoch 323 is 24.44871497154236 sec\n","Generation loss: 0.742874264717102 Discriminator loss: 1.342266321182251 \n","Time for epoch 324 is 24.522379398345947 sec\n","Generation loss: 0.7403964996337891 Discriminator loss: 1.3505500555038452 \n","Time for epoch 325 is 24.4699490070343 sec\n","Generation loss: 0.7624081969261169 Discriminator loss: 1.3068842887878418 \n","Time for epoch 326 is 24.65036702156067 sec\n","Generation loss: 0.7944014072418213 Discriminator loss: 1.2877305746078491 \n","Time for epoch 327 is 24.493208408355713 sec\n","Generation loss: 0.775254487991333 Discriminator loss: 1.3325519561767578 \n","Time for epoch 328 is 24.504790544509888 sec\n","Generation loss: 0.7467108368873596 Discriminator loss: 1.325413465499878 \n","Time for epoch 329 is 24.58145570755005 sec\n","Generation loss: 0.7728742361068726 Discriminator loss: 1.3517844676971436 \n","Time for epoch 330 is 24.562697887420654 sec\n","Generation loss: 0.8021385669708252 Discriminator loss: 1.3129377365112305 \n","Time for epoch 331 is 24.473212242126465 sec\n","Generation loss: 0.7196252942085266 Discriminator loss: 1.3040270805358887 \n","Time for epoch 332 is 24.645320177078247 sec\n","Generation loss: 0.6977400779724121 Discriminator loss: 1.3293437957763672 \n","Time for epoch 333 is 24.52852201461792 sec\n","Generation loss: 0.7341421842575073 Discriminator loss: 1.3264000415802002 \n","Time for epoch 334 is 24.554848194122314 sec\n","Generation loss: 0.7230408191680908 Discriminator loss: 1.3517277240753174 \n","Time for epoch 335 is 24.4899001121521 sec\n","Generation loss: 0.7749359607696533 Discriminator loss: 1.3691551685333252 \n","Time for epoch 336 is 24.491411924362183 sec\n","Generation loss: 0.7567380666732788 Discriminator loss: 1.3288624286651611 \n","Time for epoch 337 is 24.47358250617981 sec\n","Generation loss: 0.7527443766593933 Discriminator loss: 1.3728368282318115 \n","Time for epoch 338 is 24.60262107849121 sec\n","Generation loss: 0.8087466955184937 Discriminator loss: 1.335808515548706 \n","Time for epoch 339 is 24.54647445678711 sec\n","Generation loss: 0.7722442746162415 Discriminator loss: 1.362661361694336 \n","Time for epoch 340 is 24.55786681175232 sec\n","Generation loss: 0.7642118334770203 Discriminator loss: 1.2799804210662842 \n","Time for epoch 341 is 24.493184089660645 sec\n","Generation loss: 0.7615779638290405 Discriminator loss: 1.3550214767456055 \n","Time for epoch 342 is 24.535415410995483 sec\n","Generation loss: 0.8270506858825684 Discriminator loss: 1.2950005531311035 \n","Time for epoch 343 is 24.543858289718628 sec\n","Generation loss: 0.7796175479888916 Discriminator loss: 1.3697223663330078 \n","Time for epoch 344 is 24.488590478897095 sec\n","Generation loss: 0.7686281204223633 Discriminator loss: 1.3392671346664429 \n","Time for epoch 345 is 24.46286702156067 sec\n","Generation loss: 0.7596101760864258 Discriminator loss: 1.3307292461395264 \n","Time for epoch 346 is 24.511518001556396 sec\n","Generation loss: 0.8004504442214966 Discriminator loss: 1.3058207035064697 \n","Time for epoch 347 is 24.464295625686646 sec\n","Generation loss: 0.7419743537902832 Discriminator loss: 1.2924836874008179 \n","Time for epoch 348 is 24.59786319732666 sec\n","Generation loss: 0.7697052359580994 Discriminator loss: 1.3055124282836914 \n","Time for epoch 349 is 24.533367395401 sec\n","Generation loss: 0.7569102644920349 Discriminator loss: 1.3192675113677979 \n","Time for epoch 350 is 24.49350333213806 sec\n","Generation loss: 0.7713230848312378 Discriminator loss: 1.3037402629852295 \n","Time for epoch 351 is 24.475215196609497 sec\n","Generation loss: 0.751042902469635 Discriminator loss: 1.3108046054840088 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 352 is 24.560709476470947 sec\n","Generation loss: 0.7552134394645691 Discriminator loss: 1.3530843257904053 \n","Time for epoch 353 is 24.516741275787354 sec\n","Generation loss: 0.7627155780792236 Discriminator loss: 1.308812141418457 \n","Time for epoch 354 is 24.57493567466736 sec\n","Generation loss: 0.7560704350471497 Discriminator loss: 1.3611843585968018 \n","Time for epoch 355 is 24.51163959503174 sec\n","Generation loss: 0.7664605379104614 Discriminator loss: 1.3177344799041748 \n","Time for epoch 356 is 24.529021501541138 sec\n","Generation loss: 0.7505585551261902 Discriminator loss: 1.3493033647537231 \n","Time for epoch 357 is 24.503026485443115 sec\n","Generation loss: 0.7887512445449829 Discriminator loss: 1.3528770208358765 \n","Time for epoch 358 is 24.528618812561035 sec\n","Generation loss: 0.7352207899093628 Discriminator loss: 1.351332426071167 \n","Time for epoch 359 is 24.506425380706787 sec\n","Generation loss: 0.7384783625602722 Discriminator loss: 1.3740441799163818 \n","Time for epoch 360 is 24.490926504135132 sec\n","Generation loss: 0.7713828086853027 Discriminator loss: 1.345560073852539 \n","Time for epoch 361 is 24.5067720413208 sec\n","Generation loss: 0.7212086915969849 Discriminator loss: 1.3371597528457642 \n","Time for epoch 362 is 24.52014470100403 sec\n","Generation loss: 0.6974353194236755 Discriminator loss: 1.3940894603729248 \n","Time for epoch 363 is 24.47814440727234 sec\n","Generation loss: 0.799497663974762 Discriminator loss: 1.3237090110778809 \n","Time for epoch 364 is 24.506721019744873 sec\n","Generation loss: 0.7680253982543945 Discriminator loss: 1.376603603363037 \n","Time for epoch 365 is 24.625914096832275 sec\n","Generation loss: 0.7803356647491455 Discriminator loss: 1.3488085269927979 \n","Time for epoch 366 is 24.508142471313477 sec\n","Generation loss: 0.7955958843231201 Discriminator loss: 1.333376407623291 \n","Time for epoch 367 is 24.49806833267212 sec\n","Generation loss: 0.7778410315513611 Discriminator loss: 1.363412857055664 \n","Time for epoch 368 is 24.509342670440674 sec\n","Generation loss: 0.7843010425567627 Discriminator loss: 1.352034091949463 \n","Time for epoch 369 is 24.52258801460266 sec\n","Generation loss: 0.7890852093696594 Discriminator loss: 1.3351619243621826 \n","Time for epoch 370 is 24.539470672607422 sec\n","Generation loss: 0.7732598781585693 Discriminator loss: 1.3252100944519043 \n","Time for epoch 371 is 24.473632335662842 sec\n","Generation loss: 0.7381104230880737 Discriminator loss: 1.3631908893585205 \n","Time for epoch 372 is 24.578343629837036 sec\n","Generation loss: 0.7038632035255432 Discriminator loss: 1.3495067358016968 \n","Time for epoch 373 is 24.540990829467773 sec\n","Generation loss: 0.7404563426971436 Discriminator loss: 1.3163042068481445 \n","Time for epoch 374 is 24.521172523498535 sec\n","Generation loss: 0.7370660901069641 Discriminator loss: 1.3644579648971558 \n","Time for epoch 375 is 24.501486778259277 sec\n","Generation loss: 0.8023138046264648 Discriminator loss: 1.305422067642212 \n","Time for epoch 376 is 24.501615285873413 sec\n","Generation loss: 0.7492945194244385 Discriminator loss: 1.281028389930725 \n","Time for epoch 377 is 24.5381977558136 sec\n","Generation loss: 0.7978817224502563 Discriminator loss: 1.3064403533935547 \n","Time for epoch 378 is 24.550124406814575 sec\n","Generation loss: 0.7653319239616394 Discriminator loss: 1.3255589008331299 \n","Time for epoch 379 is 24.560760736465454 sec\n","Generation loss: 0.8109484314918518 Discriminator loss: 1.310727596282959 \n","Time for epoch 380 is 24.536505937576294 sec\n","Generation loss: 0.7592087984085083 Discriminator loss: 1.35103178024292 \n","Time for epoch 381 is 24.496495008468628 sec\n","Generation loss: 0.7928808331489563 Discriminator loss: 1.3185057640075684 \n","Time for epoch 382 is 24.503204822540283 sec\n","Generation loss: 0.7203277349472046 Discriminator loss: 1.3437387943267822 \n","Time for epoch 383 is 24.516911029815674 sec\n","Generation loss: 0.7898796796798706 Discriminator loss: 1.3604551553726196 \n","Time for epoch 384 is 24.53609871864319 sec\n","Generation loss: 0.781408965587616 Discriminator loss: 1.324009656906128 \n","Time for epoch 385 is 24.731467962265015 sec\n","Generation loss: 0.7680639028549194 Discriminator loss: 1.3036308288574219 \n","Time for epoch 386 is 24.899643898010254 sec\n","Generation loss: 0.74710613489151 Discriminator loss: 1.338552474975586 \n","Time for epoch 387 is 25.024574041366577 sec\n","Generation loss: 0.8031194806098938 Discriminator loss: 1.3785219192504883 \n","Time for epoch 388 is 24.986117601394653 sec\n","Generation loss: 0.7761115431785583 Discriminator loss: 1.3642313480377197 \n","Time for epoch 389 is 25.019694566726685 sec\n","Generation loss: 0.745720624923706 Discriminator loss: 1.3031411170959473 \n","Time for epoch 390 is 25.03110909461975 sec\n","Generation loss: 0.766453206539154 Discriminator loss: 1.3438494205474854 \n","Time for epoch 391 is 24.950844049453735 sec\n","Generation loss: 0.7657761573791504 Discriminator loss: 1.3245892524719238 \n","Time for epoch 392 is 24.95159125328064 sec\n","Generation loss: 0.7482418417930603 Discriminator loss: 1.3630043268203735 \n","Time for epoch 393 is 24.893694162368774 sec\n","Generation loss: 0.7839578986167908 Discriminator loss: 1.3810877799987793 \n","Time for epoch 394 is 24.89831519126892 sec\n","Generation loss: 0.8362318277359009 Discriminator loss: 1.347916603088379 \n","Time for epoch 395 is 24.859411001205444 sec\n","Generation loss: 0.7855626344680786 Discriminator loss: 1.3020832538604736 \n","Time for epoch 396 is 24.884838581085205 sec\n","Generation loss: 0.793724000453949 Discriminator loss: 1.3694632053375244 \n","Time for epoch 397 is 24.665853261947632 sec\n","Generation loss: 0.7479003667831421 Discriminator loss: 1.322821855545044 \n","Time for epoch 398 is 24.678082942962646 sec\n","Generation loss: 0.7426913976669312 Discriminator loss: 1.3320623636245728 \n","Time for epoch 399 is 24.70089626312256 sec\n","Generation loss: 0.7467111349105835 Discriminator loss: 1.3166948556900024 \n","Time for epoch 400 is 24.88948369026184 sec\n","Generation loss: 0.734264612197876 Discriminator loss: 1.4098997116088867 \n","Time for epoch 401 is 24.927541732788086 sec\n","Generation loss: 0.7718877792358398 Discriminator loss: 1.3362716436386108 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 402 is 24.940722227096558 sec\n","Generation loss: 0.706706166267395 Discriminator loss: 1.3289515972137451 \n","Time for epoch 403 is 24.917966842651367 sec\n","Generation loss: 0.7212492823600769 Discriminator loss: 1.3507080078125 \n","Time for epoch 404 is 24.96481227874756 sec\n","Generation loss: 0.791990339756012 Discriminator loss: 1.3135771751403809 \n","Time for epoch 405 is 24.90977144241333 sec\n","Generation loss: 0.7847706079483032 Discriminator loss: 1.3312385082244873 \n","Time for epoch 406 is 24.946433305740356 sec\n","Generation loss: 0.7637070417404175 Discriminator loss: 1.3358089923858643 \n","Time for epoch 407 is 24.89688205718994 sec\n","Generation loss: 0.7494559288024902 Discriminator loss: 1.3103641271591187 \n","Time for epoch 408 is 24.949168920516968 sec\n","Generation loss: 0.7686930894851685 Discriminator loss: 1.3720426559448242 \n","Time for epoch 409 is 24.91986584663391 sec\n","Generation loss: 0.7453905940055847 Discriminator loss: 1.3253779411315918 \n","Time for epoch 410 is 24.936896562576294 sec\n","Generation loss: 0.7403931021690369 Discriminator loss: 1.3664734363555908 \n","Time for epoch 411 is 24.87442398071289 sec\n","Generation loss: 0.7832944989204407 Discriminator loss: 1.3388824462890625 \n","Time for epoch 412 is 24.842060327529907 sec\n","Generation loss: 0.7242297530174255 Discriminator loss: 1.3338813781738281 \n","Time for epoch 413 is 24.901682138442993 sec\n","Generation loss: 0.7162158489227295 Discriminator loss: 1.3485053777694702 \n","Time for epoch 414 is 24.87462854385376 sec\n","Generation loss: 0.7266085147857666 Discriminator loss: 1.3867617845535278 \n","Time for epoch 415 is 24.852637767791748 sec\n","Generation loss: 0.7603939175605774 Discriminator loss: 1.338069200515747 \n","Time for epoch 416 is 24.87403678894043 sec\n","Generation loss: 0.8036870956420898 Discriminator loss: 1.342732310295105 \n","Time for epoch 417 is 24.87051248550415 sec\n","Generation loss: 0.7333781719207764 Discriminator loss: 1.334528923034668 \n","Time for epoch 418 is 24.92102813720703 sec\n","Generation loss: 0.7624618411064148 Discriminator loss: 1.3067692518234253 \n","Time for epoch 419 is 24.873061180114746 sec\n","Generation loss: 0.761698305606842 Discriminator loss: 1.3369472026824951 \n","Time for epoch 420 is 24.873244285583496 sec\n","Generation loss: 0.7252195477485657 Discriminator loss: 1.303666353225708 \n","Time for epoch 421 is 24.862953424453735 sec\n","Generation loss: 0.7637083530426025 Discriminator loss: 1.3185479640960693 \n","Time for epoch 422 is 24.957804441452026 sec\n","Generation loss: 0.772246241569519 Discriminator loss: 1.3879444599151611 \n","Time for epoch 423 is 24.9188814163208 sec\n","Generation loss: 0.7794188261032104 Discriminator loss: 1.319930911064148 \n","Time for epoch 424 is 24.8863627910614 sec\n","Generation loss: 0.8059597015380859 Discriminator loss: 1.3616241216659546 \n","Time for epoch 425 is 24.930505752563477 sec\n","Generation loss: 0.7567393183708191 Discriminator loss: 1.3311675786972046 \n","Time for epoch 426 is 24.907459259033203 sec\n","Generation loss: 0.7650132179260254 Discriminator loss: 1.3377141952514648 \n","Time for epoch 427 is 24.901832580566406 sec\n","Generation loss: 0.7411371469497681 Discriminator loss: 1.3938419818878174 \n","Time for epoch 428 is 24.966795444488525 sec\n","Generation loss: 0.7844202518463135 Discriminator loss: 1.3785204887390137 \n","Time for epoch 429 is 24.90613079071045 sec\n","Generation loss: 0.7747340798377991 Discriminator loss: 1.3394439220428467 \n","Time for epoch 430 is 24.965364933013916 sec\n","Generation loss: 0.7410742044448853 Discriminator loss: 1.3289756774902344 \n","Time for epoch 431 is 24.91789412498474 sec\n","Generation loss: 0.7603656649589539 Discriminator loss: 1.334957242012024 \n","Time for epoch 432 is 24.92319345474243 sec\n","Generation loss: 0.7564300298690796 Discriminator loss: 1.3635282516479492 \n","Time for epoch 433 is 24.892793655395508 sec\n","Generation loss: 0.7331734299659729 Discriminator loss: 1.3424081802368164 \n","Time for epoch 434 is 24.907794713974 sec\n","Generation loss: 0.7963588833808899 Discriminator loss: 1.2961039543151855 \n","Time for epoch 435 is 24.929070234298706 sec\n","Generation loss: 0.7239902019500732 Discriminator loss: 1.3509925603866577 \n","Time for epoch 436 is 24.911559343338013 sec\n","Generation loss: 0.7648134827613831 Discriminator loss: 1.3386317491531372 \n","Time for epoch 437 is 24.915441274642944 sec\n","Generation loss: 0.7578941583633423 Discriminator loss: 1.3687140941619873 \n","Time for epoch 438 is 24.892089128494263 sec\n","Generation loss: 0.7495698928833008 Discriminator loss: 1.353898048400879 \n","Time for epoch 439 is 24.87825322151184 sec\n","Generation loss: 0.7570634484291077 Discriminator loss: 1.3575758934020996 \n","Time for epoch 440 is 24.870026111602783 sec\n","Generation loss: 0.7845218777656555 Discriminator loss: 1.3210976123809814 \n","Time for epoch 441 is 24.850615978240967 sec\n","Generation loss: 0.7461318373680115 Discriminator loss: 1.34228515625 \n","Time for epoch 442 is 24.884575128555298 sec\n","Generation loss: 0.7625430226325989 Discriminator loss: 1.3319387435913086 \n","Time for epoch 443 is 24.845666646957397 sec\n","Generation loss: 0.7740322351455688 Discriminator loss: 1.3222458362579346 \n","Time for epoch 444 is 24.91680359840393 sec\n","Generation loss: 0.8010061979293823 Discriminator loss: 1.3391823768615723 \n","Time for epoch 445 is 24.88520884513855 sec\n","Generation loss: 0.731659471988678 Discriminator loss: 1.364359974861145 \n","Time for epoch 446 is 24.856926441192627 sec\n","Generation loss: 0.7656227946281433 Discriminator loss: 1.3178327083587646 \n","Time for epoch 447 is 24.8668692111969 sec\n","Generation loss: 0.7082940936088562 Discriminator loss: 1.3540892601013184 \n","Time for epoch 448 is 24.897218465805054 sec\n","Generation loss: 0.7400314211845398 Discriminator loss: 1.3481149673461914 \n","Time for epoch 449 is 24.90571665763855 sec\n","Generation loss: 0.7692596912384033 Discriminator loss: 1.3372604846954346 \n","Time for epoch 450 is 24.942244291305542 sec\n","Generation loss: 0.7504469156265259 Discriminator loss: 1.3347716331481934 \n","Time for epoch 451 is 24.954148530960083 sec\n","Generation loss: 0.7331083416938782 Discriminator loss: 1.3675622940063477 \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 288x288 with 16 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Time for epoch 452 is 24.947983264923096 sec\n","Generation loss: 0.7768058776855469 Discriminator loss: 1.3569369316101074 \n","Time for epoch 453 is 24.859325885772705 sec\n","Generation loss: 0.759906530380249 Discriminator loss: 1.3336005210876465 \n","Time for epoch 454 is 24.992116928100586 sec\n","Generation loss: 0.7593214511871338 Discriminator loss: 1.3203644752502441 \n","Time for epoch 455 is 24.847782611846924 sec\n","Generation loss: 0.7881014347076416 Discriminator loss: 1.3442678451538086 \n","Time for epoch 456 is 24.90203619003296 sec\n","Generation loss: 0.7929800748825073 Discriminator loss: 1.3004456758499146 \n","Time for epoch 457 is 24.880066633224487 sec\n","Generation loss: 0.692004382610321 Discriminator loss: 1.3563193082809448 \n","Time for epoch 458 is 24.942036867141724 sec\n","Generation loss: 0.7612990140914917 Discriminator loss: 1.3366985321044922 \n","Time for epoch 459 is 24.908786296844482 sec\n","Generation loss: 0.7879543304443359 Discriminator loss: 1.3542158603668213 \n","Time for epoch 460 is 24.909693002700806 sec\n","Generation loss: 0.7668027281761169 Discriminator loss: 1.315340280532837 \n","Time for epoch 461 is 41.075966119766235 sec\n","Generation loss: 0.7486859560012817 Discriminator loss: 1.3778579235076904 \n","Time for epoch 462 is 24.824331283569336 sec\n","Generation loss: 0.7398105263710022 Discriminator loss: 1.3568283319473267 \n","Time for epoch 463 is 24.840847969055176 sec\n","Generation loss: 0.7275131940841675 Discriminator loss: 1.333409309387207 \n","Time for epoch 464 is 24.85635495185852 sec\n","Generation loss: 0.7666381001472473 Discriminator loss: 1.298497200012207 \n","Time for epoch 465 is 24.910692930221558 sec\n","Generation loss: 0.7551113963127136 Discriminator loss: 1.303276538848877 \n","Time for epoch 466 is 24.957218408584595 sec\n","Generation loss: 0.7456629872322083 Discriminator loss: 1.3365283012390137 \n","Time for epoch 467 is 24.93180537223816 sec\n","Generation loss: 0.7901939749717712 Discriminator loss: 1.3849363327026367 \n","Time for epoch 468 is 24.937352657318115 sec\n","Generation loss: 0.7405617237091064 Discriminator loss: 1.3467823266983032 \n","Time for epoch 469 is 24.931498527526855 sec\n","Generation loss: 0.7195457220077515 Discriminator loss: 1.296704888343811 \n","Time for epoch 470 is 24.9896502494812 sec\n","Generation loss: 0.7753034830093384 Discriminator loss: 1.34578537940979 \n","Time for epoch 471 is 24.973559141159058 sec\n","Generation loss: 0.7922042012214661 Discriminator loss: 1.3286120891571045 \n","Time for epoch 472 is 24.954436540603638 sec\n","Generation loss: 0.6846410632133484 Discriminator loss: 1.37532377243042 \n","Time for epoch 473 is 24.917060136795044 sec\n","Generation loss: 0.7705631256103516 Discriminator loss: 1.3483269214630127 \n","Time for epoch 474 is 24.898104906082153 sec\n","Generation loss: 0.7252892255783081 Discriminator loss: 1.3471086025238037 \n","Time for epoch 475 is 24.903019189834595 sec\n","Generation loss: 0.8126722574234009 Discriminator loss: 1.3355793952941895 \n","Time for epoch 476 is 24.922606468200684 sec\n","Generation loss: 0.7742995023727417 Discriminator loss: 1.3919689655303955 \n","Time for epoch 477 is 24.868510007858276 sec\n","Generation loss: 0.7710572481155396 Discriminator loss: 1.3252196311950684 \n","Time for epoch 478 is 24.97139596939087 sec\n","Generation loss: 0.7840179204940796 Discriminator loss: 1.365475058555603 \n","Time for epoch 479 is 24.8541579246521 sec\n","Generation loss: 0.7726229429244995 Discriminator loss: 1.327523112297058 \n","Time for epoch 480 is 24.899192810058594 sec\n","Generation loss: 0.7226928472518921 Discriminator loss: 1.3657233715057373 \n","Time for epoch 481 is 24.866456508636475 sec\n","Generation loss: 0.7703482508659363 Discriminator loss: 1.336883306503296 \n","Time for epoch 482 is 24.923786401748657 sec\n","Generation loss: 0.7304455637931824 Discriminator loss: 1.3125520944595337 \n","Time for epoch 483 is 24.908058643341064 sec\n","Generation loss: 0.7399913668632507 Discriminator loss: 1.3293755054473877 \n","Time for epoch 484 is 24.897454977035522 sec\n","Generation loss: 0.764965295791626 Discriminator loss: 1.3120691776275635 \n","Time for epoch 485 is 24.874979972839355 sec\n","Generation loss: 0.808574914932251 Discriminator loss: 1.2972630262374878 \n","Time for epoch 486 is 24.9253191947937 sec\n","Generation loss: 0.7806611061096191 Discriminator loss: 1.3079748153686523 \n","Time for epoch 487 is 24.905142068862915 sec\n","Generation loss: 0.7613882422447205 Discriminator loss: 1.3907442092895508 \n","Time for epoch 488 is 24.91701054573059 sec\n","Generation loss: 0.7778235077857971 Discriminator loss: 1.3685662746429443 \n","Time for epoch 489 is 24.88908338546753 sec\n","Generation loss: 0.7322256565093994 Discriminator loss: 1.364771842956543 \n","Time for epoch 490 is 24.900946855545044 sec\n","Generation loss: 0.7674026489257812 Discriminator loss: 1.322843313217163 \n","Time for epoch 491 is 24.93627119064331 sec\n","Generation loss: 0.7367892861366272 Discriminator loss: 1.3753697872161865 \n","Time for epoch 492 is 24.907350778579712 sec\n","Generation loss: 0.7379125356674194 Discriminator loss: 1.3169962167739868 \n","Time for epoch 493 is 24.874403953552246 sec\n","Generation loss: 0.7589854598045349 Discriminator loss: 1.336784839630127 \n","Time for epoch 494 is 24.942415475845337 sec\n","Generation loss: 0.7415519952774048 Discriminator loss: 1.3134171962738037 \n","Time for epoch 495 is 24.85624670982361 sec\n","Generation loss: 0.7350336909294128 Discriminator loss: 1.420162320137024 \n","Time for epoch 496 is 24.84911608695984 sec\n","Generation loss: 0.7845270037651062 Discriminator loss: 1.3079612255096436 \n","Time for epoch 497 is 24.890485763549805 sec\n","Generation loss: 0.7236766815185547 Discriminator loss: 1.3409287929534912 \n","Time for epoch 498 is 24.909355401992798 sec\n","Generation loss: 0.7458747625350952 Discriminator loss: 1.3700041770935059 \n","Time for epoch 499 is 24.922828912734985 sec\n","Generation loss: 0.7323430180549622 Discriminator loss: 1.3516528606414795 \n","Time for epoch 500 is 24.86689591407776 sec\n","Generation loss: 0.7302721738815308 Discriminator loss: 1.361490249633789 \n","\n","Total time taken: 12381.38s\n","\n","\n","\n","\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["gan = DCGAN(data)\n","gan.make_generator_model()\n","gan.make_discriminator_model()\n","gan.dir()\n","gan.train_dcgan(epochs=500, save_interval=50)\n","gan.plot_losses()\n","gan.gif()"]},{"cell_type":"markdown","metadata":{},"source":["## In this part a test is provided."]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[],"source":["def test_network_empty(network):\n","    error = 'When you create a network instance all networks should be None and addable by hand later.'\n","    assert network.generator is None, error\n","    assert network.discriminator is None, error\n","\n","def test_functional(network):\n","    assert (isinstance(network.generator, Functional) ), \"\"\"\n","    After building the generator must be a keras Functional.\"\"\"\n","    assert (isinstance(network.discriminator, Functional)), \"\"\"\n","    After building the discriminator must be a keras Functional.\"\"\"\n","    \n","def test_shape(network):\n","    assert network.generator.output_shape == (None, 28, 28, 1), \"\"\"\n","    After building the generator it must have a shape (None, 28, 28, 1).\"\"\"\n","    assert network.discriminator.output_shape == (None, 1) , \"\"\"\n","        After building the encoder it must have shape (None, 1), but is\n","        \"\"\" + str(network.discriminator.output_shape)"]},{"cell_type":"code","execution_count":46,"metadata":{},"outputs":[],"source":["test_data = mnist.MNISTData(data_fraction=1/100)\n","test_gan = DCGAN(test_data)\n","\n","test_network_empty(test_gan)\n","\n","test_gan.make_generator_model()\n","test_gan.make_discriminator_model()\n","\n","test_functional(test_gan)\n","test_shape(test_gan)"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"name":"DCGAN_mnist.ipynb","provenance":[{"file_id":"1nIiHLgYl53OZWhgPmQxm09avXVAOJd5b","timestamp":1633442598877}]},"interpreter":{"hash":"789093bb736187c46b4a361b2c83e244a48ff9bb7ad40e66e5ffb40455c8edff"},"kernelspec":{"display_name":"Python 3.8.8 64-bit ('MLinPhysics': conda)","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"}},"nbformat":4,"nbformat_minor":0}
diff --git a/VAE.ipynb b/VAE.ipynb
deleted file mode 100644
index 217c53f..0000000
--- a/VAE.ipynb
+++ /dev/null
@@ -1,698 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "nbgrader": {
-     "grade": false,
-     "grade_id": "cell-336b858f8c1a675b",
-     "locked": true,
-     "schema_version": 3,
-     "solution": false,
-     "task": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow.python.keras import Input\n",
-    "from tensorflow.python.keras.engine.functional import Functional\n",
-    "from tensorflow.python.keras.engine.keras_tensor import KerasTensor\n",
-    "from tensorflow.python.keras.models import Model, Sequential\n",
-    "\n",
-    "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.layers import Dense, Lambda\n",
-    "#from tensorflow.keras import layers\n",
-    "\n",
-    "#from tensorflow.python.keras.utils.np_utils import to_categorical\n",
-    "from tensorflow.python.keras.regularizers import l2\n",
-    "from tensorflow.keras import backend as K\n",
-    "tf.compat.v1.disable_eager_execution()\n",
-    "\n",
-    "#from scipy.stats import norm\n",
-    "\n",
-    "import tensorflow.python.util.deprecation as deprecation\n",
-    "deprecation._PRINT_DEPRECATION_WARNINGS = False\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import the MNIST data class and call it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import MNIST_dataset as mnist\n",
-    "data = mnist.MNISTData()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "nbgrader": {
-     "grade": false,
-     "grade_id": "cell-6ee6a27aac2a7e5b",
-     "locked": true,
-     "schema_version": 3,
-     "solution": false,
-     "task": false
-    }
-   },
-   "source": [
-    "# Variational Autoencoder using Keras\n",
-    "The aim of this class is to implement the variational autoencoder and to test it on the MNIST dataset and then build a variational autoencoder that is able to classify MNIST data in its latent dimension with gaussian noise applied to the orignal images.\n",
-    "\n",
-    "Code partially adapted from [Keras Documentation](https://keras.io/examples/generative/vae/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "nbgrader": {
-     "grade": false,
-     "grade_id": "cell-9df5209009034d9d",
-     "locked": false,
-     "schema_version": 3,
-     "solution": true,
-     "task": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class VAE:\n",
-    "    \"\"\"Variational Autencoder Class\"\"\"\n",
-    "    def __init__(self, data: mnist.MNISTData, num_classes=10):\n",
-    "        self.x_train = data.x_train\n",
-    "        self.x_test = data.x_test\n",
-    "        self.y_train = data.y_train\n",
-    "        self.y_test = data.y_test\n",
-    "\n",
-    "        self.num_classes = num_classes\n",
-    "        self.input_shape = self.x_train[0].shape\n",
-    "        self.input = Input(shape=self.input_shape,name='encoder_input')\n",
-    "        self.latent_dim = 2\n",
-    "\n",
-    "        self.encoder = None\n",
-    "        self.encoder_model = None\n",
-    "\n",
-    "        self.decoder = None\n",
-    "        self.VAE = None\n",
-    "        self.encoded_mean = None\n",
-    "        self.encoded_var = None\n",
-    "        self.decoder_model=None\n",
-    "        \n",
-    "    def build_encoder(self):\n",
-    "        \"\"\"Building the encoder architecture for the variational autoencoder. \n",
-    "        The final encoding dimension is 2. \n",
-    "        \"\"\"\n",
-    "        self.encoder = Dense(512, activation='relu')(self.input)\n",
-    "        self.encoder = Dense(64, activation='relu')(self.encoder)\n",
-    "        self.encoded_mean = Dense(self.latent_dim)(self.encoder)\n",
-    "        self.encoded_var = Dense(self.latent_dim)(self.encoder)\n",
-    "        self.encoder = Lambda(self.sampling, output_shape=(self.latent_dim,), name = 'encoder_output')([self.encoded_mean, self.encoded_var])\n",
-    "\n",
-    "        #building a model for the encoder in order to be able to predict and plot the latent dimension\n",
-    "        self.encoder_model = Model(self.input, [self.encoded_mean, self.encoded_var, self.encoder], name='encoder')\n",
-    "    \n",
-    "    def build_decoder(self):\n",
-    "        \"\"\"Building the decoder architecture and storing the output in self.decoder.\"\"\"\n",
-    "        if self.encoder is None:\n",
-    "            raise RuntimeError(\"The encoder has to be built before you can build the decoder!\")        \n",
-    "\n",
-    "        self.decoder = Dense(64, activation='relu')(self.encoder)\n",
-    "        self.decoder = Dense(512, activation='relu')(self.decoder)\n",
-    "        self.decoder = Dense(784, activation='sigmoid', name='decoder_output')(self.decoder)\n",
-    "        \n",
-    "    def build_classifier(self):\n",
-    "        \"\"\" Building the classifier architecture, using self.encoder as input.\"\"\"\n",
-    "\n",
-    "        if self.encoder is None:\n",
-    "            raise RuntimeError(\"The encoder has to be built before you can build the classifier!\")\n",
-    "        if self.decoder is None:\n",
-    "            raise RuntimeError(\"The decoder has to be built before you can build the autoencoder!\")\n",
-    "        self.latent_classifier = Dense(128, kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01), activation='relu')(self.encoder)\n",
-    "        self.latent_classifier = Dense(64,  kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01), activation='relu')(self.latent_classifier)\n",
-    "        self.latent_classifier = Dense(32,  kernel_regularizer=l2(0.01), bias_regularizer=l2(0.01), activation='relu')(self.latent_classifier)\n",
-    "\n",
-    "        self.latent_classifier = Dense(self.num_classes, activation='softmax',name='classifier_output')(self.latent_classifier)\n",
-    "        \n",
-    "    def build_vae(self, use_latent_classifier=False):\n",
-    "        \"\"\" Building the whole variational autoencoder Model from self.encoder and self.decoder with \n",
-    "        self.input as input. It is used self.custom_loss as the model loss function.\n",
-    "        \"\"\"\n",
-    "        if self.encoder is None:\n",
-    "            raise RuntimeError(\"The encoder has to be built before you can build the autoencoder!\")\n",
-    "        if self.decoder is None:\n",
-    "            raise RuntimeError(\"The decoder has to be built before you can build the autoencoder!\")\n",
-    "        \n",
-    "        if use_latent_classifier:\n",
-    "            if self.latent_classifier is None:\n",
-    "                raise RuntimeError(\"If you want to use the option with the latent classifier, you have to build it \"\n",
-    "                                   \"beforehand!\")\n",
-    "            self.VAE = Model(self.input, outputs=[self.decoder, self.latent_classifier])\n",
-    "            self.VAE.compile(loss=self.custom_loss(self.encoded_mean, self.encoded_var), loss_weights=[1, 0.1], optimizer='adam')#, metrics=\"accuracy\")\n",
-    "        else:\n",
-    "            self.VAE = Model(self.input, outputs=self.decoder)\n",
-    "            self.VAE.compile(optimizer='adam', loss=self.custom_loss(self.encoded_mean, self.encoded_var))\n",
-    "            self.VAE.summary()\n",
-    "\n",
-    "    def sampling(self, args):\n",
-    "        \"\"\" Implement the Reparameterization trick. The function returns\n",
-    "         a vector randomly sampled from the latent space.\n",
-    "         # Arguments\n",
-    "        args (tensor): mean and log of variance of Q(z|X)\n",
-    "         # Returns\n",
-    "            z (tensor): sampled latent vector   \n",
-    "        \"\"\"\n",
-    "        z_mean, z_log_var = args\n",
-    "        batch = K.shape(z_mean)[0]\n",
-    "        dim = K.int_shape(z_mean)[1]\n",
-    "        # we apply the multigaussian noise to every points at once\n",
-    "        epsilon = K.random_normal(shape=(batch, dim))\n",
-    "        return z_mean + K.exp(0.5 * z_log_var) * epsilon\n",
-    "    \n",
-    "\n",
-    "    def custom_loss(self, mean, var):\n",
-    "        \"\"\"Implement the loss function for the variational autoencoder.\n",
-    "        Sort of recepy for tf to how to compute loss function from two values.\"\"\"\n",
-    "        \n",
-    "        def loss(y_true, y_pred):\n",
-    "\n",
-    "            reconstruction_loss = tf.losses.mean_squared_error(y_true, y_pred)*784\n",
-    "            kl_loss = 1. + var - K.square(mean) - K.exp(var)\n",
-    "            kl_loss = K.sum(kl_loss, axis=-1)\n",
-    "            kl_loss *= -0.5\n",
-    "            beta = 1.0*10**-2\n",
-    "            return K.mean(reconstruction_loss + beta*kl_loss)\n",
-    "\n",
-    "        return loss\n",
-    "        \n",
-    "        \n",
-    "    def fit_data(self, batch_size=128, epochs=40, use_latent_classifier=False):\n",
-    "        \"\"\"Write the fit function for the autoencoder. \n",
-    "        Store the fit history in self.history to be able to plot the fitting scores.\"\"\"\n",
-    "        \n",
-    "        if use_latent_classifier:\n",
-    "            self.history = self.VAE.fit(self.x_train, [self.x_train, self.y_train],\n",
-    "                                                validation_data=(self.x_test, [self.x_test, self.y_test]),\n",
-    "                                                batch_size=batch_size, epochs=epochs,\n",
-    "                                                shuffle=True,\n",
-    "                                                )\n",
-    "        else:\n",
-    "            self.history = self.VAE.fit(self.x_train, self.x_train, validation_data=(self.x_test, self.x_test),\n",
-    "                                                batch_size=batch_size, epochs=epochs,\n",
-    "                                                shuffle=True,\n",
-    "                                                )\n",
-    "        \n",
-    "        self.history = self.history.history\n",
-    "\n",
-    "    def dir(self):\n",
-    "        \"\"\"Creation of the directories\"\"\"\n",
-    "        dir = os.path.join(\"images\")\n",
-    "        if not os.path.exists(dir):\n",
-    "            os.mkdir(dir)\n",
-    "        dir2 = os.path.join(\"./images/VAE\")\n",
-    "        if not os.path.exists(dir2):\n",
-    "            os.mkdir(dir2)\n",
-    "    \n",
-    "    def plot_score(self, model_name=None):\n",
-    "        \"\"\"Plots the scores achieved during the fitting.\"\"\"\n",
-    "        plt.plot(self.history['loss'])\n",
-    "        plt.plot(self.history['val_loss'])\n",
-    "        plt.ylabel('Model Accuracy')\n",
-    "        plt.xlabel('Epoch')\n",
-    "        plt.legend(['Train', 'Test'], loc='best')\n",
-    "        plt.title('Accuracy of {model_name}'.format(model_name=model_name))\n",
-    "        \n",
-    "        plt.savefig('./images/VAE/Accuracy of {model_name}.png'.format(model_name=model_name))\n",
-    "        \n",
-    "        plt.show()\n",
-    "\n",
-    "    def plot_latent_dimension(self, model_name=None):\n",
-    "        \"\"\"Plots the latent dimension of the autoencoder.\"\"\"\n",
-    "        prediction = self.encoder_model.predict(self.x_test)\n",
-    "        fig = plt.figure(figsize=(10, 10))\n",
-    "        fig.patch.set_facecolor(\"white\")\n",
-    "        plt.scatter(prediction[2][:, 0], prediction[2][:, 1], c=np.argmax(self.y_test, axis=1) , cmap=\"Set3\")\n",
-    "        plt.colorbar()\n",
-    "        plt.title('Latent space {model_name}'.format(model_name=model_name))\n",
-    "        \n",
-    "        plt.savefig('./images/VAE/Latent dimension of {model_name}.png'.format(model_name=model_name))\n",
-    "\n",
-    "        plt.show()  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"functional_29\"\n",
-      "__________________________________________________________________________________________________\n",
-      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
-      "==================================================================================================\n",
-      "encoder_input (InputLayer)      [(None, 784)]        0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_81 (Dense)                (None, 512)          401920      encoder_input[0][0]              \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_82 (Dense)                (None, 64)           32832       dense_81[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_83 (Dense)                (None, 2)            130         dense_82[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_84 (Dense)                (None, 2)            130         dense_82[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "encoder_output (Lambda)         (None, 2)            0           dense_83[0][0]                   \n",
-      "                                                                 dense_84[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_85 (Dense)                (None, 64)           192         encoder_output[0][0]             \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_86 (Dense)                (None, 512)          33280       dense_85[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "decoder_output (Dense)          (None, 784)          402192      dense_86[0][0]                   \n",
-      "==================================================================================================\n",
-      "Total params: 870,676\n",
-      "Trainable params: 870,676\n",
-      "Non-trainable params: 0\n",
-      "__________________________________________________________________________________________________\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/40\n",
-      "60000/60000 [==============================] - 6s 102us/sample - loss: 45.2457 - val_loss: 37.6821\n",
-      "Epoch 2/40\n",
-      "60000/60000 [==============================] - 6s 95us/sample - loss: 36.2524 - val_loss: 34.7764\n",
-      "Epoch 3/40\n",
-      "60000/60000 [==============================] - 6s 106us/sample - loss: 34.0498 - val_loss: 33.3234\n",
-      "Epoch 4/40\n",
-      "60000/60000 [==============================] - 7s 111us/sample - loss: 32.8452 - val_loss: 32.4891\n",
-      "Epoch 5/40\n",
-      "60000/60000 [==============================] - 7s 110us/sample - loss: 32.1166 - val_loss: 31.8767\n",
-      "Epoch 6/40\n",
-      "60000/60000 [==============================] - 7s 111us/sample - loss: 31.5335 - val_loss: 31.4575\n",
-      "Epoch 7/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 31.0908 - val_loss: 31.1333\n",
-      "Epoch 8/40\n",
-      "60000/60000 [==============================] - 7s 111us/sample - loss: 30.7700 - val_loss: 30.8534\n",
-      "Epoch 9/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 30.4641 - val_loss: 30.6746\n",
-      "Epoch 10/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 30.1821 - val_loss: 30.3966\n",
-      "Epoch 11/40\n",
-      "60000/60000 [==============================] - 7s 112us/sample - loss: 29.9033 - val_loss: 30.2331\n",
-      "Epoch 12/40\n",
-      "60000/60000 [==============================] - 7s 112us/sample - loss: 29.6902 - val_loss: 30.1111\n",
-      "Epoch 13/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 29.4538 - val_loss: 29.9694\n",
-      "Epoch 14/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 29.3278 - val_loss: 29.7804\n",
-      "Epoch 15/40\n",
-      "60000/60000 [==============================] - 7s 112us/sample - loss: 29.1179 - val_loss: 29.8238\n",
-      "Epoch 16/40\n",
-      "60000/60000 [==============================] - 7s 115us/sample - loss: 28.9441 - val_loss: 29.5089\n",
-      "Epoch 17/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 28.7928 - val_loss: 29.4238\n",
-      "Epoch 18/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 28.6550 - val_loss: 29.2484\n",
-      "Epoch 19/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 28.5070 - val_loss: 29.3867\n",
-      "Epoch 20/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 28.4331 - val_loss: 29.3034\n",
-      "Epoch 21/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 28.3426 - val_loss: 29.0956\n",
-      "Epoch 22/40\n",
-      "60000/60000 [==============================] - 7s 111us/sample - loss: 28.1605 - val_loss: 28.9311\n",
-      "Epoch 23/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 28.0950 - val_loss: 28.9863\n",
-      "Epoch 24/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 28.0245 - val_loss: 28.9332\n",
-      "Epoch 25/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 27.9350 - val_loss: 28.9780\n",
-      "Epoch 26/40\n",
-      "60000/60000 [==============================] - 7s 109us/sample - loss: 27.8940 - val_loss: 28.7808\n",
-      "Epoch 27/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 27.7766 - val_loss: 28.7428\n",
-      "Epoch 28/40\n",
-      "60000/60000 [==============================] - 7s 110us/sample - loss: 27.6965 - val_loss: 28.7697\n",
-      "Epoch 29/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 27.6083 - val_loss: 28.6452\n",
-      "Epoch 30/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 27.5189 - val_loss: 28.6393\n",
-      "Epoch 31/40\n",
-      "60000/60000 [==============================] - 7s 112us/sample - loss: 27.5000 - val_loss: 28.5919\n",
-      "Epoch 32/40\n",
-      "60000/60000 [==============================] - 7s 115us/sample - loss: 27.4035 - val_loss: 28.5137\n",
-      "Epoch 33/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 27.3919 - val_loss: 28.6041\n",
-      "Epoch 34/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 27.3146 - val_loss: 28.5032\n",
-      "Epoch 35/40\n",
-      "60000/60000 [==============================] - 7s 111us/sample - loss: 27.1710 - val_loss: 28.5193\n",
-      "Epoch 36/40\n",
-      "60000/60000 [==============================] - 7s 115us/sample - loss: 27.1770 - val_loss: 28.5020\n",
-      "Epoch 37/40\n",
-      "60000/60000 [==============================] - 7s 117us/sample - loss: 27.1638 - val_loss: 28.4467\n",
-      "Epoch 38/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 27.0809 - val_loss: 28.4215\n",
-      "Epoch 39/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 27.0311 - val_loss: 28.3936\n",
-      "Epoch 40/40\n",
-      "60000/60000 [==============================] - 7s 112us/sample - loss: 26.9807 - val_loss: 28.3598\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "network = VAE(data)\n",
-    "network.build_encoder()\n",
-    "network.build_decoder()\n",
-    "network.build_vae()\n",
-    "network.fit_data()\n",
-    "network.dir()\n",
-    "network.plot_latent_dimension(\"Variational Autoencoder without classifier\")\n",
-    "network.plot_score(\"Variational Autoencoder without classifier\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/40\n",
-      "60000/60000 [==============================] - 7s 121us/sample - loss: 50.9148 - decoder_output_loss: 46.0197 - classifier_output_loss: 43.8399 - val_loss: 40.8236 - val_decoder_output_loss: 38.3034 - val_classifier_output_loss: 21.0122\n",
-      "Epoch 2/40\n",
-      "60000/60000 [==============================] - 7s 117us/sample - loss: 38.9978 - decoder_output_loss: 36.8797 - classifier_output_loss: 16.7939 - val_loss: 37.0147 - val_decoder_output_loss: 35.2484 - val_classifier_output_loss: 12.6710\n",
-      "Epoch 3/40\n",
-      "60000/60000 [==============================] - 7s 122us/sample - loss: 36.0143 - decoder_output_loss: 34.4042 - classifier_output_loss: 12.0488 - val_loss: 34.9290 - val_decoder_output_loss: 33.4044 - val_classifier_output_loss: 11.6541\n",
-      "Epoch 4/40\n",
-      "60000/60000 [==============================] - 7s 117us/sample - loss: 34.1812 - decoder_output_loss: 32.7694 - classifier_output_loss: 10.3955 - val_loss: 33.5615 - val_decoder_output_loss: 32.2035 - val_classifier_output_loss: 10.1113\n",
-      "Epoch 5/40\n",
-      "60000/60000 [==============================] - 7s 117us/sample - loss: 33.0173 - decoder_output_loss: 31.7371 - classifier_output_loss: 9.2745 - val_loss: 32.6790 - val_decoder_output_loss: 31.2990 - val_classifier_output_loss: 9.9746\n",
-      "Epoch 6/40\n",
-      "60000/60000 [==============================] - 7s 118us/sample - loss: 32.1573 - decoder_output_loss: 30.9615 - classifier_output_loss: 8.5869 - val_loss: 32.1522 - val_decoder_output_loss: 30.8228 - val_classifier_output_loss: 9.9618\n",
-      "Epoch 7/40\n",
-      "60000/60000 [==============================] - 7s 121us/sample - loss: 31.4701 - decoder_output_loss: 30.3320 - classifier_output_loss: 8.1699 - val_loss: 31.3812 - val_decoder_output_loss: 30.2037 - val_classifier_output_loss: 9.4170\n",
-      "Epoch 8/40\n",
-      "60000/60000 [==============================] - 7s 111us/sample - loss: 30.9183 - decoder_output_loss: 29.8273 - classifier_output_loss: 7.8286 - val_loss: 30.9101 - val_decoder_output_loss: 29.7139 - val_classifier_output_loss: 8.7237\n",
-      "Epoch 9/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 30.4299 - decoder_output_loss: 29.3848 - classifier_output_loss: 7.4936 - val_loss: 30.5863 - val_decoder_output_loss: 29.4804 - val_classifier_output_loss: 8.5730\n",
-      "Epoch 10/40\n",
-      "60000/60000 [==============================] - 7s 120us/sample - loss: 30.0434 - decoder_output_loss: 29.0296 - classifier_output_loss: 7.2869 - val_loss: 30.3150 - val_decoder_output_loss: 29.1322 - val_classifier_output_loss: 8.5735\n",
-      "Epoch 11/40\n",
-      "60000/60000 [==============================] - 7s 120us/sample - loss: 29.6999 - decoder_output_loss: 28.7258 - classifier_output_loss: 6.9972 - val_loss: 30.0916 - val_decoder_output_loss: 29.0214 - val_classifier_output_loss: 8.3605\n",
-      "Epoch 12/40\n",
-      "60000/60000 [==============================] - 7s 117us/sample - loss: 29.4340 - decoder_output_loss: 28.4823 - classifier_output_loss: 6.8792 - val_loss: 29.9099 - val_decoder_output_loss: 28.7744 - val_classifier_output_loss: 9.1860\n",
-      "Epoch 13/40\n",
-      "60000/60000 [==============================] - 7s 121us/sample - loss: 29.1343 - decoder_output_loss: 28.2146 - classifier_output_loss: 6.6339 - val_loss: 29.6091 - val_decoder_output_loss: 28.5454 - val_classifier_output_loss: 8.2579\n",
-      "Epoch 14/40\n",
-      "60000/60000 [==============================] - 7s 119us/sample - loss: 28.9236 - decoder_output_loss: 28.0270 - classifier_output_loss: 6.4775 - val_loss: 29.4483 - val_decoder_output_loss: 28.4306 - val_classifier_output_loss: 7.6657\n",
-      "Epoch 15/40\n",
-      "60000/60000 [==============================] - 7s 121us/sample - loss: 28.7182 - decoder_output_loss: 27.8404 - classifier_output_loss: 6.3698 - val_loss: 29.3284 - val_decoder_output_loss: 28.2846 - val_classifier_output_loss: 7.8781\n",
-      "Epoch 16/40\n",
-      "60000/60000 [==============================] - 7s 116us/sample - loss: 28.5414 - decoder_output_loss: 27.6788 - classifier_output_loss: 6.2820 - val_loss: 29.2884 - val_decoder_output_loss: 28.3127 - val_classifier_output_loss: 8.0152\n",
-      "Epoch 17/40\n",
-      "60000/60000 [==============================] - 7s 115us/sample - loss: 28.3409 - decoder_output_loss: 27.4967 - classifier_output_loss: 6.1622 - val_loss: 29.1734 - val_decoder_output_loss: 28.2473 - val_classifier_output_loss: 7.6961\n",
-      "Epoch 18/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 28.1606 - decoder_output_loss: 27.3394 - classifier_output_loss: 5.9953 - val_loss: 29.0571 - val_decoder_output_loss: 28.0302 - val_classifier_output_loss: 7.5634\n",
-      "Epoch 19/40\n",
-      "60000/60000 [==============================] - 7s 115us/sample - loss: 28.0207 - decoder_output_loss: 27.2066 - classifier_output_loss: 5.9841 - val_loss: 29.0187 - val_decoder_output_loss: 28.0295 - val_classifier_output_loss: 7.4727\n",
-      "Epoch 20/40\n",
-      "60000/60000 [==============================] - 7s 109us/sample - loss: 27.9057 - decoder_output_loss: 27.1012 - classifier_output_loss: 5.9534 - val_loss: 28.9214 - val_decoder_output_loss: 27.9576 - val_classifier_output_loss: 7.4140\n",
-      "Epoch 21/40\n",
-      "60000/60000 [==============================] - 6s 108us/sample - loss: 27.8064 - decoder_output_loss: 27.0114 - classifier_output_loss: 5.8994 - val_loss: 28.8675 - val_decoder_output_loss: 27.9176 - val_classifier_output_loss: 7.3237\n",
-      "Epoch 22/40\n",
-      "60000/60000 [==============================] - 6s 98us/sample - loss: 27.6749 - decoder_output_loss: 26.9023 - classifier_output_loss: 5.7341 - val_loss: 28.8523 - val_decoder_output_loss: 27.8816 - val_classifier_output_loss: 7.4396\n",
-      "Epoch 23/40\n",
-      "60000/60000 [==============================] - 7s 108us/sample - loss: 27.5623 - decoder_output_loss: 26.7899 - classifier_output_loss: 5.7676 - val_loss: 28.7520 - val_decoder_output_loss: 27.7843 - val_classifier_output_loss: 7.2527\n",
-      "Epoch 24/40\n",
-      "60000/60000 [==============================] - 7s 112us/sample - loss: 27.4763 - decoder_output_loss: 26.7105 - classifier_output_loss: 5.7439 - val_loss: 28.6584 - val_decoder_output_loss: 27.7537 - val_classifier_output_loss: 7.3818\n",
-      "Epoch 25/40\n",
-      "60000/60000 [==============================] - 7s 111us/sample - loss: 27.3650 - decoder_output_loss: 26.6109 - classifier_output_loss: 5.6804 - val_loss: 28.5556 - val_decoder_output_loss: 27.6505 - val_classifier_output_loss: 7.3195\n",
-      "Epoch 26/40\n",
-      "60000/60000 [==============================] - 6s 104us/sample - loss: 27.3001 - decoder_output_loss: 26.5565 - classifier_output_loss: 5.6110 - val_loss: 28.5076 - val_decoder_output_loss: 27.6099 - val_classifier_output_loss: 7.2306\n",
-      "Epoch 27/40\n",
-      "60000/60000 [==============================] - 7s 109us/sample - loss: 27.2214 - decoder_output_loss: 26.4828 - classifier_output_loss: 5.5952 - val_loss: 28.4844 - val_decoder_output_loss: 27.5883 - val_classifier_output_loss: 7.3844\n",
-      "Epoch 28/40\n",
-      "60000/60000 [==============================] - 7s 116us/sample - loss: 27.1149 - decoder_output_loss: 26.3863 - classifier_output_loss: 5.5241 - val_loss: 28.4750 - val_decoder_output_loss: 27.5495 - val_classifier_output_loss: 7.2936\n",
-      "Epoch 29/40\n",
-      "60000/60000 [==============================] - 7s 117us/sample - loss: 27.0489 - decoder_output_loss: 26.3197 - classifier_output_loss: 5.5655 - val_loss: 28.3821 - val_decoder_output_loss: 27.4626 - val_classifier_output_loss: 7.1226\n",
-      "Epoch 30/40\n",
-      "60000/60000 [==============================] - 7s 117us/sample - loss: 26.9603 - decoder_output_loss: 26.2435 - classifier_output_loss: 5.4767 - val_loss: 28.3927 - val_decoder_output_loss: 27.5038 - val_classifier_output_loss: 7.1174\n",
-      "Epoch 31/40\n",
-      "60000/60000 [==============================] - 7s 110us/sample - loss: 26.8780 - decoder_output_loss: 26.1676 - classifier_output_loss: 5.4198 - val_loss: 28.3015 - val_decoder_output_loss: 27.4436 - val_classifier_output_loss: 6.9463\n",
-      "Epoch 32/40\n",
-      "60000/60000 [==============================] - 7s 116us/sample - loss: 26.8584 - decoder_output_loss: 26.1499 - classifier_output_loss: 5.4326 - val_loss: 28.3970 - val_decoder_output_loss: 27.4502 - val_classifier_output_loss: 7.3790\n",
-      "Epoch 33/40\n",
-      "60000/60000 [==============================] - 7s 114us/sample - loss: 26.7825 - decoder_output_loss: 26.0808 - classifier_output_loss: 5.3998 - val_loss: 28.2636 - val_decoder_output_loss: 27.3794 - val_classifier_output_loss: 7.1744\n",
-      "Epoch 34/40\n",
-      "60000/60000 [==============================] - 7s 109us/sample - loss: 26.7458 - decoder_output_loss: 26.0498 - classifier_output_loss: 5.3641 - val_loss: 28.3569 - val_decoder_output_loss: 27.4604 - val_classifier_output_loss: 7.1161\n",
-      "Epoch 35/40\n",
-      "60000/60000 [==============================] - 6s 107us/sample - loss: 26.6760 - decoder_output_loss: 25.9829 - classifier_output_loss: 5.3538 - val_loss: 28.1656 - val_decoder_output_loss: 27.3050 - val_classifier_output_loss: 7.0669\n",
-      "Epoch 36/40\n",
-      "60000/60000 [==============================] - 7s 109us/sample - loss: 26.6218 - decoder_output_loss: 25.9360 - classifier_output_loss: 5.3021 - val_loss: 28.1956 - val_decoder_output_loss: 27.3502 - val_classifier_output_loss: 7.2281\n",
-      "Epoch 37/40\n",
-      "60000/60000 [==============================] - 7s 116us/sample - loss: 26.5694 - decoder_output_loss: 25.8859 - classifier_output_loss: 5.3110 - val_loss: 28.2743 - val_decoder_output_loss: 27.3723 - val_classifier_output_loss: 7.3507\n",
-      "Epoch 38/40\n",
-      "60000/60000 [==============================] - 7s 116us/sample - loss: 26.5261 - decoder_output_loss: 25.8452 - classifier_output_loss: 5.3032 - val_loss: 28.1154 - val_decoder_output_loss: 27.2955 - val_classifier_output_loss: 6.9219\n",
-      "Epoch 39/40\n",
-      "60000/60000 [==============================] - 7s 117us/sample - loss: 26.4694 - decoder_output_loss: 25.7936 - classifier_output_loss: 5.2794 - val_loss: 28.1211 - val_decoder_output_loss: 27.1994 - val_classifier_output_loss: 7.4320\n",
-      "Epoch 40/40\n",
-      "60000/60000 [==============================] - 7s 113us/sample - loss: 26.4145 - decoder_output_loss: 25.7416 - classifier_output_loss: 5.2576 - val_loss: 28.0869 - val_decoder_output_loss: 27.1894 - val_classifier_output_loss: 6.9315\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "network_with_classifier = VAE(data)\n",
-    "network_with_classifier.build_encoder()\n",
-    "network_with_classifier.build_decoder()\n",
-    "network_with_classifier.build_classifier()\n",
-    "network_with_classifier.build_vae(use_latent_classifier=True)\n",
-    "network_with_classifier.fit_data(use_latent_classifier=True)\n",
-    "network_with_classifier.dir()\n",
-    "network_with_classifier.plot_latent_dimension(\"Variational Autoencoder with classifier\")\n",
-    "network_with_classifier.plot_score(\"Variational Autoencoder with classifier\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## In this part a test is provided."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def test_network_empty(network):\n",
-    "    error = 'When you create a network instance all networks should be None and addable by hand later.'\n",
-    "    assert network.encoder is None, error\n",
-    "    assert network.decoder is None, error\n",
-    "    assert network.VAE is None, error\n",
-    "\n",
-    "def test_tensor_and_functional(network):\n",
-    "    assert (isinstance(network.encoder, KerasTensor) or isinstance(network.encoder, tf.Tensor)), \"\"\"\n",
-    "    After building the encoder it must be a keras tensor.\"\"\"\n",
-    "    assert (isinstance(network.decoder, KerasTensor) or isinstance(network.decoder, tf.Tensor)), \"\"\"\n",
-    "    After building the decoder it must be a keras tensor.\"\"\"\n",
-    "    assert isinstance(network.VAE, Functional), \"\"\"\n",
-    "    After building the encoder it must be a keras Functional.\"\"\"\n",
-    "    assert (isinstance(network.latent_classifier, KerasTensor) or \n",
-    "    isinstance(network.latent_classifier, tf.Tensor)), \"\"\"\n",
-    "    After building the latent_classifier it must be a keras tensor.\"\"\"\n",
-    "\n",
-    "def test_shape(network):\n",
-    "    assert network.decoder.shape[-1] == 784, \"\"\"\n",
-    "    After building the decoder it must be a keras tensor.\"\"\"\n",
-    "    assert network.encoder.shape[-1] == 2, \"\"\"\n",
-    "    After building the encoder it must have shape (None,2), but is\n",
-    "    \"\"\" + str(network.encoder.shape)\n",
-    "    assert (isinstance(network.latent_classifier, KerasTensor) or \n",
-    "    isinstance(network.latent_classifier, tf.Tensor)), \"\"\"\n",
-    "    After building the latent_classifier it must be a keras tensor.\"\"\"\n",
-    "    assert network.VAE.input_shape == network.VAE.output_shape\n",
-    "\n",
-    "def test_sampling(network):\n",
-    "    samples=[]\n",
-    "    samples_var=[]\n",
-    "    samples_mean=[]\n",
-    "    for _ in range(100):\n",
-    "        prediction = network.encoder_model.predict(network.x_test[0].reshape(1,-1))\n",
-    "        samples.append(prediction[2])\n",
-    "        samples_mean.append(prediction[0])\n",
-    "        samples_var.append(prediction[1])\n",
-    "    x = int(np.isclose(np.array(samples)[:,0][:,0].var(),0, atol=0.00001))+ int(np.isclose(np.array(samples_var)[:,0][:,0].var(),0, atol=0.00001))+int(np.isclose(np.array(samples_mean)[:,0][:,0].var(), 0, atol=0.00001))\n",
-    "    assert x==2, \"There should be some variance when sampling from the latent dimension.\" \n",
-    "\n",
-    "def test_classifier(network, dataset):\n",
-    "    assert network.VAE.input_shape in network.VAE.output_shape\n",
-    "    assert (None, 10) in network.VAE.output_shape\n",
-    "    prediction = network.VAE.predict(dataset.x_test)\n",
-    "    assert (np.all(prediction[1] <= 1) and np.all(prediction[1] >= 0)) and (\n",
-    "        np.all(prediction[0] <= 1) and np.all(prediction[0] >= 0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"functional_19\"\n",
-      "__________________________________________________________________________________________________\n",
-      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
-      "==================================================================================================\n",
-      "encoder_input (InputLayer)      [(None, 784)]        0                                            \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_66 (Dense)                (None, 512)          401920      encoder_input[0][0]              \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_67 (Dense)                (None, 64)           32832       dense_66[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_68 (Dense)                (None, 2)            130         dense_67[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_69 (Dense)                (None, 2)            130         dense_67[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "encoder_output (Lambda)         (None, 2)            0           dense_68[0][0]                   \n",
-      "                                                                 dense_69[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_70 (Dense)                (None, 64)           192         encoder_output[0][0]             \n",
-      "__________________________________________________________________________________________________\n",
-      "dense_71 (Dense)                (None, 512)          33280       dense_70[0][0]                   \n",
-      "__________________________________________________________________________________________________\n",
-      "decoder_output (Dense)          (None, 784)          402192      dense_71[0][0]                   \n",
-      "==================================================================================================\n",
-      "Total params: 870,676\n",
-      "Trainable params: 870,676\n",
-      "Non-trainable params: 0\n",
-      "__________________________________________________________________________________________________\n",
-      "Train on 600 samples, validate on 100 samples\n",
-      "600/600 [==============================] - 0s 252us/sample - loss: 178.7936 - decoder_output_loss: 169.0773 - classifier_output_loss: 70.6653 - val_loss: 143.7744 - val_decoder_output_loss: 135.4780 - val_classifier_output_loss: 70.5487\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_data = mnist.MNISTData(data_fraction =1/100)\n",
-    "test_network = VAE(test_data)\n",
-    "\n",
-    "test_network_empty(test_network)\n",
-    "\n",
-    "test_network = VAE(test_data)\n",
-    "test_network.build_encoder()\n",
-    "test_network.build_decoder()\n",
-    "test_network.build_classifier()\n",
-    "test_network.build_vae()\n",
-    "\n",
-    "test_tensor_and_functional(test_network)\n",
-    "test_shape(test_network)\n",
-    "test_sampling(test_network)\n",
-    "\n",
-    "test_network.build_vae(use_latent_classifier=True)\n",
-    "test_network.fit_data(epochs=1, use_latent_classifier=True)\n",
-    "\n",
-    "test_classifier(test_network, test_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "celltoolbar": "Create Assignment",
-  "interpreter": {
-   "hash": "789093bb736187c46b4a361b2c83e244a48ff9bb7ad40e66e5ffb40455c8edff"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.8.8 64-bit ('MLinPhysics': conda)",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/__pycache__/MNIST_dataset.cpython-38.pyc b/__pycache__/MNIST_dataset.cpython-38.pyc
new file mode 100644
index 0000000..119634a
Binary files /dev/null and b/__pycache__/MNIST_dataset.cpython-38.pyc differ
diff --git a/images/AE/Accuracy of Autoencoder without classifier.png b/images/AE/Accuracy of Autoencoder without classifier.png
new file mode 100644
index 0000000..30edef7
Binary files /dev/null and b/images/AE/Accuracy of Autoencoder without classifier.png differ
diff --git a/images/AE/Latent dimension of Autoencoder without classifier.png b/images/AE/Latent dimension of Autoencoder without classifier.png
new file mode 100644
index 0000000..7d29ce2
Binary files /dev/null and b/images/AE/Latent dimension of Autoencoder without classifier.png differ
diff --git a/images/AE/reconstructed images.png b/images/AE/reconstructed images.png
new file mode 100644
index 0000000..7c5b46d
Binary files /dev/null and b/images/AE/reconstructed images.png differ
diff --git a/model_1/hls4ml_prj.tar.gz b/model_1/hls4ml_prj.tar.gz
new file mode 100644
index 0000000..80f2005
Binary files /dev/null and b/model_1/hls4ml_prj.tar.gz differ
diff --git a/model_1/hls4ml_prj/build_lib.sh b/model_1/hls4ml_prj/build_lib.sh
new file mode 100644
index 0000000..6f95a94
--- /dev/null
+++ b/model_1/hls4ml_prj/build_lib.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+CC=g++
+if [[ "$OSTYPE" == "linux-gnu" ]]; then
+    CFLAGS="-O3 -fPIC -std=c++11 -fno-gnu-unique"
+elif [[ "$OSTYPE" == "darwin"* ]]; then
+    CFLAGS="-O3 -fPIC -std=c++11"
+fi
+LDFLAGS=
+INCFLAGS="-Ifirmware/ap_types/"
+PROJECT=myproject
+LIB_STAMP=3D7f2580
+
+${CC} ${CFLAGS} ${INCFLAGS} -c firmware/${PROJECT}.cpp -o ${PROJECT}.o
+${CC} ${CFLAGS} ${INCFLAGS} -c ${PROJECT}_bridge.cpp -o ${PROJECT}_bridge.o
+${CC} ${CFLAGS} ${INCFLAGS} -shared ${PROJECT}.o ${PROJECT}_bridge.o -o firmware/${PROJECT}-${LIB_STAMP}.so
+rm -f *.o
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/build_prj.tcl b/model_1/hls4ml_prj/build_prj.tcl
new file mode 100644
index 0000000..c82ef94
--- /dev/null
+++ b/model_1/hls4ml_prj/build_prj.tcl
@@ -0,0 +1,134 @@
+#################
+#    HLS4ML
+#################
+array set opt {
+  reset      0
+  csim       1
+  synth      1
+  cosim      1
+  validation 1
+  export     0
+  vsynth     0
+}
+
+foreach arg $::argv {
+  foreach o [lsort [array names opt]] {
+    regexp "$o=+(\\w+)" $arg unused opt($o)
+  }
+}
+
+proc report_time { op_name time_start time_end } {
+  set time_taken [expr $time_end - $time_start]
+  set time_s [expr ($time_taken / 1000) % 60]
+  set time_m [expr ($time_taken / (1000*60)) % 60]
+  set time_h [expr ($time_taken / (1000*60*60)) % 24]
+  puts "***** ${op_name} COMPLETED IN ${time_h}h${time_m}m${time_s}s *****"
+}
+
+# Compare file content: 1 = same, 0 = different
+proc compare_files {file_1 file_2} {
+    # Check if files exist, error otherwise
+    if {! ([file exists $file_1] && [file exists $file_2])} {
+        return 0
+    }
+    # Files with different sizes are obviously different
+    if {[file size $file_1] != [file size $file_2]} {
+        return 0
+    }
+
+    # String compare the content of the files
+    set fh_1 [open $file_1 r]
+    set fh_2 [open $file_2 r]
+    set equal [string equal [read $fh_1] [read $fh_2]]
+    close $fh_1
+    close $fh_2
+    return $equal
+}
+
+file mkdir tb_data
+set CSIM_RESULTS "./tb_data/csim_results.log"
+set RTL_COSIM_RESULTS "./tb_data/rtl_cosim_results.log"
+
+if {$opt(reset)} {
+  open_project -reset myproject_prj
+} else {
+  open_project myproject_prj
+}
+set_top myproject
+add_files firmware/myproject.cpp -cflags "-std=c++0x"
+add_files -tb myproject_test.cpp -cflags "-std=c++0x"
+add_files -tb firmware/weights
+add_files -tb tb_data
+if {$opt(reset)} {
+  open_solution -reset "solution1"
+} else {
+  open_solution "solution1"
+}
+catch {config_array_partition -maximum_size 4096}
+config_compile -name_max_length 60
+set_part {xcu250-figd2104-2L-e}
+create_clock -period 5 -name default
+
+
+if {$opt(csim)} {
+  puts "***** C SIMULATION *****"
+  set time_start [clock clicks -milliseconds]
+  csim_design
+  set time_end [clock clicks -milliseconds]
+  report_time "C SIMULATION" $time_start $time_end
+}
+
+if {$opt(synth)} {
+  puts "***** C/RTL SYNTHESIS *****"
+  set time_start [clock clicks -milliseconds]
+  csynth_design
+  set time_end [clock clicks -milliseconds]
+  report_time "C/RTL SYNTHESIS" $time_start $time_end
+}
+
+if {$opt(cosim)} {
+  puts "***** C/RTL SIMULATION *****"
+  # TODO: This is a workaround (Xilinx defines __RTL_SIMULATION__ only for SystemC testbenches).
+  add_files -tb myproject_test.cpp -cflags "-std=c++0x -DRTL_SIM"
+  set time_start [clock clicks -milliseconds]
+  cosim_design -trace_level all
+  set time_end [clock clicks -milliseconds]
+  puts "INFO:"
+  puts [read [open myproject_prj/solution1/sim/report/myproject_cosim.rpt r]]
+  report_time "C/RTL SIMULATION" $time_start $time_end
+}
+
+if {$opt(validation)} {
+  puts "***** C/RTL VALIDATION *****"
+  if {[compare_files $CSIM_RESULTS $RTL_COSIM_RESULTS]} {
+      puts "INFO: Test PASSED"
+  } else {
+      puts "ERROR: Test failed"
+      puts "ERROR: - csim log:      $CSIM_RESULTS"
+      puts "ERROR: - RTL-cosim log: $RTL_COSIM_RESULTS"
+      exit 1
+  }
+}
+
+if {$opt(export)} {
+  puts "***** EXPORT IP *****"
+  set time_start [clock clicks -milliseconds]
+  export_design -format ip_catalog
+  set time_end [clock clicks -milliseconds]
+  report_time "EXPORT IP" $time_start $time_end
+}
+
+if {$opt(vsynth)} {
+  puts "***** VIVADO SYNTHESIS *****"
+  if {[file exist myproject_prj/solution1/syn/vhdl]} {
+    set time_start [clock clicks -milliseconds]
+    exec vivado -mode batch -source vivado_synth.tcl >@ stdout
+    set time_end [clock clicks -milliseconds]
+    report_time "VIVADO SYNTHESIS" $time_start $time_end
+  } else {
+    puts "ERROR: Cannot find generated VHDL files. Did you run C synthesis?"
+    exit 1
+  }
+}
+
+exit
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_common.h b/model_1/hls4ml_prj/firmware/ap_types/ap_common.h
new file mode 100644
index 0000000..4d2886c
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_common.h
@@ -0,0 +1,376 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_COMMON_H__
+#define __AP_COMMON_H__
+
+// ----------------------------------------------------------------------
+
+// Forward declaration of all AP types.
+#include <ap_decl.h>
+
+
+#ifdef __SYNTHESIS__
+#error "The open-source version of AP types does not support synthesis."
+#endif // ifdef __SYNTHESIS__
+#define _AP_ENABLE_HALF_ 0
+
+
+#if _AP_ENABLE_HALF_ == 1
+// Before ap_private definition.
+#ifdef __SYNTHESIS__
+#define _HLS_HALF_DEFINED_
+typedef __fp16 half;
+#else
+class half;
+#endif // __SYNTHESIS__
+#endif // _AP_ENABLE_HALF_
+
+// ----------------------------------------------------------------------
+
+// Macro functions
+#define AP_MAX(a, b) ((a) > (b) ? (a) : (b))
+#define AP_MIN(a, b) ((a) < (b) ? (a) : (b))
+#define AP_ABS(a) ((a) >= 0 ? (a) : -(a))
+
+#ifndef AP_ASSERT
+#ifndef __SYNTHESIS__
+#include <assert.h>
+#define AP_ASSERT(cond, msg) assert((cond) && (msg))
+#else
+#define AP_ASSERT(cond, msg)
+#endif // ifndef __SYNTHESIS__
+#endif // ifndef AP_ASSERT
+
+#ifndef __SYNTHESIS__
+// for fprintf messages.
+#include <stdio.h>
+// for exit on error.
+#include <stdlib.h>
+#endif
+
+// same disable condition as assert.
+#if !defined(__SYNTHESIS__) && !defined(NDEBUG)
+
+#define _AP_DEBUG(cond, ...)                  \
+  do {                                        \
+    if ((cond)) {                             \
+      fprintf(stderr, "DEBUG: " __VA_ARGS__); \
+      fprintf(stderr, "\n");                  \
+    }                                         \
+  } while (0)
+#define _AP_WARNING(cond, ...)                  \
+  do {                                          \
+    if ((cond)) {                               \
+      fprintf(stderr, "WARNING: " __VA_ARGS__); \
+      fprintf(stderr, "\n");                    \
+    }                                           \
+  } while (0)
+#define _AP_ERROR(cond, ...)                  \
+  do {                                        \
+    if ((cond)) {                             \
+      fprintf(stderr, "ERROR: " __VA_ARGS__); \
+      fprintf(stderr, "\n");                  \
+      abort();                                \
+    }                                         \
+  } while (0)
+
+#else // if !defined(__SYNTHESIS__) && !defined(NDEBUG)
+
+#define __AP_VOID_CAST static_cast<void>
+#define _AP_DEBUG(cond, ...) (__AP_VOID_CAST(0))
+#define _AP_WARNING(cond, ...) (__AP_VOID_CAST(0))
+#define _AP_ERROR(cond, ...) (__AP_VOID_CAST(0))
+
+#endif // if !defined(__SYNTHESIS__) && !defined(NDEBUG) else
+
+// ----------------------------------------------------------------------
+
+// Attribute only for synthesis
+#ifdef __SYNTHESIS__
+#define INLINE inline __attribute__((always_inline))
+//#define INLINE inline __attribute__((noinline))
+#else
+#define INLINE inline
+#endif
+
+#define AP_WEAK
+// __attribute__((weak))
+
+#ifndef AP_INT_MAX_W
+#define AP_INT_MAX_W 1024
+#endif
+
+#define BIT_WIDTH_UPPER_LIMIT (1 << 15)
+#if AP_INT_MAX_W > BIT_WIDTH_UPPER_LIMIT
+#error "Bitwidth exceeds 32768 (1 << 15), the maximum allowed value"
+#endif
+
+#define MAX_MODE(BITS) ((BITS + 1023) / 1024)
+
+// ----------------------------------------------------------------------
+
+// XXX apcc cannot handle global std::ios_base::Init() brought in by <iostream>
+#ifndef AP_AUTOCC
+#ifndef __SYNTHESIS__
+// for overload operator<<
+#include <iostream>
+#endif
+#endif // ifndef AP_AUTOCC
+
+#ifndef __SYNTHESIS__
+// for string format.
+#include <sstream>
+// for string.
+#include <string>
+#endif
+
+// for detecting if char is signed.
+enum { CHAR_IS_SIGNED = (char)-1 < 0 };
+
+// TODO we have similar traits in x_hls_utils.h, should consider unify.
+namespace _ap_type {
+template <typename _Tp>
+struct is_signed {
+  static const bool value = _Tp(-1) < _Tp(1);
+};
+
+template <typename _Tp>
+struct is_integral {
+  static const bool value = false;
+};
+#define DEF_IS_INTEGRAL(CTYPE)      \
+  template <>                       \
+  struct is_integral<CTYPE> {       \
+    static const bool value = true; \
+  };
+DEF_IS_INTEGRAL(bool)
+DEF_IS_INTEGRAL(char)
+DEF_IS_INTEGRAL(signed char)
+DEF_IS_INTEGRAL(unsigned char)
+DEF_IS_INTEGRAL(short)
+DEF_IS_INTEGRAL(unsigned short)
+DEF_IS_INTEGRAL(int)
+DEF_IS_INTEGRAL(unsigned int)
+DEF_IS_INTEGRAL(long)
+DEF_IS_INTEGRAL(unsigned long)
+DEF_IS_INTEGRAL(ap_slong)
+DEF_IS_INTEGRAL(ap_ulong)
+#undef DEF_IS_INTEGRAL
+
+template <bool, typename _Tp = void>
+struct enable_if {};
+// partial specialization for true
+template <typename _Tp>
+struct enable_if<true, _Tp> {
+  typedef _Tp type;
+};
+
+template <typename _Tp>
+struct remove_const {
+  typedef _Tp type;
+};
+
+template <typename _Tp>
+struct remove_const<_Tp const> {
+  typedef _Tp type;
+};
+} // namespace _ap_type
+
+// ----------------------------------------------------------------------
+
+// Define ssdm_int and _ssdm_op.
+// XXX deleted in open-source version
+
+#ifndef NON_C99STRING
+#define _AP_C99 true
+#else
+#define _AP_C99 false
+#endif
+
+static inline unsigned char guess_radix(const char* s) {
+  unsigned char rd = 10; ///< default radix
+  const char* p = s;
+  // skip neg sign if it exists
+  if (p[0] == '-' || p[0] == '+') ++p;
+  // guess based on following two bits.
+  if (p[0] == '0') {
+    if (p[1] == 'b' || p[1] == 'B') {
+      rd = 2;
+    } else if (p[1] == 'o' || p[1] == 'O') {
+      rd = 8;
+    } else if (p[1] == 'x' || p[1] == 'X') {
+      rd = 16;
+    } else if (p[1] == 'd' || p[1] == 'D') {
+      rd = 10;
+    }
+  }
+  return rd;
+}
+
+// ----------------------------------------------------------------------
+
+// Basic integral struct upon which ap_int and ap_fixed are defined.
+#ifdef __SYNTHESIS__
+// Use ssdm_int, a compiler dependent, attribute constrained integeral type as
+// basic data type.
+#define _AP_ROOT_TYPE ssdm_int
+// Basic ops.
+#define _AP_ROOT_op_concat(Ret, X, Y) _ssdm_op_concat(Ret, X, Y)
+#define _AP_ROOT_op_get_bit(Val, Bit) _ssdm_op_get_bit(Val, Bit)
+#define _AP_ROOT_op_set_bit(Val, Bit, Repl) _ssdm_op_set_bit(Val, Bit, Repl)
+#define _AP_ROOT_op_get_range(Val, Lo, Hi) _ssdm_op_get_range(Val, Lo, Hi)
+#define _AP_ROOT_op_set_range(Val, Lo, Hi, Repl) \
+  _ssdm_op_set_range(Val, Lo, Hi, Repl)
+#define _AP_ROOT_op_reduce(Op, Val) _ssdm_op_reduce(Op, Val)
+#else // ifdef __SYNTHESIS__
+// Use ap_private for compiler-independent basic data type
+template <int _AP_W, bool _AP_S, bool _AP_C = _AP_W <= 64>
+class ap_private;
+/// model ssdm_int in standard C++ for simulation.
+template <int _AP_W, bool _AP_S>
+struct ssdm_int_sim {
+  /// integral type with template-specified width and signedness.
+  ap_private<_AP_W, _AP_S> V;
+  ssdm_int_sim() {}
+};
+#define _AP_ROOT_TYPE ssdm_int_sim
+// private's ref uses _AP_ROOT_TYPE.
+#include <etc/ap_private.h>
+// XXX The C-sim model cannot use GCC-extension
+// Basic ops. Ret and Val are ap_private.
+template <typename _Tp1, typename _Tp2, typename _Tp3>
+inline _Tp1 _AP_ROOT_op_concat(const _Tp1& Ret, const _Tp2& X, const _Tp3& Y) {
+  _Tp1 r = (X).operator,(Y);
+  return r;
+}
+#define _AP_ROOT_op_get_bit(Val, Bit) (Val).get_bit((Bit))
+template <typename _Tp1, typename _Tp2, typename _Tp3>
+inline _Tp1& _AP_ROOT_op_set_bit(_Tp1& Val, const _Tp2& Bit, const _Tp3& Repl) {
+  (Val).set_bit((Bit), (Repl));
+  return Val;
+}
+// notice the order of high and low index is different in ssdm call and
+// ap_private.range()...
+#define _AP_ROOT_op_get_range(Val, Lo, Hi) (Val).range((Hi), (Lo))
+template <typename _Tp1, typename _Tp2, typename _Tp3, typename _Tp4>
+inline _Tp1& _AP_ROOT_op_set_range(_Tp1& Val, const _Tp2& Lo, const _Tp3& Hi,
+                                   const _Tp4& Repl) {
+  (Val).range((Hi), (Lo)) = Repl;
+  return (Val);
+}
+#define _AP_ROOT_op_and_reduce(Val) (Val).and_reduce()
+#define _AP_ROOT_op_nand_reduce(Val) (Val).nand_reduce()
+#define _AP_ROOT_op_or_reduce(Val) (Val).or_reduce()
+#define _AP_ROOT_op_xor_reduce(Val) (Val).xor_reduce()
+// ## is the concatenation in preprocessor:
+#define _AP_ROOT_op_reduce(Op, Val) _AP_ROOT_op_##Op##_reduce(Val)
+#endif // ifdef __SYNTHESIS__ else
+
+// ----------------------------------------------------------------------
+
+// Constants for half, single, double pricision floating points
+#define HALF_MAN 10
+#define FLOAT_MAN 23
+#define DOUBLE_MAN 52
+
+#define HALF_EXP 5
+#define FLOAT_EXP 8
+#define DOUBLE_EXP 11
+
+#define BIAS(e) ((1L << (e - 1L)) - 1L)
+#define HALF_BIAS BIAS(HALF_EXP)
+#define FLOAT_BIAS BIAS(FLOAT_EXP)
+#define DOUBLE_BIAS BIAS(DOUBLE_EXP)
+
+#define APFX_IEEE_DOUBLE_E_MAX DOUBLE_BIAS
+#define APFX_IEEE_DOUBLE_E_MIN (-DOUBLE_BIAS + 1)
+
+INLINE ap_ulong doubleToRawBits(double pf) {
+  union {
+    ap_ulong __L;
+    double __D;
+  } LD;
+  LD.__D = pf;
+  return LD.__L;
+}
+
+INLINE unsigned int floatToRawBits(float pf) {
+  union {
+    unsigned int __L;
+    float __D;
+  } LD;
+  LD.__D = pf;
+  return LD.__L;
+}
+
+#if _AP_ENABLE_HALF_ == 1
+INLINE unsigned short halfToRawBits(half pf) {
+#ifdef __SYNTHESIS__
+  union {
+    unsigned short __L;
+    half __D;
+  } LD;
+  LD.__D = pf;
+  return LD.__L;
+#else
+  return pf.get_bits();
+#endif
+}
+#endif
+
+// usigned long long is at least 64-bit
+INLINE double rawBitsToDouble(ap_ulong pi) {
+  union {
+    ap_ulong __L;
+    double __D;
+  } LD;
+  LD.__L = pi;
+  return LD.__D;
+}
+
+// long is at least 32-bit
+INLINE float rawBitsToFloat(unsigned long pi) {
+  union {
+    unsigned int __L;
+    float __D;
+  } LD;
+  LD.__L = pi;
+  return LD.__D;
+}
+
+#if _AP_ENABLE_HALF_ == 1
+// short is at least 16-bit
+INLINE half rawBitsToHalf(unsigned short pi) {
+#ifdef __SYNTHESIS__
+  union {
+    unsigned short __L;
+    half __D;
+  } LD;
+  LD.__L = pi;
+  return LD.__D;
+#else
+  // sim model of half has a non-trivial constructor
+  half __D;
+  __D.set_bits(pi);
+  return __D;
+#endif
+}
+#endif
+
+#endif // ifndef __AP_COMMON_H__
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_decl.h b/model_1/hls4ml_prj/firmware/ap_types/ap_decl.h
new file mode 100644
index 0000000..ddd00f1
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_decl.h
@@ -0,0 +1,212 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_DECL_H__
+#define __AP_DECL_H__
+
+// ----------------------------------------------------------------------
+
+#if !defined(__AP_FIXED_H__) && !defined(__AP_INT_H__) && !defined(__AUTOPILOT_CBE_H__) && !defined(__HLS_HALF_H__)
+#error "Only ap_fixed.h and ap_int.h can be included directly in user code."
+#endif
+
+// Test __SYNTHESIS__ only for mode
+#if !defined(__SYNTHESIS__) && (defined(AESL_SYN) || defined(__HLS_SYN__))
+//#pragma message "AESL_SYN and __HLS_SYN__ should be replaced by __SYNTHESIS__"
+#define __SYNTHESIS__
+#endif
+
+/* for safety*/
+#if (defined(_AP_N) || defined(_AP_C))
+#error One or more of the following is defined: _AP_N, _AP_C. Definition conflicts with their usage as template parameters.
+#endif
+
+/* for safety*/
+#if (defined(_AP_W) || defined(_AP_I) || defined(_AP_S) || defined(_AP_Q) || \
+     defined(_AP_O) || defined(_AP_W2) || defined(_AP_I2) ||                 \
+     defined(_AP_S2) || defined(_AP_Q2) || defined(_AP_O2) ||                \
+     defined(_AP_N) || defined(_AP_N2))
+#error \
+    "One or more of the following is defined: _AP_W, _AP_I, _AP_S, _AP_Q, _AP_O,  _AP_N, _AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2. Definition conflicts with their usage as template parameters."
+#endif
+
+/*for safety*/
+#if (defined(_AP_W3) || defined(_AP_S3) || defined(_AP_W4) || defined(_AP_S4))
+#error \
+    "One or more of the following is defined: _AP_W3, _AP_S3, _AP_W4,_AP_S4. Definition conflicts with their usage as template parameters."
+#endif
+
+#if (defined(_AP_W1) || defined(_AP_S1) || defined(_AP_T) || \
+     defined(_AP_T1) || defined(_AP_T2) || defined(_AP_T3) || defined(_AP_T4))
+#error \
+    "One or more of the following is defined: _AP_W1, _AP_S1, _AP_T,  _AP_T1, _AP_T2, _AP_T3, _AP_T4. Definition conflicts with their usage as template parameters."
+#endif
+
+#ifndef __cplusplus
+#error "AP data type can only be used in C++"
+#endif
+
+// ----------------------------------------------------------------------
+
+#ifndef __SC_COMPATIBLE__
+/// ap_fixed quantification mode
+enum ap_q_mode {
+  AP_RND,         //< rounding to plus infinity
+  AP_RND_ZERO,    //< rounding to zero
+  AP_RND_MIN_INF, //< rounding to minus infinity
+  AP_RND_INF,     //< rounding to infinity
+  AP_RND_CONV,    //< convergent rounding
+  AP_TRN,         //< truncation
+  AP_TRN_ZERO,    //< truncation to zero
+};
+
+// FIXME for legacy code
+#ifndef SYSTEMC_INCLUDED
+#define SC_RND AP_RND
+#define SC_RND_ZERO AP_RND_ZERO
+#define SC_RND_MIN_INF AP_RND_MIN_INF
+#define SC_RND_INF AP_RND_INF
+#define SC_RND_CONV AP_RND_CONV
+#define SC_TRN AP_TRN
+#define SC_TRN_ZERO AP_TRN_ZERO
+#endif // !defined(SYSTEMC_INCLUDED)
+
+/// ap_fixed saturation mode
+enum ap_o_mode {
+  AP_SAT,      //< saturation
+  AP_SAT_ZERO, //< saturation to zero
+  AP_SAT_SYM,  //< symmetrical saturation
+  AP_WRAP,     //< wrap-around (*)
+  AP_WRAP_SM,  //< sign magnitude wrap-around (*)
+};
+
+// FIXME for legacy code
+#ifndef SYSTEMC_INCLUDED
+#define SC_SAT AP_SAT
+#define SC_SAT_ZERO AP_SAT_ZERO
+#define SC_SAT_SYM AP_SAT_SYM
+#define SC_WRAP AP_WRAP
+#define SC_WRAP_SM AP_WRAP_SM
+#endif // !defined(SYSTEMC_INCLUDED)
+
+#else // defined(__SC_COMPATIBLE__)
+
+// There will not be sc_fxdefs.h, and the emu should be defined by ap_fixed.
+
+/// ap_fixed quantification mode
+enum ap_q_mode {
+  SC_RND,         //< rounding to plus infinity
+  SC_RND_ZERO,    //< rounding to zero
+  SC_RND_MIN_INF, //< rounding to minus infinity
+  SC_RND_INF,     //< rounding to infinity
+  SC_RND_CONV,    //< convergent rounding
+  SC_TRN,         //< truncation
+  SC_TRN_ZERO,    //< truncation to zero
+};
+
+#define AP_RND SC_RND
+#define AP_RND_ZERO SC_RND_ZERO
+#define AP_RND_MIN_INF SC_RND_MIN_INF
+#define AP_RND_INF SC_RND_INF
+#define AP_RND_CONV SC_RND_CONV
+#define AP_TRN SC_TRN
+#define AP_TRN_ZERO SC_TRN_ZERO
+
+/// ap_fixed saturation mode
+enum ap_o_mode {
+  SC_SAT,      //< saturation
+  SC_SAT_ZERO, //< saturation to zero
+  SC_SAT_SYM,  //< symmetrical saturation
+  SC_WRAP,     //< wrap-around (*)
+  SC_WRAP_SM,  //< sign magnitude wrap-around (*)
+};
+
+#define AP_SAT SC_SAT
+#define AP_SAT_ZERO SC_SAT_ZERO
+#define AP_SAT_SYM SC_SAT_SYM
+#define AP_WRAP SC_WRAP
+#define AP_WRAP_SM SC_WRAP_SM
+
+#endif // defined(__SC_COMPATIBLE__)
+
+template <int _AP_W, bool _AP_S>
+struct ap_int_base;
+
+template <int _AP_W>
+struct ap_int;
+
+template <int _AP_W>
+struct ap_uint;
+
+template <int _AP_W, bool _AP_S>
+struct ap_range_ref;
+
+template <int _AP_W, bool _AP_S>
+struct ap_bit_ref;
+
+template <int _AP_W1, typename _AP_T1, int _AP_W2, typename _AP_T2>
+struct ap_concat_ref;
+
+template <int _AP_W, int _AP_I, bool _AP_S = true, ap_q_mode _AP_Q = AP_TRN,
+          ap_o_mode _AP_O = AP_WRAP, int _AP_N = 0>
+struct ap_fixed_base;
+
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q = AP_TRN,
+          ap_o_mode _AP_O = AP_WRAP, int _AP_N = 0>
+struct ap_fixed;
+
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q = AP_TRN,
+          ap_o_mode _AP_O = AP_WRAP, int _AP_N = 0>
+struct ap_ufixed;
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+struct af_range_ref;
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+struct af_bit_ref;
+
+/// string base mode
+enum BaseMode { AP_BIN = 2, AP_OCT = 8, AP_DEC = 10, AP_HEX = 16 };
+
+#ifndef SYSTEMC_INCLUDED
+#define SC_BIN 2
+#define SC_OCT 8
+#define SC_DEC 10
+#define SC_HEX 16
+#endif // !defined(SYSTEMC_INCLUDED)
+
+// Alias C data types
+#ifdef _MSC_VER
+typedef signed __int64 ap_slong;
+typedef unsigned __int64 ap_ulong;
+#else  // !defined(_MSC_VER)
+typedef signed long long ap_slong;
+typedef unsigned long long ap_ulong;
+#endif // !defined(_MSC_VER)
+
+enum {
+  _AP_SIZE_char = 8,
+  _AP_SIZE_short = sizeof(short) * 8,
+  _AP_SIZE_int = sizeof(int) * 8,
+  _AP_SIZE_long = sizeof(long) * 8,
+  _AP_SIZE_ap_slong = sizeof(ap_slong) * 8
+};
+
+#endif // !defined(__AP_DECL_H__)
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_fixed.h b/model_1/hls4ml_prj/firmware/ap_types/ap_fixed.h
new file mode 100644
index 0000000..cd0192b
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_fixed.h
@@ -0,0 +1,360 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_FIXED_H__
+#define __AP_FIXED_H__
+
+#include <ap_common.h>
+#include <ap_fixed_base.h>
+#include <ap_fixed_ref.h>
+
+//---------------------------------------------------------------
+
+/// Signed Arbitrary Precision Fixed-Point Type.
+// default for _AP_Q, _AP_O and _AP_N set in ap_decl.h
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q, ap_o_mode _AP_O, int _AP_N>
+struct ap_fixed : ap_fixed_base<_AP_W, _AP_I, true, _AP_Q, _AP_O, _AP_N> {
+  typedef ap_fixed_base<_AP_W, _AP_I, true, _AP_Q, _AP_O, _AP_N> Base;
+  // Constructor
+  /// default ctor
+  INLINE ap_fixed() : Base() {}
+
+  /// default copy ctor
+  INLINE ap_fixed(const ap_fixed& op) { Base::V = op.V; }
+
+  /// copy ctor from ap_fixed_base.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed(const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2,
+                                      _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed(const volatile ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2,
+                                               _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  //// from ap_fixed
+  //template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+  //          int _AP_N2>
+  //INLINE ap_fixed(
+  //    const ap_fixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+  //    : Base(ap_fixed_base<_AP_W2, _AP_I2, true, _AP_Q2, _AP_O2, _AP_N2>(op)) {}
+
+  //template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+  //          int _AP_N2>
+  //INLINE ap_fixed(
+  //    const volatile ap_fixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+  //    : Base(ap_fixed_base<_AP_W2, _AP_I2, true, _AP_Q2, _AP_O2, _AP_N2>(op)) {}
+
+  //// from ap_ufixed.
+  //template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+  //          int _AP_N2>
+  //INLINE ap_fixed(
+  //    const ap_ufixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+  //    : Base(ap_fixed_base<_AP_W2, _AP_I2, false, _AP_Q2, _AP_O2, _AP_N2>(op)) {
+  //}
+
+  //template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+  //          int _AP_N2>
+  //INLINE ap_fixed(
+  //    const volatile ap_ufixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+  //    : Base(ap_fixed_base<_AP_W2, _AP_I2, false, _AP_Q2, _AP_O2, _AP_N2>(op)) {
+  //}
+
+  /// copy ctor from ap_int_base.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed(const ap_int_base<_AP_W2, _AP_S2>& op) : Base(op) {}
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed(const volatile ap_int_base<_AP_W2, _AP_S2>& op) : Base(op) {}
+
+  //// from ap_int.
+  //template <int _AP_W2>
+  //INLINE ap_fixed(const ap_int<_AP_W2>& op)
+  //    : Base(ap_int_base<_AP_W2, true>(op)) {}
+
+  //template <int _AP_W2>
+  //INLINE ap_fixed(const volatile ap_int<_AP_W2>& op)
+  //    : Base(ap_int_base<_AP_W2, true>(op)) {}
+
+  //// from ap_uint.
+  //template <int _AP_W2>
+  //INLINE ap_fixed(const ap_uint<_AP_W2>& op)
+  //    : Base(ap_int_base<_AP_W2, false>(op)) {}
+
+  //template <int _AP_W2>
+  //INLINE ap_fixed(const volatile ap_uint<_AP_W2>& op)
+  //    : Base(ap_int_base<_AP_W2, false>(op)) {}
+
+  // from ap_bit_ref.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed(const ap_bit_ref<_AP_W2, _AP_S2>& op) : Base(op) {}
+
+  // from ap_range_ref.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed(const ap_range_ref<_AP_W2, _AP_S2>& op) : Base(op) {}
+
+  // from ap_concat_ref.
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_fixed(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& op)
+      : Base(op) {}
+
+  // from af_bit_ref.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  // from af_range_ref.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+// from c types.
+#define CTOR(TYPE) \
+  INLINE ap_fixed(TYPE v) : Base(v) {}
+
+  CTOR(bool)
+  CTOR(char)
+  CTOR(signed char)
+  CTOR(unsigned char)
+  CTOR(short)
+  CTOR(unsigned short)
+  CTOR(int)
+  CTOR(unsigned int)
+  CTOR(long)
+  CTOR(unsigned long)
+  CTOR(ap_slong)
+  CTOR(ap_ulong)
+#if _AP_ENABLE_HALF_ == 1
+  CTOR(half)
+#endif
+  CTOR(float)
+  CTOR(double)
+#undef CTOR
+
+  INLINE ap_fixed(const char* s) : Base(s) {}
+
+  INLINE ap_fixed(const char* s, signed char rd) : Base(s, rd) {}
+
+  // Assignment
+  // The assignment operator is technically inherited; however, it is always
+  // hidden by an explicitly or implicitly defined assignment operator for the
+  // derived class.
+  /* XXX ctor will be used when right is not of proper type. */
+  INLINE ap_fixed& operator=(
+      const ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N>& op) {
+    Base::V = op.V;
+    return *this;
+  }
+
+  INLINE void operator=(
+      const ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N>& op) volatile {
+    Base::V = op.V;
+  }
+
+  INLINE ap_fixed& operator=(
+      const volatile ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N>& op) {
+    Base::V = op.V;
+    return *this;
+  }
+
+  INLINE void operator=(
+      const volatile ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N>& op) volatile {
+    Base::V = op.V;
+  }
+}; // struct ap_fixed.
+
+//-------------------------------------------------------------------
+
+// Unsigned Arbitrary Precision Fixed-Point Type.
+// default for _AP_Q, _AP_O and _AP_N set in ap_decl.h
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q, ap_o_mode _AP_O, int _AP_N>
+struct ap_ufixed : ap_fixed_base<_AP_W, _AP_I, false, _AP_Q, _AP_O, _AP_N> {
+  typedef ap_fixed_base<_AP_W, _AP_I, false, _AP_Q, _AP_O, _AP_N> Base;
+  // Constructor
+  /// default ctor
+  INLINE ap_ufixed() : Base() {}
+
+  /// default copy ctor
+  INLINE ap_ufixed(const ap_ufixed& op) { Base::V = op.V; }
+
+  /// copy ctor from ap_fixed_base
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_ufixed(const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2,
+                                       _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  /// copy ctor from ap_fixed_base
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_ufixed(const volatile ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2,
+                                                _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  //template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+  //          int _AP_N2>
+  //INLINE ap_ufixed(
+  //    const ap_fixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+  //    : Base(ap_fixed_base<_AP_W2, _AP_I2, true, _AP_Q2, _AP_O2, _AP_N2>(op)) {}
+
+  //template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+  //          int _AP_N2>
+  //INLINE ap_ufixed(
+  //    const volatile ap_fixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+  //    : Base(ap_fixed_base<_AP_W2, _AP_I2, true, _AP_Q2, _AP_O2, _AP_N2>(op)) {}
+
+  //template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+  //          int _AP_N2>
+  //INLINE ap_ufixed(
+  //    const ap_ufixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+  //    : Base(ap_fixed_base<_AP_W2, _AP_I2, false, _AP_Q2, _AP_O2, _AP_N2>(op)) {
+  //}
+
+  //template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+  //          int _AP_N2>
+  //INLINE ap_ufixed(
+  //    const volatile ap_ufixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+  //    : Base(ap_fixed_base<_AP_W2, _AP_I2, false, _AP_Q2, _AP_O2, _AP_N2>(op)) {
+  //}
+
+  /// copy ctor from ap_int_base.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_ufixed(const ap_int_base<_AP_W2, _AP_S2>& op) : Base(op) {}
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_ufixed(const volatile ap_int_base<_AP_W2, _AP_S2>& op) : Base(op) {}
+
+  //template <int _AP_W2>
+  //INLINE ap_ufixed(const ap_int<_AP_W2>& op)
+  //    : Base(ap_int_base<_AP_W2, true>(op)) {}
+
+  //template <int _AP_W2>
+  //INLINE ap_ufixed(const volatile ap_int<_AP_W2>& op)
+  //    : Base(ap_int_base<_AP_W2, true>(op)) {}
+
+  //template <int _AP_W2>
+  //INLINE ap_ufixed(const ap_uint<_AP_W2>& op)
+  //    : Base(ap_int_base<_AP_W2, false>(op)) {}
+
+  //template <int _AP_W2>
+  //INLINE ap_ufixed(const volatile ap_uint<_AP_W2>& op)
+  //    : Base(ap_int_base<_AP_W2, false>(op)) {}
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_ufixed(const ap_bit_ref<_AP_W2, _AP_S2>& op) : Base(op) {}
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_ufixed(const ap_range_ref<_AP_W2, _AP_S2>& op) : Base(op) {}
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_ufixed(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& op)
+      : Base(op) {}
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_ufixed(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_ufixed(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+#define CTOR(TYPE) \
+  INLINE ap_ufixed(TYPE v) : Base(v) {}
+
+  CTOR(bool)
+  CTOR(char)
+  CTOR(signed char)
+  CTOR(unsigned char)
+  CTOR(short)
+  CTOR(unsigned short)
+  CTOR(int)
+  CTOR(unsigned int)
+  CTOR(long)
+  CTOR(unsigned long)
+  CTOR(ap_slong)
+  CTOR(ap_ulong)
+#if _AP_ENABLE_HALF_ == 1
+  CTOR(half)
+#endif
+  CTOR(float)
+  CTOR(double)
+#undef CTOR
+
+  INLINE ap_ufixed(const char* s) : Base(s) {}
+
+  INLINE ap_ufixed(const char* s, signed char rd) : Base(s, rd) {}
+
+  // Assignment
+  INLINE ap_ufixed& operator=(
+      const ap_ufixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N>& op) {
+    Base::V = op.V;
+    return *this;
+  }
+
+  INLINE void operator=(
+      const ap_ufixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N>& op) volatile {
+    Base::V = op.V;
+  }
+
+  INLINE ap_ufixed& operator=(
+      const volatile ap_ufixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N>& op) {
+    Base::V = op.V;
+    return *this;
+  }
+
+  INLINE void operator=(const volatile ap_ufixed<_AP_W, _AP_I, _AP_Q, _AP_O,
+                                                 _AP_N>& op) volatile {
+    Base::V = op.V;
+  }
+}; // struct ap_ufixed
+
+
+#if !defined(__SYNTHESIS__) && (defined(SYSTEMC_H) || defined(SYSTEMC_INCLUDED))
+// XXX sc_trace overload for ap_fixed is already included in
+// "ap_sysc/ap_sc_extras.h", so do not define in synthesis.
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q, ap_o_mode _AP_O, int _AP_N>
+INLINE void sc_trace(sc_core::sc_trace_file* tf,
+                     const ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N>& op,
+                     const std::string& name) {
+  tf->trace(sc_dt::sc_lv<_AP_W>(op.to_string(2).c_str()), name);
+}
+
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q, ap_o_mode _AP_O, int _AP_N>
+INLINE void sc_trace(sc_core::sc_trace_file* tf,
+                     const ap_ufixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N>& op,
+                     const std::string& name) {
+  tf->trace(sc_dt::sc_lv<_AP_W>(op.to_string(2).c_str()), name);
+}
+#endif // System C sim
+
+// Specialization of std containers, so that std::complex<ap_fixed> can have its
+// image part automatically zero-initialized when only real part is provided.
+#include <ap_fixed_special.h>
+
+#endif // ifndef __AP_FIXED_H__
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_fixed_base.h b/model_1/hls4ml_prj/firmware/ap_types/ap_fixed_base.h
new file mode 100644
index 0000000..1d94b93
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_fixed_base.h
@@ -0,0 +1,2354 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_FIXED_BASE_H__
+#define __AP_FIXED_BASE_H__
+
+#ifndef __AP_FIXED_H__
+#error "Only ap_fixed.h and ap_int.h can be included directly in user code."
+#endif
+
+// for ap_int_base and its reference types.
+#include <ap_int.h>
+#ifndef __SYNTHESIS__
+#if _AP_ENABLE_HALF_ == 1
+// for half type
+#include <hls_half.h>
+#endif
+// for std io
+#include <iostream>
+#endif
+
+#ifndef __cplusplus
+#error "C++ is required to include this header file"
+#else // __cplusplus
+
+// for warning on unsupported rounding mode in conversion to float/double.
+#if !defined(__SYNTHESIS__) && __cplusplus >= 201103L && \
+    (defined(__gnu_linux__) || defined(_WIN32))
+#define AP_FIXED_ENABLE_CPP_FENV 1
+#include <cfenv>
+#endif
+
+// ----------------------------------------------------------------------
+
+/* Major TODO
+  long double support: constructor, assign and other operators.
+  binary operators with ap_fixed_base and const char*.
+  return ap_fixed/ap_ufixed when result signedness is known.
+*/
+
+// Helper function in conversion to floating point types.
+
+#ifdef __SYNTHESIS__
+#define _AP_ctype_op_get_bit(var, index) _AP_ROOT_op_get_bit(var, index)
+#define _AP_ctype_op_set_bit(var, index, x) _AP_ROOT_op_set_bit(var, index, x)
+#define _AP_ctype_op_get_range(var, low, high) \
+  _AP_ROOT_op_get_range(var, low, high)
+#define _AP_ctype_op_set_range(var, low, high, x) \
+  _AP_ROOT_op_set_range(var, low, high, x)
+#else // ifdef __SYNTHESIS__
+template <typename _Tp1, typename _Tp2>
+inline bool _AP_ctype_op_get_bit(_Tp1& var, const _Tp2& index) {
+  return !!(var & (1ull << (index)));
+}
+template <typename _Tp1, typename _Tp2, typename _Tp3>
+inline _Tp1 _AP_ctype_op_set_bit(_Tp1& var, const _Tp2& index, const _Tp3& x) {
+  var |= (((x) ? 1ull : 0ull) << (index));
+  return var;
+}
+template <typename _Tp1, typename _Tp2, typename _Tp3>
+inline _Tp1 _AP_ctype_op_get_range(_Tp1& var, const _Tp2& low,
+                                   const _Tp3& high) {
+  _Tp1 r = var;
+  ap_ulong mask = -1ll;
+  mask >>= (sizeof(_Tp1) * 8 - ((high) - (low) + 1));
+  r >>= (low);
+  r &= mask;
+  return r;
+}
+template <typename _Tp1, typename _Tp2, typename _Tp3, typename _Tp4>
+inline _Tp1 _AP_ctype_op_set_range(_Tp1& var, const _Tp2& low, const _Tp3& high,
+                                   const _Tp4& x) {
+  ap_ulong mask = -1ll;
+  mask >>= (_AP_SIZE_ap_slong - ((high) - (low) + 1));
+  var &= ~(mask << (low));
+  var |= ((mask & x) << (low));
+  return var;
+}
+#endif // ifdef __SYNTHESIS__
+
+
+// trait for letting base class to return derived class.
+// Notice that derived class template is incomplete, and we cannot use
+// the member of the derived class.
+template <int _AP_W2, int _AP_I2, bool _AP_S2>
+struct _ap_fixed_factory;
+template <int _AP_W2, int _AP_I2>
+struct _ap_fixed_factory<_AP_W2, _AP_I2, true> {
+  typedef ap_fixed<_AP_W2, _AP_I2> type;
+};
+template <int _AP_W2, int _AP_I2>
+struct _ap_fixed_factory<_AP_W2, _AP_I2, false> {
+  typedef ap_ufixed<_AP_W2, _AP_I2> type;
+};
+
+/// ap_fixed_base: AutoPilot fixed point.
+/** partial specialization of signed.
+  @tparam _AP_W width.
+  @tparam _AP_I integral part width.
+  @tparam _AP_S signed.
+  @tparam _AP_Q quantization mode. Default is AP_TRN.
+  @tparam _AP_O saturation mode. Default is AP_WRAP.
+  @tparam _AP_N saturation wrap value. Default is 0.
+ */
+// default for _AP_Q, _AP_O and _AP_N set in ap_decl.h
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+struct ap_fixed_base : _AP_ROOT_TYPE<_AP_W, _AP_S> {
+ public:
+  typedef _AP_ROOT_TYPE<_AP_W, _AP_S> Base;
+  static const int width = _AP_W;
+  static const int iwidth = _AP_I;
+  static const ap_q_mode qmode = _AP_Q;
+  static const ap_o_mode omode = _AP_O;
+
+  /// Return type trait.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2>
+  struct RType {
+    enum {
+      _AP_F = _AP_W - _AP_I,
+      F2 = _AP_W2 - _AP_I2,
+      mult_w = _AP_W + _AP_W2,
+      mult_i = _AP_I + _AP_I2,
+      mult_s = _AP_S || _AP_S2,
+      plus_w = AP_MAX(_AP_I + (_AP_S2 && !_AP_S), _AP_I2 + (_AP_S && !_AP_S2)) +
+               1 + AP_MAX(_AP_F, F2),
+      plus_i =
+          AP_MAX(_AP_I + (_AP_S2 && !_AP_S), _AP_I2 + (_AP_S && !_AP_S2)) + 1,
+      plus_s = _AP_S || _AP_S2,
+      minus_w =
+          AP_MAX(_AP_I + (_AP_S2 && !_AP_S), _AP_I2 + (_AP_S && !_AP_S2)) + 1 +
+          AP_MAX(_AP_F, F2),
+      minus_i =
+          AP_MAX(_AP_I + (_AP_S2 && !_AP_S), _AP_I2 + (_AP_S && !_AP_S2)) + 1,
+      minus_s = true,
+#ifndef __SC_COMPATIBLE__
+      div_w = _AP_S2 + _AP_W + AP_MAX(F2, 0),
+#else
+      div_w = _AP_S2 + _AP_W + AP_MAX(F2, 0) + AP_MAX(_AP_I2, 0),
+#endif
+      div_i = _AP_S2 + _AP_I + F2,
+      div_s = _AP_S || _AP_S2,
+      logic_w =
+          AP_MAX(_AP_I + (_AP_S2 && !_AP_S), _AP_I2 + (_AP_S && !_AP_S2)) +
+          AP_MAX(_AP_F, F2),
+      logic_i = AP_MAX(_AP_I + (_AP_S2 && !_AP_S), _AP_I2 + (_AP_S && !_AP_S2)),
+      logic_s = _AP_S || _AP_S2
+    };
+
+    typedef ap_fixed_base<_AP_W, _AP_I, _AP_S> lhs;
+    typedef ap_fixed_base<_AP_W2, _AP_I2, _AP_S2> rhs;
+
+    typedef ap_fixed_base<mult_w, mult_i, mult_s> mult_base;
+    typedef ap_fixed_base<plus_w, plus_i, plus_s> plus_base;
+    typedef ap_fixed_base<minus_w, minus_i, minus_s> minus_base;
+    typedef ap_fixed_base<logic_w, logic_i, logic_s> logic_base;
+    typedef ap_fixed_base<div_w, div_i, div_s> div_base;
+    typedef ap_fixed_base<_AP_W, _AP_I, _AP_S> arg1_base;
+
+    typedef typename _ap_fixed_factory<mult_w, mult_i, mult_s>::type mult;
+    typedef typename _ap_fixed_factory<plus_w, plus_i, plus_s>::type plus;
+    typedef typename _ap_fixed_factory<minus_w, minus_i, minus_s>::type minus;
+    typedef typename _ap_fixed_factory<logic_w, logic_i, logic_s>::type logic;
+    typedef typename _ap_fixed_factory<div_w, div_i, div_s>::type div;
+    typedef typename _ap_fixed_factory<_AP_W, _AP_I, _AP_S>::type arg1;
+  };
+
+ private:
+#ifndef __SYNTHESIS__
+  // This cannot handle hex float format string.
+  void fromString(const std::string& val, unsigned char radix) {
+    _AP_ERROR(!(radix == 2 || radix == 8 || radix == 10 || radix == 16),
+              "ap_fixed_base::fromString(%s, %d)", val.c_str(), radix);
+
+    Base::V = 0;
+    int startPos = 0;
+    int endPos = val.length();
+    int decPos = val.find(".");
+    if (decPos == -1) decPos = endPos;
+
+    // handle sign
+    bool isNegative = false;
+    if (val[0] == '-') {
+      isNegative = true;
+      ++startPos;
+    } else if (val[0] == '+')
+      ++startPos;
+
+    // If there are no integer bits, e.g.:
+    // .0000XXXX, then keep at least one bit.
+    // If the width is greater than the number of integer bits, e.g.:
+    // XXXX.XXXX, then we keep the integer bits
+    // if the number of integer bits is greater than the width, e.g.:
+    // XXX000 then we keep the integer bits.
+    // Always keep one bit.
+    ap_fixed_base<AP_MAX(_AP_I, 4) + 4, AP_MAX(_AP_I, 4) + 4, false>
+        integer_bits = 0;
+
+    // Figure out if we can shift instead of multiply
+    unsigned shift = (radix == 16 ? 4 : radix == 8 ? 3 : radix == 2 ? 1 : 0);
+
+    //std::cout << "\n\n" << val << "\n";
+    //std::cout << startPos << " " << decPos << " " << endPos << "\n";
+
+    bool sticky_int = false;
+
+    // Traverse the integer digits from the MSD, multiplying by radix as we go.
+    for (int i = startPos; i < decPos; i++) {
+      // Get a digit
+      char cdigit = val[i];
+      if (cdigit == '\0') continue;
+      unsigned digit = ap_private_ops::decode_digit(cdigit, radix);
+
+      sticky_int |= integer_bits[AP_MAX(_AP_I, 4) + 4 - 1] |
+                    integer_bits[AP_MAX(_AP_I, 4) + 4 - 2] |
+                    integer_bits[AP_MAX(_AP_I, 4) + 4 - 3] |
+                    integer_bits[AP_MAX(_AP_I, 4) + 4 - 4];
+      // Shift or multiply the value by the radix
+      if (shift)
+        integer_bits <<= shift;
+      else
+        integer_bits *= radix;
+
+      // Add in the digit we just interpreted
+      integer_bits += digit;
+      //std::cout << "idigit = " << digit << " " << integer_bits.to_string()
+      //    << "  " << sticky_int <<  "\n";
+    }
+    integer_bits[AP_MAX(_AP_I, 4) + 4 - 3] =
+        integer_bits[AP_MAX(_AP_I, 4) + 4 - 3] | sticky_int;
+
+    ap_fixed_base<AP_MAX(_AP_W - _AP_I, 0) + 4 + 4, 4, false> fractional_bits = 0;
+    bool sticky = false;
+
+    // Traverse the fractional digits from the LSD, dividing by radix as we go.
+    for (int i = endPos - 1; i >= decPos + 1; i--) {
+      // Get a digit
+      char cdigit = val[i];
+      if (cdigit == '\0') continue;
+      unsigned digit = ap_private_ops::decode_digit(cdigit, radix);
+      // Add in the digit we just interpreted
+      fractional_bits += digit;
+
+      sticky |= fractional_bits[0] | fractional_bits[1] | fractional_bits[2] |
+                fractional_bits[3];
+      // Shift or divide the value by the radix
+      if (shift)
+        fractional_bits >>= shift;
+      else
+        fractional_bits /= radix;
+
+      //std::cout << "fdigit = " << digit << " " << fractional_bits.to_string()
+      //    << " " << sticky << "\n";
+    }
+
+    //std::cout << "Int =" << integer_bits.to_string() << " " <<
+    //    fractional_bits.to_string() << "\n";
+
+    fractional_bits[0] = fractional_bits[0] | sticky;
+
+    if (isNegative)
+      *this = -(integer_bits + fractional_bits);
+    else
+      *this = integer_bits + fractional_bits;
+
+    //std::cout << "end = " << this->to_string(16) << "\n";
+  }
+
+  /// report invalid constrction of ap_fixed_base
+  INLINE void report() {
+    if (!_AP_S && _AP_O == AP_WRAP_SM) {
+      fprintf(stderr, "ap_ufxied<...> cannot support AP_WRAP_SM.\n");
+      exit(1);
+    }
+    if (_AP_W > MAX_MODE(AP_INT_MAX_W) * 1024) {
+      fprintf(stderr,
+              "[E] ap_%sfixed<%d, ...>: Bitwidth exceeds the "
+              "default max value %d. Please use macro "
+              "AP_INT_MAX_W to set a larger max value.\n",
+              _AP_S ? "" : "u", _AP_W, MAX_MODE(AP_INT_MAX_W) * 1024);
+      exit(1);
+    }
+  }
+#else
+  INLINE void report() {}
+#endif // ifdef __SYNTHESIS__
+
+  /// @name helper functions.
+  //  @{
+  INLINE void overflow_adjust(bool underflow, bool overflow, bool lD,
+                              bool sign) {
+    if (!underflow && !overflow) return;
+    if (_AP_O == AP_WRAP) {
+      if (_AP_N == 0) return;
+      if (_AP_S) {
+        // signed AP_WRAP
+        // n_bits == 1
+        Base::V = _AP_ROOT_op_set_bit(Base::V, _AP_W - 1, sign);
+        if (_AP_N > 1) {
+          // n_bits > 1
+          ap_int_base<_AP_W, false> mask(-1);
+          if (sign) mask.V = 0;
+          Base::V =
+              _AP_ROOT_op_set_range(Base::V, _AP_W - _AP_N, _AP_W - 2, mask.V);
+        }
+      } else {
+        // unsigned AP_WRAP
+        ap_int_base<_AP_W, false> mask(-1);
+        Base::V =
+            _AP_ROOT_op_set_range(Base::V, _AP_W - _AP_N, _AP_W - 1, mask.V);
+      }
+    } else if (_AP_O == AP_SAT_ZERO) {
+      Base::V = 0;
+    } else if (_AP_O == AP_WRAP_SM && _AP_S) {
+      bool Ro = _AP_ROOT_op_get_bit(Base::V, _AP_W - 1);
+      if (_AP_N == 0) {
+        if (lD != Ro) {
+          Base::V = ~Base::V;
+          Base::V = _AP_ROOT_op_set_bit(Base::V, _AP_W - 1, lD);
+        }
+      } else {
+        if (_AP_N == 1 && sign != Ro) {
+          Base::V = ~Base::V;
+        } else if (_AP_N > 1) {
+          bool lNo = _AP_ROOT_op_get_bit(Base::V, _AP_W - _AP_N);
+          if (lNo == sign) Base::V = ~Base::V;
+          ap_int_base<_AP_W, false> mask(-1);
+          if (sign) mask.V = 0;
+          Base::V =
+              _AP_ROOT_op_set_range(Base::V, _AP_W - _AP_N, _AP_W - 2, mask.V);
+        }
+        Base::V = _AP_ROOT_op_set_bit(Base::V, _AP_W - 1, sign);
+      }
+    } else {
+      if (_AP_S) {
+        if (overflow) {
+          Base::V = 1;
+          Base::V <<= _AP_W - 1;
+          Base::V = ~Base::V;
+        } else if (underflow) {
+          Base::V = 1;
+          Base::V <<= _AP_W - 1;
+          if (_AP_O == AP_SAT_SYM) Base::V |= 1;
+        }
+      } else {
+        if (overflow)
+          Base::V = ~(ap_int_base<_AP_W, false>(0).V);
+        else if (underflow)
+          Base::V = 0;
+      }
+    }
+  }
+
+  INLINE bool quantization_adjust(bool qb, bool r, bool s) {
+    bool carry = (bool)_AP_ROOT_op_get_bit(Base::V, _AP_W - 1);
+    if (_AP_Q == AP_TRN) return false;
+    if (_AP_Q == AP_RND_ZERO)
+      qb &= s || r;
+    else if (_AP_Q == AP_RND_MIN_INF)
+      qb &= r;
+    else if (_AP_Q == AP_RND_INF)
+      qb &= !s || r;
+    else if (_AP_Q == AP_RND_CONV)
+      qb &= _AP_ROOT_op_get_bit(Base::V, 0) || r;
+    else if (_AP_Q == AP_TRN_ZERO)
+      qb = s && (qb || r);
+    Base::V += qb;
+    return carry && (!(bool)_AP_ROOT_op_get_bit(Base::V, _AP_W - 1));
+  }
+  //  @}
+
+ public:
+  /// @name constructors.
+  //  @{
+  /// default ctor.
+  INLINE ap_fixed_base() {}
+
+  /// copy ctor.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    operator=(op);
+    report();
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base(
+      const volatile ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    operator=(op);
+    report();
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed_base(const ap_int_base<_AP_W2, _AP_S2>& op) {
+    ap_fixed_base<_AP_W2, _AP_W2, _AP_S2> tmp;
+    tmp.V = op.V;
+    operator=(tmp);
+    report();
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed_base(const volatile ap_int_base<_AP_W2, _AP_S2>& op) {
+    ap_fixed_base<_AP_W2, _AP_W2, _AP_S2> tmp;
+    tmp.V = op.V;
+    operator=(tmp);
+    report();
+  }
+
+#ifndef __SYNTHESIS__
+#ifndef NON_C99STRING
+  INLINE ap_fixed_base(const char* s, signed char rd = 0) {
+    unsigned char radix = rd;
+    std::string str = ap_private_ops::parseString(s, radix); // will guess rd, default 10
+    _AP_ERROR(radix == 0, "ap_fixed_base(const char* \"%s\", %d), str=%s, radix = %d",
+              s, rd, str.c_str(), radix); // TODO remove this check
+    fromString(str, radix);
+  }
+#else
+  INLINE ap_fixed_base(const char* s, signed char rd = 10) {
+    ap_int_base<_AP_W, _AP_S> t(s, rd);
+    Base::V = t.V;
+  }
+#endif // ifndef NON_C99STRING
+#else // ifndef __SYNTHESIS__
+  // XXX _ssdm_string2bits only takes const string and const radix.
+  // It seems XFORM will do compile time processing of the string.
+  INLINE ap_fixed_base(const char* s) {
+    typeof(Base::V) t;
+    _ssdm_string2bits((void*)(&t), (const char*)(s), 10, _AP_I, _AP_S, _AP_Q,
+                      _AP_O, _AP_N, _AP_C99);
+    Base::V = t;
+  }
+  INLINE ap_fixed_base(const char* s, signed char rd) {
+    typeof(Base::V) t;
+    _ssdm_string2bits((void*)(&t), (const char*)(s), rd, _AP_I, _AP_S, _AP_Q,
+                      _AP_O, _AP_N, _AP_C99);
+    Base::V = t;
+  }
+#endif // ifndef __SYNTHESIS__ else
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed_base(const ap_bit_ref<_AP_W2, _AP_S2>& op) {
+    *this = ((bool)op);
+    report();
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed_base(const ap_range_ref<_AP_W2, _AP_S2>& op) {
+    *this = (ap_int_base<_AP_W2, false>(op));
+    report();
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_fixed_base(
+      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& op) {
+    *this = (ap_int_base<_AP_W2 + _AP_W3, false>(op));
+    report();
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    *this = (bool(op));
+    report();
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    *this = (ap_int_base<_AP_W2, false>(op));
+    report();
+  }
+
+  // ctors from c types.
+  // make a temp ap_fixed_base first, and use ap_fixed_base.operator=
+#define CTOR_FROM_INT(C_TYPE, _AP_W2, _AP_S2)        \
+  INLINE ap_fixed_base(const C_TYPE x) {             \
+    ap_fixed_base<(_AP_W2), (_AP_W2), (_AP_S2)> tmp; \
+    tmp.V = x;                                       \
+    *this = tmp;                                     \
+  }
+
+  CTOR_FROM_INT(bool, 1, false)
+  CTOR_FROM_INT(char, 8, CHAR_IS_SIGNED)
+  CTOR_FROM_INT(signed char, 8, true)
+  CTOR_FROM_INT(unsigned char, 8, false)
+  CTOR_FROM_INT(short, _AP_SIZE_short, true)
+  CTOR_FROM_INT(unsigned short, _AP_SIZE_short, false)
+  CTOR_FROM_INT(int, _AP_SIZE_int, true)
+  CTOR_FROM_INT(unsigned int, _AP_SIZE_int, false)
+  CTOR_FROM_INT(long, _AP_SIZE_long, true)
+  CTOR_FROM_INT(unsigned long, _AP_SIZE_long, false)
+  CTOR_FROM_INT(ap_slong, _AP_SIZE_ap_slong, true)
+  CTOR_FROM_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+#undef CTOR_FROM_INT
+/*
+ * TODO:
+ *Theere used to be several funtions which were AP_WEAK.
+ *Now they're all INLINE expect ap_fixed_base(double d)
+ *Maybe we can use '#pragma HLS inline' instead of INLINE.
+ */
+  AP_WEAK ap_fixed_base(double d) {
+    ap_int_base<64, false> ireg;
+    ireg.V = doubleToRawBits(d);
+    bool isneg = _AP_ROOT_op_get_bit(ireg.V, 63);
+
+    ap_int_base<DOUBLE_EXP + 1, true> exp;
+    ap_int_base<DOUBLE_EXP, false> exp_tmp;
+    exp_tmp.V =
+        _AP_ROOT_op_get_range(ireg.V, DOUBLE_MAN, DOUBLE_MAN + DOUBLE_EXP - 1);
+    exp = exp_tmp - DOUBLE_BIAS;
+    ap_int_base<DOUBLE_MAN + 2, true> man;
+    man.V = _AP_ROOT_op_get_range(ireg.V, 0, DOUBLE_MAN - 1);
+    // do not support NaN
+    _AP_WARNING(exp == APFX_IEEE_DOUBLE_E_MAX + 1 && man.V != 0,
+                "assign NaN to fixed point value");
+    man.V = _AP_ROOT_op_set_bit(man.V, DOUBLE_MAN, 1);
+    if (isneg) man = -man;
+    if ((ireg.V & 0x7fffffffffffffffLL) == 0) {
+      Base::V = 0;
+    } else {
+      int _AP_W2 = DOUBLE_MAN + 2, _AP_I2 = exp.V + 2, _AP_F = _AP_W - _AP_I,
+          F2 = _AP_W2 - _AP_I2;
+      bool _AP_S2 = true,
+           QUAN_INC = F2 > _AP_F &&
+                      !(_AP_Q == AP_TRN || (_AP_Q == AP_TRN_ZERO && !_AP_S2));
+      bool carry = false;
+      // handle quantization
+      unsigned sh_amt = (F2 > _AP_F) ? F2 - _AP_F : _AP_F - F2;
+      if (F2 == _AP_F)
+        Base::V = man.V;
+      else if (F2 > _AP_F) {
+        if (sh_amt < DOUBLE_MAN + 2)
+          Base::V = man.V >> sh_amt;
+        else {
+          Base::V = isneg ? -1 : 0;
+        }
+        if ((_AP_Q != AP_TRN) && !((_AP_Q == AP_TRN_ZERO) && !_AP_S2)) {
+          bool qb = (F2 - _AP_F > _AP_W2) ? isneg : (bool)_AP_ROOT_op_get_bit(
+                                                        man.V, F2 - _AP_F - 1);
+          bool r =
+              (F2 > _AP_F + 1)
+                  ? _AP_ROOT_op_get_range(man.V, 0, (F2 - _AP_F - 2 < _AP_W2)
+                                                        ? (F2 - _AP_F - 2)
+                                                        : (_AP_W2 - 1)) != 0
+                  : false;
+          carry = quantization_adjust(qb, r, isneg);
+        }
+      } else { // no quantization
+        Base::V = man.V;
+        if (sh_amt < _AP_W)
+          Base::V = Base::V << sh_amt;
+        else
+          Base::V = 0;
+      }
+      // handle overflow/underflow
+      if ((_AP_O != AP_WRAP || _AP_N != 0) &&
+          ((!_AP_S && _AP_S2) ||
+           _AP_I - _AP_S <
+               _AP_I2 - _AP_S2 +
+                   (QUAN_INC ||
+                    (_AP_S2 && (_AP_O == AP_SAT_SYM))))) { // saturation
+        bool deleted_zeros = _AP_S2 ? true : !carry, deleted_ones = true;
+        bool neg_src = isneg;
+        bool lD = false;
+        int pos1 = F2 - _AP_F + _AP_W;
+        int pos2 = F2 - _AP_F + _AP_W + 1;
+        bool newsignbit = _AP_ROOT_op_get_bit(Base::V, _AP_W - 1);
+        if (pos1 < _AP_W2 && pos1 >= 0)
+          // lD = _AP_ROOT_op_get_bit(man.V, pos1);
+          lD = (man.V >> pos1) & 1;
+        if (pos1 < _AP_W2) {
+          bool Range1_all_ones = true;
+          bool Range1_all_zeros = true;
+          bool Range2_all_ones = true;
+          ap_int_base<DOUBLE_MAN + 2, false> Range2;
+          ap_int_base<DOUBLE_MAN + 2, false> all_ones(-1);
+
+          if (pos2 >= 0 && pos2 < _AP_W2) {
+            // Range2.V = _AP_ROOT_op_get_range(man.V,
+            //                        pos2, _AP_W2 - 1);
+            Range2.V = man.V;
+            Range2.V >>= pos2;
+            Range2_all_ones = Range2 == (all_ones >> pos2);
+          } else if (pos2 < 0)
+            Range2_all_ones = false;
+          if (pos1 >= 0 && pos2 < _AP_W2) {
+            Range1_all_ones = Range2_all_ones && lD;
+            Range1_all_zeros = !Range2.V && !lD;
+          } else if (pos2 == _AP_W2) {
+            Range1_all_ones = lD;
+            Range1_all_zeros = !lD;
+          } else if (pos1 < 0) {
+            Range1_all_zeros = !man.V;
+            Range1_all_ones = false;
+          }
+
+          deleted_zeros =
+              deleted_zeros && (carry ? Range1_all_ones : Range1_all_zeros);
+          deleted_ones =
+              carry ? Range2_all_ones && (pos1 < 0 || !lD) : Range1_all_ones;
+          neg_src = isneg && !(carry && Range1_all_ones);
+        } else
+          neg_src = isneg && newsignbit;
+        bool neg_trg = _AP_S && newsignbit;
+        bool overflow = (neg_trg || !deleted_zeros) && !isneg;
+        bool underflow = (!neg_trg || !deleted_ones) && neg_src;
+        if ((_AP_O == AP_SAT_SYM) && _AP_S2 && _AP_S)
+          underflow |=
+              neg_src &&
+              (_AP_W > 1 ? _AP_ROOT_op_get_range(Base::V, 0, _AP_W - 2) == 0
+                         : true);
+        overflow_adjust(underflow, overflow, lD, neg_src);
+      }
+    }
+    report();
+  }
+
+  // TODO more optimized implementation.
+  INLINE ap_fixed_base(float d) { *this = ap_fixed_base(double(d)); }
+
+#if _AP_ENABLE_HALF_ == 1
+  // TODO more optimized implementation.
+  INLINE ap_fixed_base(half d) { *this = ap_fixed_base(double(d)); }
+#endif
+  //  @}
+
+  /// @name assign operator
+  /// assign, using another ap_fixed_base of same template parameters.
+  /*
+  INLINE ap_fixed_base& operator=(
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) {
+    Base::V = op.V;
+    return *this;
+  }
+  */
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base& operator=(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+
+    const int _AP_F = _AP_W - _AP_I;
+    const int F2 = _AP_W2 - _AP_I2;
+    const int QUAN_INC =
+          F2 > _AP_F && !(_AP_Q == AP_TRN || (_AP_Q == AP_TRN_ZERO && !_AP_S2));
+
+    if (!op) Base::V = 0;
+    bool carry = false;
+    bool signbit = _AP_ROOT_op_get_bit(op.V, _AP_W2 - 1);
+    bool isneg = signbit && _AP_S2;
+    if (F2 == _AP_F)
+      Base::V = op.V;
+    else if (F2 > _AP_F) {
+      unsigned int sh_amt = F2 - _AP_F;
+      //  moves bits right, handle quantization.
+      if (sh_amt < _AP_W2) {
+        Base::V = op.V >> sh_amt;
+      } else {
+        Base::V = isneg ? -1 : 0;
+      }
+      if (_AP_Q != AP_TRN && !(_AP_Q == AP_TRN_ZERO && !_AP_S2)) {
+        bool qbit = _AP_ROOT_op_get_bit(op.V, F2 - _AP_F - 1);
+        // bit after LSB.
+        bool qb = (F2 - _AP_F > _AP_W2) ? _AP_S2 && signbit : qbit;
+        enum { hi = ((F2 - _AP_F - 2) < _AP_W2) ? (F2 - _AP_F - 2) : (_AP_W2 - 1) };
+        // bits after qb.
+        bool r = (F2 > _AP_F + 1) ? (_AP_ROOT_op_get_range(op.V, 0, hi) != 0) : false;
+        carry = quantization_adjust(qb, r, isneg);
+      }
+    } else {
+      unsigned  sh_amt = _AP_F - F2;
+      // moves bits left, no quantization
+      if (sh_amt < _AP_W) {
+        if (_AP_W > _AP_W2) {
+          // extend and then shift, avoid losing bits.
+          Base::V = op.V;
+          Base::V <<= sh_amt;
+        } else {
+          // shift and truncate.
+          Base::V = op.V << sh_amt;
+        }
+      } else {
+        Base::V = 0;
+      }
+    }
+    // handle overflow/underflow
+    if ((_AP_O != AP_WRAP || _AP_N != 0) &&
+        ((!_AP_S && _AP_S2) ||
+         _AP_I - _AP_S <
+             _AP_I2 - _AP_S2 +
+                 (QUAN_INC || (_AP_S2 && _AP_O == AP_SAT_SYM)))) { // saturation
+      bool deleted_zeros = _AP_S2 ? true : !carry;
+      bool deleted_ones = true;
+      bool neg_src = isneg;
+      bool newsignbit = _AP_ROOT_op_get_bit(Base::V, _AP_W - 1);
+      enum { pos1 = F2 - _AP_F + _AP_W, pos2 = F2 - _AP_F + _AP_W + 1 };
+      bool lD = (pos1 < _AP_W2 && pos1 >= 0) ? _AP_ROOT_op_get_bit(op.V, pos1)
+                                             : false;
+      if (pos1 < _AP_W2) {
+        bool Range1_all_ones = true;
+        bool Range1_all_zeros = true;
+        bool Range2_all_ones = true;
+        ap_int_base<_AP_W2, false> all_ones(-1);
+
+        if (pos2 < _AP_W2 && pos2 >= 0) {
+          ap_int_base<_AP_W2, false> Range2;
+          Range2.V = _AP_ROOT_op_get_range(op.V, pos2, _AP_W2 - 1);
+          Range2_all_ones = Range2 == (all_ones >> pos2);
+        } else if (pos2 < 0) {
+          Range2_all_ones = false;
+        }
+
+        if (pos1 >= 0 && pos2 < _AP_W2) {
+          ap_int_base<_AP_W2, false> Range1;
+          Range1.V = _AP_ROOT_op_get_range(op.V, pos1, _AP_W2 - 1);
+          Range1_all_ones = Range1 == (all_ones >> pos1);
+          Range1_all_zeros = !Range1.V;
+        } else if (pos2 == _AP_W2) {
+          Range1_all_ones = lD;
+          Range1_all_zeros = !lD;
+        } else if (pos1 < 0) {
+          Range1_all_zeros = !op.V;
+          Range1_all_ones = false;
+        }
+
+        deleted_zeros =
+            deleted_zeros && (carry ? Range1_all_ones : Range1_all_zeros);
+        deleted_ones =
+            carry ? Range2_all_ones && (pos1 < 0 || !lD) : Range1_all_ones;
+        neg_src = isneg && !(carry && Range1_all_ones);
+      } else
+        neg_src = isneg && newsignbit;
+      bool neg_trg = _AP_S && newsignbit;
+      bool overflow = (neg_trg || !deleted_zeros) && !isneg;
+      bool underflow = (!neg_trg || !deleted_ones) && neg_src;
+      if ((_AP_O == AP_SAT_SYM) && _AP_S2 && _AP_S)
+        underflow |=
+            neg_src &&
+            (_AP_W > 1 ? _AP_ROOT_op_get_range(Base::V, 0, _AP_W - 2) == 0
+                       : true);
+
+      overflow_adjust(underflow, overflow, lD, neg_src);
+    }
+    return *this;
+  } // operator= 
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base& operator=(
+      const volatile ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    operator=(const_cast<const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(op));
+    return *this;
+  }
+
+  /// Set this ap_fixed_base with ULL.
+  INLINE ap_fixed_base& setBits(ap_ulong bv) {
+    // TODO when ull is not be long enough...
+    Base::V = bv;
+    return *this;
+  }
+
+  /// Return a ap_fixed_base object whose this->V is assigned by bv.
+  static INLINE ap_fixed_base bitsToFixed(ap_ulong bv) {
+    // TODO fix when ull is not be long enough...
+    ap_fixed_base t;
+#ifdef __SYNTHESIS__
+    t.V = bv;
+#else
+    t.V.set_bits(bv);
+#endif
+    return t;
+  }
+
+  // Explicit conversion functions to ap_int_base.
+  /** Captures all integer bits, in truncate mode.
+   *  @param[in] Cnative follow conversion from double to int.
+   */
+  INLINE ap_int_base<AP_MAX(_AP_I, 1), _AP_S> to_ap_int_base(
+      bool Cnative = true) const {
+    ap_int_base<AP_MAX(_AP_I, 1), _AP_S> ret;
+    if (_AP_I == 0) {
+      ret.V = 0;
+    } else if (_AP_I > 0 && _AP_I <= _AP_W) {
+      ret.V = _AP_ROOT_op_get_range(Base::V, _AP_W - _AP_I, _AP_W - 1);
+    } else if (_AP_I > _AP_W) {
+      ret.V = _AP_ROOT_op_get_range(Base::V, 0, _AP_W - 1);
+      ret.V <<= (_AP_I - _AP_W);
+    }
+    /* Consider the following case
+     *   float f = -7.5f;
+     *   ap_fixed<8,4> t = f;  // -8 0 0 0 . 0.5
+     *   int i = t.to_int();
+     * the result should be -7 instead of -8.
+     * Therefore, after truncation, the value should be increated by 1.
+     * For (-1, 0), carry to MSB will happen, but result 0 is still correct.
+     */
+    if (Cnative && _AP_I < _AP_W) {
+      // Follow C native data type, conversion from double to int
+      if (_AP_S && _AP_ROOT_op_get_bit(Base::V, _AP_W - 1) && (_AP_I < _AP_W) &&
+          (_AP_ROOT_op_get_range(
+               Base::V, 0, _AP_I < 0 ? _AP_W - 1 : _AP_W - _AP_I - 1) != 0))
+        ++ret;
+    } else {
+      // Follow OSCI library, conversion from sc_fixed to sc_int
+    }
+    return ret;
+  };
+
+ public:
+  template <int _AP_W2, bool _AP_S2>
+  INLINE operator ap_int_base<_AP_W2, _AP_S2>() const {
+    return ap_int_base<_AP_W2, _AP_S2>(to_ap_int_base());
+  }
+
+  // Explicit conversion function to C built-in integral type.
+  INLINE char to_char() const { return to_ap_int_base().to_char(); }
+
+  INLINE int to_int() const { return to_ap_int_base().to_int(); }
+
+  INLINE unsigned to_uint() const { return to_ap_int_base().to_uint(); }
+
+  INLINE ap_slong to_int64() const { return to_ap_int_base().to_int64(); }
+
+  INLINE ap_ulong to_uint64() const { return to_ap_int_base().to_uint64(); }
+
+  /// covert function to double.
+  /** only round-half-to-even mode supported, does not obey FE env. */
+  INLINE double to_double() const {
+#if defined(AP_FIXED_ENABLE_CPP_FENV)
+    _AP_WARNING(std::fegetround() != FE_TONEAREST,
+                "Only FE_TONEAREST is supported");
+#endif
+    enum { BITS = DOUBLE_MAN + DOUBLE_EXP + 1 };
+    if (!Base::V) return 0.0f;
+    bool s = _AP_S && _AP_ROOT_op_get_bit(Base::V, _AP_W - 1); ///< sign.
+    ap_int_base<_AP_W, false> tmp;
+    if (s)
+      tmp.V = -Base::V; // may truncate one bit extra from neg in sim.
+    else
+      tmp.V = Base::V;
+    int l = tmp.countLeadingZeros(); ///< number of leading zeros.
+    int e = _AP_I - l - 1 + DOUBLE_BIAS; ///< exponent
+    int lsb_index = _AP_W - l - 1 - DOUBLE_MAN;
+    // more than 0.5?
+    bool a = (lsb_index >=2) ?
+        (_AP_ROOT_op_get_range(tmp.V, 0, lsb_index - 2) != 0) : 0;
+    // round to even
+    a |= (lsb_index >=0) ? _AP_ROOT_op_get_bit(tmp.V, lsb_index) : 0;
+    // ull is at least 64-bit
+    ap_ulong m;
+    // may actually left shift, ensure buffer is wide enough.
+    if (_AP_W > BITS) {
+      m = (lsb_index >= 1) ? (ap_ulong)(tmp.V >> (lsb_index - 1))
+                           : (ap_ulong)(tmp.V << (1 - lsb_index));
+    } else {
+      m = (ap_ulong)tmp.V;
+      m = (lsb_index >= 1) ? (m >> (lsb_index - 1))
+                           : (m << (1 - lsb_index));
+    }
+    m += a;
+    m >>= 1;
+    //std::cout << '\n' << std::hex << m << '\n'; // TODO delete this
+    // carry to MSB, increase exponent
+    if (_AP_ctype_op_get_bit(m, DOUBLE_MAN + 1)) {
+      e += 1;
+    }
+    // set sign and exponent
+    m = _AP_ctype_op_set_bit(m, BITS - 1, s);
+    //std::cout << m << '\n'; // TODO delete this
+    m = _AP_ctype_op_set_range(m, DOUBLE_MAN, DOUBLE_MAN + DOUBLE_EXP - 1, e);
+    //std::cout << std::hex << m << std::dec << std::endl; // TODO delete this
+    // cast to fp
+    return rawBitsToDouble(m);
+  }
+
+  /// convert function to float.
+  /** only round-half-to-even mode supported, does not obey FE env. */
+  INLINE float to_float() const {
+#if defined(AP_FIXED_ENABLE_CPP_FENV)
+    _AP_WARNING(std::fegetround() != FE_TONEAREST,
+                "Only FE_TONEAREST is supported");
+#endif
+    enum { BITS = FLOAT_MAN + FLOAT_EXP + 1 };
+    if (!Base::V) return 0.0f;
+    bool s = _AP_S && _AP_ROOT_op_get_bit(Base::V, _AP_W - 1); ///< sign.
+    ap_int_base<_AP_W, false> tmp;
+    if (s)
+      tmp.V = -Base::V; // may truncate one bit extra from neg in sim.
+    else
+      tmp.V = Base::V;
+    int l = tmp.countLeadingZeros();  ///< number of leading zeros.
+    int e = _AP_I - l - 1 + FLOAT_BIAS; ///< exponent
+    int lsb_index = _AP_W - l - 1 - FLOAT_MAN;
+    // more than 0.5?
+    bool a = (lsb_index >=2) ?
+        (_AP_ROOT_op_get_range(tmp.V, 0, lsb_index - 2) != 0) : 0;
+    // round to even
+    a |= (lsb_index >=0) ? _AP_ROOT_op_get_bit(tmp.V, lsb_index) : 0;
+    // ul is at least 32-bit
+    unsigned long m;
+    // may actually left shift, ensure buffer is wide enough.
+    if (_AP_W > BITS) {
+      m = (lsb_index >= 1) ? (unsigned long)(tmp.V >> (lsb_index - 1))
+                           : (unsigned long)(tmp.V << (1 - lsb_index));
+    } else {
+      m = (unsigned long)tmp.V;
+      m = (lsb_index >= 1) ? (m >> (lsb_index - 1))
+                           : (m << (1 - lsb_index));
+    }
+    m += a;
+    m >>= 1;
+    // carry to MSB, increase exponent
+    if (_AP_ctype_op_get_bit(m, FLOAT_MAN + 1)) {
+      e += 1;
+    }
+    // set sign and exponent
+    m = _AP_ctype_op_set_bit(m, BITS - 1, s);
+    m = _AP_ctype_op_set_range(m, FLOAT_MAN, FLOAT_MAN + FLOAT_EXP - 1, e);
+    // cast to fp
+    return rawBitsToFloat(m);
+  }
+
+#if _AP_ENABLE_HALF_ == 1
+  /// convert function to half.
+  /** only round-half-to-even mode supported, does not obey FE env. */
+  INLINE half to_half() const {
+#if defined(AP_FIXED_ENABLE_CPP_FENV)
+    _AP_WARNING(std::fegetround() != FE_TONEAREST,
+                "Only FE_TONEAREST is supported");
+#endif
+    enum { BITS = HALF_MAN + HALF_EXP + 1 };
+    if (!Base::V) return 0.0f;
+    bool s = _AP_S && _AP_ROOT_op_get_bit(Base::V, _AP_W - 1); ///< sign.
+    ap_int_base<_AP_W, false> tmp;
+    if (s)
+      tmp.V = -Base::V; // may truncate one bit extra from neg in sim.
+    else
+      tmp.V = Base::V;
+    int l = tmp.countLeadingZeros();  ///< number of leading zeros.
+    int e = _AP_I - l - 1 + HALF_BIAS; ///< exponent
+    int lsb_index = _AP_W - l - 1 - HALF_MAN;
+    // more than 0.5?
+    bool a = (lsb_index >=2) ?
+        (_AP_ROOT_op_get_range(tmp.V, 0, lsb_index - 2) != 0) : 0;
+    // round to even
+    a |= (lsb_index >=0) ? _AP_ROOT_op_get_bit(tmp.V, lsb_index) : 0;
+    // short is at least 16-bit
+    unsigned short m;
+    // may actually left shift, ensure buffer is wide enough.
+    if (_AP_W > BITS) {
+      m = (lsb_index >= 1) ? (unsigned short)(tmp.V >> (lsb_index - 1))
+                           : (unsigned short)(tmp.V << (1 - lsb_index));
+    } else {
+      m = (unsigned short)tmp.V;
+      m = (lsb_index >= 1) ? (m >> (lsb_index - 1))
+                           : (m << (1 - lsb_index));
+    }
+    m += a;
+    m >>= 1;
+    // carry to MSB, increase exponent
+    if (_AP_ctype_op_get_bit(m, HALF_MAN + 1)) {
+      e += 1;
+    }
+    // set sign and exponent
+    m = _AP_ctype_op_set_bit(m, BITS - 1, s);
+    m = _AP_ctype_op_set_range(m, HALF_MAN, HALF_MAN + HALF_EXP - 1, e);
+    // cast to fp
+    return rawBitsToHalf(m);
+  }
+#endif
+
+  // FIXME inherited from old code, this may loose precision!
+  INLINE operator long double() const { return (long double)to_double(); }
+
+  INLINE operator double() const { return to_double(); }
+
+  INLINE operator float() const { return to_float(); }
+
+#if _AP_ENABLE_HALF_ == 1
+  INLINE operator half() const { return to_half(); }
+#endif
+
+  INLINE operator bool() const { return (bool)Base::V != 0; }
+
+  INLINE operator char() const { return (char)to_int(); }
+
+  INLINE operator signed char() const { return (signed char)to_int(); }
+
+  INLINE operator unsigned char() const { return (unsigned char)to_uint(); }
+
+  INLINE operator short() const { return (short)to_int(); }
+
+  INLINE operator unsigned short() const { return (unsigned short)to_uint(); }
+
+  INLINE operator int() const { return to_int(); }
+
+  INLINE operator unsigned int() const { return to_uint(); }
+
+// FIXME don't assume data width...
+#ifdef __x86_64__
+  INLINE operator long() const { return (long)to_int64(); }
+
+  INLINE operator unsigned long() const { return (unsigned long)to_uint64(); }
+#else
+  INLINE operator long() const { return (long)to_int(); }
+
+  INLINE operator unsigned long() const { return (unsigned long)to_uint(); }
+#endif // ifdef __x86_64__ else
+
+  INLINE operator ap_ulong() const { return to_uint64(); }
+
+  INLINE operator ap_slong() const { return to_int64(); }
+
+  INLINE int length() const { return _AP_W; };
+
+  // bits_to_int64 deleted.
+#ifndef __SYNTHESIS__
+  // Used in autowrap, when _AP_W < 64.
+  INLINE ap_ulong bits_to_uint64() const {
+    return (Base::V).to_uint64();
+  }
+#endif
+
+  // Count the number of zeros from the most significant bit
+  // to the first one bit. Note this is only for ap_fixed_base whose
+  // _AP_W <= 64, otherwise will incur assertion.
+  INLINE int countLeadingZeros() {
+#ifdef __SYNTHESIS__
+    // TODO: used llvm.ctlz intrinsic ?
+    if (_AP_W <= 32) {
+      ap_int_base<32, false> t(-1ULL);
+      t.range(_AP_W - 1, 0) = this->range(0, _AP_W - 1);
+      return __builtin_ctz(t.V);
+    } else if (_AP_W <= 64) {
+      ap_int_base<64, false> t(-1ULL);
+      t.range(_AP_W - 1, 0) = this->range(0, _AP_W - 1);
+      return __builtin_ctzll(t.V);
+    } else {
+      enum {__N = (_AP_W + 63) / 64};
+      int NZeros = 0;
+      int i = 0;
+      bool hitNonZero = false;
+      for (i = 0; i < __N - 1; ++i) {
+        ap_int_base<64, false> t;
+        t.range(0, 63) = this->range(_AP_W - i * 64 - 64, _AP_W - i * 64 - 1);
+        NZeros += hitNonZero ? 0 : __builtin_clzll(t.V);
+        hitNonZero |= (t != 0);
+      }
+      if (!hitNonZero) {
+        ap_int_base<64, false> t(-1ULL);
+        t.range(63 - (_AP_W - 1) % 64, 63) = this->range(0, (_AP_W - 1) % 64);
+        NZeros += __builtin_clzll(t.V);
+      }
+      return NZeros;
+    }
+#else
+    return Base::V.countLeadingZeros();
+#endif
+  }
+
+  // Arithmetic : Binary
+  // -------------------------------------------------------------------------
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE typename RType<_AP_W2, _AP_I2, _AP_S2>::mult operator*(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op2)
+      const {
+    typename RType<_AP_W2, _AP_I2, _AP_S2>::mult_base r, t;
+    r.V = Base::V;
+    t.V = op2.V;
+    r.V *= op2.V;
+    return r;
+  }
+
+  // multiply function deleted.
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE typename RType<_AP_W2, _AP_I2, _AP_S2>::div operator/(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op2)
+      const {
+    typename RType<_AP_W2, _AP_I2, _AP_S2>::div_base r;
+#ifndef __SYNTHESIS__
+    enum {F2 = _AP_W2-_AP_I2,
+              _W1=AP_MAX(_AP_W + AP_MAX(F2, 0) + ((_AP_S2 && !_AP_S) ? 1 : 0), _AP_W2 + ((_AP_S && !_AP_S2) ? 1 : 0))};
+    ap_int_base<_W1,_AP_S||_AP_S2> dividend,divisior;
+    ap_int_base<_W1,_AP_S> tmp1;
+    ap_int_base<_W1,_AP_S2> tmp2;
+    tmp1.V = Base::V;
+    tmp1.V <<= AP_MAX(F2,0);
+    tmp2.V = op2.V;
+    dividend = tmp1;
+    divisior = tmp2;
+    r.V = ((_AP_S||_AP_S2) ? dividend.V.sdiv(divisior.V): dividend.V.udiv(divisior.V));
+#else
+    #ifndef __SC_COMPATIBLE__
+        ap_fixed_base<_AP_W + AP_MAX(_AP_W2 - _AP_I2, 0),_AP_I, _AP_S> t(*this);
+    #else
+        ap_fixed_base<_AP_W + AP_MAX(_AP_W2 - _AP_I2, 0) + AP_MAX(_AP_I2, 0),_AP_I, _AP_S> t(*this);
+    #endif
+        r.V = t.V / op2.V;
+#endif
+/*
+    enum {
+      F2 = _AP_W2 - _AP_I2,
+      shl = AP_MAX(F2, 0) + AP_MAX(_AP_I2, 0),
+#ifndef __SC_COMPATIBLE__
+      shr = AP_MAX(_AP_I2, 0),
+#else
+      shr = 0,
+#endif
+      W3 = _AP_S2 + _AP_W + shl,
+      S3 = _AP_S || _AP_S2,
+    };
+    ap_int_base<W3, S3> dividend, t;
+    dividend.V = Base::V;
+    // multiply both by (1 << F2), and than do integer division.
+    dividend.V <<= (int) shl;
+#ifdef __SYNTHESIS__
+    // .V's have right signedness, and will have right extending.
+    t.V = dividend.V / op2.V;
+#else
+    // XXX op2 may be wider than dividend, and sdiv and udiv takes the same with
+    // as left hand operand, so data might be truncated by mistake if not
+    // handled here.
+    t.V = S3 ? dividend.V.sdiv(op2.V) : dividend.V.udiv(op2.V);
+#endif
+    r.V = t.V >> (int) shr;
+*/
+    return r;
+  }
+
+#define OP_BIN_AF(Sym, Rty)                                                \
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,         \
+            ap_o_mode _AP_O2, int _AP_N2>                                  \
+  INLINE typename RType<_AP_W2, _AP_I2, _AP_S2>::Rty operator Sym(         \
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& \
+          op2) const {                                                     \
+    typename RType<_AP_W2, _AP_I2, _AP_S2>::Rty##_base ret, lhs(*this),    \
+        rhs(op2);                                                          \
+    ret.V = lhs.V Sym rhs.V;                                               \
+    return ret;                                                            \
+  }
+
+  OP_BIN_AF(+, plus)
+  OP_BIN_AF(-, minus)
+  OP_BIN_AF(&, logic)
+  OP_BIN_AF(|, logic)
+  OP_BIN_AF(^, logic)
+
+// Arithmetic : assign
+// -------------------------------------------------------------------------
+#define OP_ASSIGN_AF(Sym)                                                  \
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,         \
+            ap_o_mode _AP_O2, int _AP_N2>                                  \
+  INLINE ap_fixed_base& operator Sym##=(                                   \
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& \
+          op2) {                                                           \
+    *this = operator Sym(op2);                                             \
+    return *this;                                                          \
+  }
+
+  OP_ASSIGN_AF(*)
+  OP_ASSIGN_AF(/)
+  OP_ASSIGN_AF(+)
+  OP_ASSIGN_AF(-)
+  OP_ASSIGN_AF(&)
+  OP_ASSIGN_AF(|)
+  OP_ASSIGN_AF(^)
+
+  // Prefix and postfix increment and decrement.
+  // -------------------------------------------------------------------------
+
+  /// Prefix increment
+  INLINE ap_fixed_base& operator++() {
+    operator+=(ap_fixed_base<_AP_W - _AP_I + 1, 1, false>(1));
+    return *this;
+  }
+
+  /// Prefix decrement.
+  INLINE ap_fixed_base& operator--() {
+    operator-=(ap_fixed_base<_AP_W - _AP_I + 1, 1, false>(1));
+    return *this;
+  }
+
+  /// Postfix increment
+  INLINE const ap_fixed_base operator++(int) {
+    ap_fixed_base r(*this);
+    operator++();
+    return r;
+  }
+
+  /// Postfix decrement
+  INLINE const ap_fixed_base operator--(int) {
+    ap_fixed_base r(*this);
+    operator--();
+    return r;
+  }
+
+  // Unary arithmetic.
+  // -------------------------------------------------------------------------
+  INLINE ap_fixed_base operator+() { return *this; }
+
+  INLINE ap_fixed_base<_AP_W + 1, _AP_I + 1, true> operator-() const {
+    ap_fixed_base<_AP_W + 1, _AP_I + 1, true> r(*this);
+    r.V = -r.V;
+    return r;
+  }
+
+  INLINE ap_fixed_base<_AP_W, _AP_I, true, _AP_Q, _AP_O, _AP_N> getNeg() {
+    ap_fixed_base<_AP_W, _AP_I, true, _AP_Q, _AP_O, _AP_N> r(*this);
+    r.V = -r.V;
+    return r;
+  }
+
+  // Not (!)
+  // -------------------------------------------------------------------------
+  INLINE bool operator!() const { return Base::V == 0; }
+
+  // Bitwise complement
+  // -------------------------------------------------------------------------
+  // XXX different from Mentor's ac_fixed.
+  INLINE ap_fixed_base<_AP_W, _AP_I, _AP_S> operator~() const {
+    ap_fixed_base<_AP_W, _AP_I, _AP_S> r;
+    r.V = ~Base::V;
+    return r;
+  }
+
+  // Shift
+  // -------------------------------------------------------------------------
+  // left shift is the same as moving point right, i.e. increate I.
+  template <int _AP_SHIFT>
+  INLINE ap_fixed_base<_AP_W, _AP_I + _AP_SHIFT, _AP_S> lshift() const {
+    ap_fixed_base<_AP_W, _AP_I + _AP_SHIFT, _AP_S> r;
+    r.V = Base::V;
+    return r;
+  }
+
+  template <int _AP_SHIFT>
+  INLINE ap_fixed_base<_AP_W, _AP_I - _AP_SHIFT, _AP_S> rshift() const {
+    ap_fixed_base<_AP_W, _AP_I - _AP_SHIFT, _AP_S> r;
+    r.V = Base::V;
+    return r;
+  }
+
+  // Because the return type is the type of the the first operand, shift assign
+  // operators do not carry out any quantization or overflow
+  // While systemc, shift assigns for sc_fixed/sc_ufixed will result in
+  // quantization or overflow (depending on the mode of the first operand)
+  INLINE ap_fixed_base operator<<(unsigned int sh) const {
+    ap_fixed_base r;
+    r.V = Base::V << sh;
+// TODO check shift overflow?
+#ifdef __SC_COMPATIBLE__
+    if (sh == 0) return r;
+    if (_AP_O != AP_WRAP || _AP_N != 0) {
+      bool neg_src = _AP_S && _AP_ROOT_op_get_bit(Base::V, _AP_W - 1);
+      bool allones, allzeros;
+      ap_int_base<_AP_W, false> ones(-1);
+      if (sh <= _AP_W) {
+        ap_int_base<_AP_W, false> range1;
+        range1.V = _AP_ROOT_op_get_range(
+            const_cast<ap_fixed_base*>(this)->Base::V, _AP_W - sh, _AP_W - 1);
+        allones = range1 == (ones >> (_AP_W - sh));
+        allzeros = range1 == 0;
+      } else {
+        allones = false;
+        allzeros = Base::V == 0;
+      }
+      bool overflow = !allzeros && !neg_src;
+      bool underflow = !allones && neg_src;
+      if ((_AP_O == AP_SAT_SYM) && _AP_S)
+        underflow |=
+            neg_src &&
+            (_AP_W > 1 ? _AP_ROOT_op_get_range(r.V, 0, _AP_W - 2) == 0 : true);
+      bool lD = false;
+      if (sh < _AP_W) lD = _AP_ROOT_op_get_bit(Base::V, _AP_W - sh - 1);
+      r.overflow_adjust(underflow, overflow, lD, neg_src);
+    }
+#endif
+    return r;
+  }
+
+  INLINE ap_fixed_base operator>>(unsigned int sh) const {
+    ap_fixed_base r;
+    r.V = Base::V >> sh;
+// TODO check shift overflow?
+#ifdef __SC_COMPATIBLE__
+    if (sh == 0) return r;
+    if (_AP_Q != AP_TRN) {
+      bool qb = false;
+      if (sh <= _AP_W) qb = _AP_ROOT_op_get_bit(Base::V, sh - 1);
+      bool rb = false;
+      if (sh > 1 && sh <= _AP_W)
+        rb = _AP_ROOT_op_get_range(const_cast<ap_fixed_base*>(this)->Base::V, 0,
+                                   sh - 2) != 0;
+      else if (sh > _AP_W)
+        rb = Base::V != 0;
+      r.quantization_adjust(qb, rb,
+                            _AP_S && _AP_ROOT_op_get_bit(Base::V, _AP_W - 1));
+    }
+#endif
+    return r;
+  }
+
+  // left and right shift for int
+  INLINE ap_fixed_base operator<<(int sh) const {
+    ap_fixed_base r;
+    bool isNeg = sh < 0;
+    unsigned int ush = isNeg ? -sh : sh;
+    if (isNeg) {
+      return operator>>(ush);
+    } else {
+      return operator<<(ush);
+    }
+  }
+
+  INLINE ap_fixed_base operator>>(int sh) const {
+    bool isNeg = sh < 0;
+    unsigned int ush = isNeg ? -sh : sh;
+    if (isNeg) {
+      return operator<<(ush);
+    } else {
+      return operator>>(ush);
+    }
+  }
+
+  // left and right shift for ap_int.
+  template <int _AP_W2>
+  INLINE ap_fixed_base operator<<(const ap_int_base<_AP_W2, true>& op2) const {
+    // TODO the code seems not optimal. ap_fixed<8,8> << ap_int<2> needs only a
+    // small mux, but integer need a big one!
+    int sh = op2.to_int();
+    return operator<<(sh);
+  }
+
+  template <int _AP_W2>
+  INLINE ap_fixed_base operator>>(const ap_int_base<_AP_W2, true>& op2) const {
+    int sh = op2.to_int();
+    return operator>>(sh);
+  }
+
+  // left and right shift for ap_uint.
+  template <int _AP_W2>
+  INLINE ap_fixed_base operator<<(const ap_int_base<_AP_W2, false>& op2) const {
+    unsigned int sh = op2.to_uint();
+    return operator<<(sh);
+  }
+
+  template <int _AP_W2>
+  INLINE ap_fixed_base operator>>(const ap_int_base<_AP_W2, false>& op2) const {
+    unsigned int sh = op2.to_uint();
+    return operator>>(sh);
+  }
+
+  // left and right shift for ap_fixed
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base operator<<(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&
+          op2) {
+    return operator<<(op2.to_ap_int_base());
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base operator>>(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&
+          op2) {
+    return operator>>(op2.to_ap_int_base());
+  }
+
+  // Shift assign.
+  // -------------------------------------------------------------------------
+
+  // left shift assign.
+  INLINE ap_fixed_base& operator<<=(const int sh) {
+    *this = operator<<(sh);
+    return *this;
+  }
+
+  INLINE ap_fixed_base& operator<<=(const unsigned int sh) {
+    *this = operator<<(sh);
+    return *this;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed_base& operator<<=(const ap_int_base<_AP_W2, _AP_S2>& sh) {
+    *this = operator<<(sh.to_int());
+    return *this;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base& operator<<=(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&
+          sh) {
+    *this = operator<<(sh.to_int());
+    return *this;
+  }
+
+  // right shift assign.
+  INLINE ap_fixed_base& operator>>=(const int sh) {
+    *this = operator>>(sh);
+    return *this;
+  }
+
+  INLINE ap_fixed_base& operator>>=(const unsigned int sh) {
+    *this = operator>>(sh);
+    return *this;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_fixed_base& operator>>=(const ap_int_base<_AP_W2, _AP_S2>& sh) {
+    *this = operator>>(sh.to_int());
+    return *this;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_fixed_base& operator>>=(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&
+          sh) {
+    *this = operator>>(sh.to_int());
+    return *this;
+  }
+
+// Comparisons.
+// -------------------------------------------------------------------------
+#define OP_CMP_AF(Sym)                                                         \
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,             \
+            ap_o_mode _AP_O2, int _AP_N2>                                      \
+  INLINE bool operator Sym(const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, \
+                                               _AP_O2, _AP_N2>& op2) const {   \
+    enum { _AP_F = _AP_W - _AP_I, F2 = _AP_W2 - _AP_I2 };                      \
+    if (_AP_F == F2)                                                           \
+      return Base::V Sym op2.V;                                                \
+    else if (_AP_F > F2)                                                       \
+      return Base::V Sym ap_fixed_base<AP_MAX(_AP_W2 + _AP_F - F2, 1), _AP_I2, \
+                                       _AP_S2, _AP_Q2, _AP_O2, _AP_N2>(op2).V; \
+    else                                                                       \
+      return ap_fixed_base<AP_MAX(_AP_W + F2 - _AP_F + 1, 1), _AP_I + 1,       \
+                           _AP_S, _AP_Q, _AP_O, _AP_N>(*this).V Sym op2.V;     \
+    return false;                                                              \
+  }
+
+  OP_CMP_AF(>)
+  OP_CMP_AF(<)
+  OP_CMP_AF(>=)
+  OP_CMP_AF(<=)
+  OP_CMP_AF(==)
+  OP_CMP_AF(!=)
+// FIXME: Move compare with double out of struct ap_fixed_base defination
+//        and combine it with compare operator(double, ap_fixed_base)
+#define DOUBLE_CMP_AF(Sym) \
+  INLINE bool operator Sym(double d) const { return to_double() Sym d; }
+
+  DOUBLE_CMP_AF(>)
+  DOUBLE_CMP_AF(<)
+  DOUBLE_CMP_AF(>=)
+  DOUBLE_CMP_AF(<=)
+  DOUBLE_CMP_AF(==)
+  DOUBLE_CMP_AF(!=)
+
+  // Bit and Slice Select
+  INLINE af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> operator[](
+      unsigned index) {
+    _AP_WARNING(index >= _AP_W, "Attempting to read bit beyond MSB");
+    return af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>(this, index);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> operator[](
+      const ap_int_base<_AP_W2, _AP_S2>& index) {
+    _AP_WARNING(index < 0, "Attempting to read bit with negative index");
+    _AP_WARNING(index >= _AP_W, "Attempting to read bit beyond MSB");
+    return af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>(this,
+                                                                index.to_int());
+  }
+
+  INLINE bool operator[](unsigned index) const {
+    _AP_WARNING(index >= _AP_W, "Attempting to read bit beyond MSB");
+    return _AP_ROOT_op_get_bit(const_cast<ap_fixed_base*>(this)->V, index);
+  }
+
+  INLINE af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> bit(
+      unsigned index) {
+    _AP_WARNING(index >= _AP_W, "Attempting to read bit beyond MSB");
+    return af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>(this, index);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> bit(
+      const ap_int_base<_AP_W2, _AP_S2>& index) {
+    _AP_WARNING(index < 0, "Attempting to read bit with negative index");
+    _AP_WARNING(index >= _AP_W, "Attempting to read bit beyond MSB");
+    return af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>(this,
+                                                                index.to_int());
+  }
+
+  INLINE bool bit(unsigned index) const {
+    _AP_WARNING(index >= _AP_W, "Attempting to read bit beyond MSB");
+    return _AP_ROOT_op_get_bit(const_cast<ap_fixed_base*>(this)->V, index);
+  }
+
+  template <int _AP_W2>
+  INLINE af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> get_bit(
+      const ap_int_base<_AP_W2, true>& index) {
+    _AP_WARNING(index < _AP_I - _AP_W,
+                "Attempting to read bit with negative index");
+    _AP_WARNING(index >= _AP_I, "Attempting to read bit beyond MSB");
+    return af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>(
+        this, index.to_int() + _AP_W - _AP_I);
+  }
+
+  INLINE bool get_bit(int index) const {
+    _AP_WARNING(index >= _AP_I, "Attempting to read bit beyond MSB");
+    _AP_WARNING(index < _AP_I - _AP_W, "Attempting to read bit beyond MSB");
+    return _AP_ROOT_op_get_bit(const_cast<ap_fixed_base*>(this)->V,
+                               index + _AP_W - _AP_I);
+  }
+#if 0
+  INLINE af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> get_bit(
+      int index) {
+    _AP_WARNING(index < _AP_I - _AP_W,
+              "Attempting to read bit with negative index");
+    _AP_WARNING(index >= _AP_I, "Attempting to read bit beyond MSB");
+    return af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>(
+        this, index + _AP_W - _AP_I);
+  }
+#endif
+
+  template <int _AP_W2>
+  INLINE bool get_bit(const ap_int_base<_AP_W2, true>& index) const {
+    _AP_WARNING(index >= _AP_I, "Attempting to read bit beyond MSB");
+    _AP_WARNING(index < _AP_I - _AP_W, "Attempting to read bit beyond MSB");
+    return _AP_ROOT_op_get_bit(const_cast<ap_fixed_base*>(this)->V,
+                               index.to_int() + _AP_W - _AP_I);
+  }
+
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> range(int Hi,
+                                                                      int Lo) {
+    _AP_WARNING((Hi >= _AP_W) || (Lo >= _AP_W), "Out of bounds in range()");
+    return af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>(this, Hi, Lo);
+  }
+
+  // This is a must to strip constness to produce reference type.
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> range(
+      int Hi, int Lo) const {
+    _AP_WARNING((Hi >= _AP_W) || (Lo >= _AP_W), "Out of bounds in range()");
+    return af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>(
+        const_cast<ap_fixed_base*>(this), Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> range(
+      const ap_int_base<_AP_W2, _AP_S2>& HiIdx,
+      const ap_int_base<_AP_W3, _AP_S3>& LoIdx) {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return this->range(Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> range(
+      const ap_int_base<_AP_W2, _AP_S2>& HiIdx,
+      const ap_int_base<_AP_W3, _AP_S3>& LoIdx) const {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return this->range(Hi, Lo);
+  }
+
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> range() {
+    return this->range(_AP_W - 1, 0);
+  }
+
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> range() const {
+    return this->range(_AP_W - 1, 0);
+  }
+
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> operator()(
+      int Hi, int Lo) {
+    return this->range(Hi, Lo);
+  }
+
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> operator()(
+      int Hi, int Lo) const {
+    return this->range(Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> operator()(
+      const ap_int_base<_AP_W2, _AP_S2>& HiIdx,
+      const ap_int_base<_AP_W3, _AP_S3>& LoIdx) {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return this->range(Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> operator()(
+      const ap_int_base<_AP_W2, _AP_S2>& HiIdx,
+      const ap_int_base<_AP_W3, _AP_S3>& LoIdx) const {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return this->range(Hi, Lo);
+  }
+
+  INLINE bool is_zero() const { return Base::V == 0; }
+
+  INLINE bool is_neg() const {
+    if (_AP_S && _AP_ROOT_op_get_bit(Base::V, _AP_W - 1)) return true;
+    return false;
+  }
+
+  INLINE int wl() const { return _AP_W; }
+
+  INLINE int iwl() const { return _AP_I; }
+
+  INLINE ap_q_mode q_mode() const { return _AP_Q; }
+
+  INLINE ap_o_mode o_mode() const { return _AP_O; }
+
+  INLINE int n_bits() const { return _AP_N; }
+
+  // print a string representation of this number in the given radix.
+  // Radix support is 2, 8, 10, or 16.
+  // The result will include a prefix indicating the radix, except for decimal,
+  // where no prefix is needed.  The default is to output a signed representation
+  // of signed numbers, or an unsigned representation  of unsigned numbers.  For
+  // non-decimal formats, this can be changed by the 'sign' argument.
+#ifndef __SYNTHESIS__
+  std::string to_string(unsigned char radix = 2, bool sign = _AP_S) const {
+    // XXX in autosim/autowrap.tcl "(${name}).to_string(2).c_str()" is used to
+    // initialize sc_lv, which seems incapable of handling format "-0b".
+    if (radix == 2) sign = false;
+
+    std::string str;
+    str.clear();
+    char step = 0;
+    bool isNeg = sign && (Base::V < 0);
+
+    // Extend to take care of the -MAX case.
+    ap_fixed_base<_AP_W + 1, _AP_I + 1> tmp(*this);
+    if (isNeg) {
+      tmp = -tmp;
+      str += '-';
+    }
+    std::string prefix;
+    switch (radix) {
+      case 2:
+        prefix = "0b";
+        step = 1;
+        break;
+      case 8:
+        prefix = "0o";
+        step = 3;
+        break;
+      case 16:
+        prefix = "0x";
+        step = 4;
+        break;
+      default:
+        break;
+    }
+
+    if (_AP_I > 0) {
+      // Note we drop the quantization and rounding flags here.  The
+      // integer part is always in range, and the fractional part we
+      // want to drop.  Also, the number is always positive, because
+      // of the absolute value above.
+      ap_int_base<AP_MAX(_AP_I + 1, 1), false> int_part;
+      //   [1] [ I ] d [ W - I ]
+      //    |     |            |
+      //    |    W-I           0
+      //    W
+      int_part.V = _AP_ROOT_op_get_range(
+          tmp.V, _AP_W - _AP_I, _AP_W);
+      str += int_part.to_string(radix, false);
+    } else {
+      str += prefix;
+      str += '0';
+    }
+
+    ap_fixed_base<AP_MAX(_AP_W - _AP_I, 1), 0, false> frac_part = tmp;
+
+    if (radix == 10) {
+      if (frac_part != 0) {
+        str += ".";
+        while (frac_part != 0) {
+          char digit = (frac_part * radix).to_char();
+          str += static_cast<char>(digit + '0');
+          frac_part *= radix;
+        }
+      }
+    } else {
+      if (frac_part != 0) {
+        str += ".";
+        for (signed i = _AP_W - _AP_I - 1; i >= 0; i -= step) {
+          char digit = frac_part.range(i, AP_MAX(0, i - step + 1)).to_char();
+          // If we have a partial bit pattern at the end, then we need
+          // to put it in the high-order bits of 'digit'.
+          int offset = AP_MIN(0, i - step + 1);
+          digit <<= -offset;
+          str += digit < 10 ? static_cast<char>(digit + '0')
+                            : static_cast<char>(digit - 10 + 'a');
+        }
+        if (radix == 16)
+          str += "p0"; // C99 Hex constants are required to have an exponent.
+      }
+    }
+    return str;
+  }
+#else
+  // XXX HLS will delete this in synthesis
+  INLINE char* to_string(unsigned char radix = 2, bool sign = _AP_S) const {
+    return 0;
+  }
+#endif
+}; // struct ap_fixed_base.
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE void b_not(
+    ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& ret,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) {
+  ret.V = ~op.V;
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE void b_and(
+    ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& ret,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op1,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op2) {
+  ret.V = op1.V & op2.V;
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE void b_or(
+    ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& ret,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op1,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op2) {
+  ret.V = op1.V | op2.V;
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE void b_xor(
+    ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& ret,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op1,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op2) {
+  ret.V = op1.V ^ op2.V;
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N, int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+          ap_o_mode _AP_O2, int _AP_N2>
+INLINE void neg(
+    ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& ret,
+    const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+  ap_fixed_base<_AP_W2 + !_AP_S2, _AP_I2 + !_AP_S2, true, _AP_Q2, _AP_O2,
+                _AP_N2>
+      t;
+  t.V = -op.V;
+  ret = t;
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N, int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+          ap_o_mode _AP_O2, int _AP_N2>
+INLINE void lshift(
+    ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& ret,
+    const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op,
+    int i) {
+  enum {
+    F2 = _AP_W2 - _AP_I2,
+    _AP_I3 = AP_MAX(_AP_I, _AP_I2),
+    _AP_W3 = _AP_I3 + F2,
+  };
+  // wide buffer
+  ap_fixed_base<_AP_W3, _AP_I3, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> t;
+  t.V = op.V;
+  t.V <<= i; // FIXME overflow?
+  // handle quantization and overflow
+  ret = t;
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N, int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+          ap_o_mode _AP_O2, int _AP_N2>
+INLINE void rshift(
+    ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& ret,
+    const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op,
+    int i) {
+  enum {
+    F = _AP_W - _AP_I,
+    F2 = _AP_W2 - _AP_I2,
+    F3 = AP_MAX(F, F2),
+    _AP_W3 = _AP_I2 + F3,
+    sh = F - F2,
+  };
+  // wide buffer
+  ap_fixed_base<_AP_W3, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> t;
+  t.V = op.V;
+  if (sh >= 0)
+    t.V <<= (int) sh;
+  t.V >>= i;
+  // handle quantization and overflow
+  ret = t;
+}
+
+//// FIXME
+//// These partial specialization ctors allow code like
+////   char c = 'a';
+////   ap_fixed_base<8, 8, true> x(c);
+//// but what bout ap_fixed_base<9, 9, true> y(c) ?
+//
+
+#ifndef __SYNTHESIS__
+INLINE std::string scientificFormat(std::string& input) {
+  if (input.length() == 0) return input;
+
+  size_t decPosition = input.find('.');
+  if (decPosition == std::string::npos) decPosition = input.length();
+
+  size_t firstNonZeroPos = 0;
+  for (; input[firstNonZeroPos] > '9' || input[firstNonZeroPos] < '1';
+       firstNonZeroPos++)
+    ;
+
+  int exp;
+  if (firstNonZeroPos > decPosition)
+    exp = decPosition - firstNonZeroPos;
+  else
+    exp = decPosition - firstNonZeroPos - 1;
+  std::string expString = "";
+  if (exp == 0)
+    ;
+  else if (exp < 0) {
+    expString += "e-";
+    exp = -exp;
+  } else
+    expString += "e+";
+
+  if (exp < 10 && exp > 0) {
+    expString += '0';
+    expString += (char)('0' + exp);
+  } else if (exp != 0) {
+    std::string tmp;
+
+    std::ostringstream oss;
+    oss << exp;
+
+    tmp = oss.str();
+    expString += tmp;
+  }
+
+  int lastNonZeroPos = (int)(input.length() - 1);
+  for (; lastNonZeroPos >= 0; --lastNonZeroPos)
+    if (input[lastNonZeroPos] <= '9' && input[lastNonZeroPos] > '0') break;
+
+  std::string ans = "";
+  ans += input[firstNonZeroPos];
+  if (firstNonZeroPos != (size_t)lastNonZeroPos) {
+    ans += '.';
+    for (int i = firstNonZeroPos + 1; i <= lastNonZeroPos; i++)
+      if (input[i] != '.') ans += input[i];
+  }
+
+  ans += expString;
+  return ans;
+}
+
+INLINE std::string reduceToPrecision(std::string& input, int precision) {
+  bool isZero = true;
+  size_t inputLen = input.length();
+  for (size_t i = 0; i < inputLen && isZero; i++)
+    if (input[i] != '.' && input[i] != '0') isZero = false;
+  if (isZero) return "0";
+
+  // Find the first valid number, skip '-'
+  int FirstNonZeroPos = 0;
+  int LastNonZeroPos = (int)inputLen - 1;
+  int truncBitPosition = 0;
+  size_t decPosition = input.find('.');
+  for (; input[FirstNonZeroPos] < '1' || input[FirstNonZeroPos] > '9';
+       FirstNonZeroPos++)
+    ;
+
+  for (; input[LastNonZeroPos] < '1' || input[LastNonZeroPos] > '9';
+       LastNonZeroPos--)
+    ;
+
+  if (decPosition == std::string::npos) decPosition = inputLen;
+  // Count the valid number, to decide whether we need to truncate
+  if ((int)decPosition > LastNonZeroPos) {
+    if (LastNonZeroPos - FirstNonZeroPos + 1 <= precision) return input;
+    truncBitPosition = FirstNonZeroPos + precision;
+  } else if ((int)decPosition < FirstNonZeroPos) { // This is pure decimal
+    if (LastNonZeroPos - FirstNonZeroPos + 1 <= precision) {
+      if (FirstNonZeroPos - decPosition - 1 < 4) {
+        return input;
+      } else {
+        if (input[0] == '-') {
+          std::string tmp = input.substr(1, inputLen - 1);
+          return std::string("-") + scientificFormat(tmp);
+        } else
+          return scientificFormat(input);
+      }
+    }
+    truncBitPosition = FirstNonZeroPos + precision;
+  } else {
+    if (LastNonZeroPos - FirstNonZeroPos <= precision) return input;
+    truncBitPosition = FirstNonZeroPos + precision + 1;
+  }
+
+  // duplicate the input string, we want to add "0" before the valid numbers
+  // This is easy for quantization, since we may change 9999 to 10000
+  std::string ans = "";
+  std::string dupInput = "0";
+  if (input[0] == '-') {
+    ans += '-';
+    dupInput += input.substr(1, inputLen - 1);
+  } else {
+    dupInput += input.substr(0, inputLen);
+    ++truncBitPosition;
+  }
+
+  // Add 'carry' after truncation, if necessary
+  bool carry = dupInput[truncBitPosition] > '4';
+  for (int i = truncBitPosition - 1; i >= 0 && carry; i--) {
+    if (dupInput[i] == '.') continue;
+    if (dupInput[i] == '9')
+      dupInput[i] = '0';
+    else {
+      ++dupInput[i];
+      carry = false;
+    }
+  }
+
+  // bits outside precision range should be set to 0
+  if (dupInput[0] == '1')
+    FirstNonZeroPos = 0;
+  else {
+    FirstNonZeroPos = 0;
+    while (dupInput[FirstNonZeroPos] < '1' || dupInput[FirstNonZeroPos] > '9')
+      ++FirstNonZeroPos;
+  }
+
+  unsigned it = FirstNonZeroPos;
+  int NValidNumber = 0;
+  while (it < dupInput.length()) {
+    if (dupInput[it] == '.') {
+      ++it;
+      continue;
+    }
+    ++NValidNumber;
+    if (NValidNumber > precision) dupInput[it] = '0';
+    ++it;
+  }
+
+  // Here we wanted to adjust the truncate position and the value
+  decPosition = dupInput.find('.');
+  if (decPosition == std::string::npos) // When this is integer
+    truncBitPosition = (int)dupInput.length();
+  else
+    for (truncBitPosition = (int)(dupInput.length() - 1); truncBitPosition >= 0;
+         --truncBitPosition) {
+      if (dupInput[truncBitPosition] == '.') break;
+      if (dupInput[truncBitPosition] != '0') {
+        truncBitPosition++;
+        break;
+      }
+    }
+
+  if (dupInput[0] == '1')
+    dupInput = dupInput.substr(0, truncBitPosition);
+  else
+    dupInput = dupInput.substr(1, truncBitPosition - 1);
+
+  decPosition = dupInput.find('.');
+  if (decPosition != std::string::npos) {
+    size_t it = 0;
+    for (it = decPosition + 1; dupInput[it] == '0'; it++)
+      ;
+    if (it - decPosition - 1 < 4) {
+      ans += dupInput;
+      return ans;
+    } else {
+      ans += scientificFormat(dupInput);
+      return ans;
+    }
+  } else if ((int)(dupInput.length()) <= precision) {
+    ans += dupInput;
+    return ans;
+  }
+
+  ans += scientificFormat(dupInput);
+  return ans;
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE void print(
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& x) {
+  if (_AP_I > 0) {
+    ap_int_base<_AP_I, _AP_S> p1;
+    p1.V = x.V >> (_AP_W - _AP_I);
+    print(p1.V); // print overlaod for .V should exit
+  } else {
+    printf("0");
+  }
+  printf(".");
+  if (_AP_I < _AP_W) {
+    ap_int_base<_AP_W - _AP_I, false> p2;
+    p2.V = _AP_ROOT_op_get_range(x.V, 0, _AP_W - _AP_I);
+    print(p2.V, false); // print overlaod for .V should exit
+  }
+}
+#endif // ifndef __SYNTHESIS__
+
+// XXX the following two functions have to exist in synthesis,
+// as some old HLS Video Library code uses the ostream overload,
+// although HLS will later delete I/O function call.
+
+/// Output streaming
+//-----------------------------------------------------------------------------
+// XXX apcc cannot handle global std::ios_base::Init() brought in by <iostream>
+#ifndef AP_AUTOCC
+#ifndef __SYNTHESIS__
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE std::ostream& operator<<(
+    std::ostream& out,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& x) {
+  // TODO support std::ios_base::fmtflags
+  unsigned width = out.width();
+  unsigned precision = out.precision();
+  char fill = out.fill();
+  std::string str = x.to_string(10, _AP_S);
+  str = reduceToPrecision(str, precision);
+  if (width > str.length()) {
+    for (unsigned i = 0; i < width - str.length(); ++i)
+      out << fill;
+  }
+  out << str;
+  return out;
+}
+#endif // ifndef __SYNTHESIS__
+
+/// Input streaming
+// -----------------------------------------------------------------------------
+#ifndef __SYNTHESIS__
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE std::istream& operator>>(
+    std::istream& in,
+    ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& x) {
+  double d;
+  in >> d;
+  x = ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>(d);
+  return in;
+}
+#endif
+#endif // ifndef AP_AUTOCC
+
+/// Operators mixing Integers with ap_fixed_base
+// -----------------------------------------------------------------------------
+#define AF_BIN_OP_WITH_INT_SF(BIN_OP, C_TYPE, _AP_W2, _AP_S2, RTYPE)     \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,           \
+            ap_o_mode _AP_O, int _AP_N>                                  \
+  INLINE typename ap_fixed_base<_AP_W, _AP_I, _AP_S>::template RType<    \
+      _AP_W2, _AP_W2, _AP_S2>::RTYPE                                     \
+  operator BIN_OP(                                                       \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op, \
+      C_TYPE i_op) {                                                     \
+    return op.operator BIN_OP(ap_int_base<_AP_W2, _AP_S2>(i_op));        \
+  }
+
+#define AF_BIN_OP_WITH_INT(BIN_OP, C_TYPE, _AP_W2, _AP_S2, RTYPE)           \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N>                                     \
+  INLINE typename ap_fixed_base<_AP_W, _AP_I, _AP_S>::template RType<       \
+      _AP_W2, _AP_W2, _AP_S2>::RTYPE                                        \
+  operator BIN_OP(                                                          \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,    \
+      C_TYPE i_op) {                                                        \
+    return op.operator BIN_OP(ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op)); \
+  }                                                                         \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N>                                     \
+  INLINE typename ap_fixed_base<_AP_W, _AP_I, _AP_S>::template RType<       \
+      _AP_W2, _AP_W2, _AP_S2>::RTYPE                                        \
+  operator BIN_OP(                                                          \
+      C_TYPE i_op,                                                          \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) {  \
+    return ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op).operator BIN_OP(op); \
+  }
+
+#define AF_REL_OP_WITH_INT(REL_OP, C_TYPE, _AP_W2, _AP_S2)                  \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N>                                     \
+  INLINE bool operator REL_OP(                                              \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,    \
+      C_TYPE i_op) {                                                        \
+    return op.operator REL_OP(ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op)); \
+  }                                                                         \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N>                                     \
+  INLINE bool operator REL_OP(                                              \
+      C_TYPE i_op,                                                          \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) {  \
+    return ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op).operator REL_OP(op); \
+  }
+
+#define AF_ASSIGN_OP_WITH_INT(ASSIGN_OP, C_TYPE, _AP_W2, _AP_S2)               \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,                 \
+            ap_o_mode _AP_O, int _AP_N>                                        \
+  INLINE ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>&              \
+  operator ASSIGN_OP(                                                          \
+      ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,             \
+      C_TYPE i_op) {                                                           \
+    return op.operator ASSIGN_OP(ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op)); \
+  }
+
+#define AF_ASSIGN_OP_WITH_INT_SF(ASSIGN_OP, C_TYPE, _AP_W2, _AP_S2)  \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,       \
+            ap_o_mode _AP_O, int _AP_N>                              \
+  INLINE ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>&    \
+  operator ASSIGN_OP(                                                \
+      ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,   \
+      C_TYPE i_op) {                                                 \
+    return op.operator ASSIGN_OP(ap_int_base<_AP_W2, _AP_S2>(i_op)); \
+  }
+
+#define ALL_AF_OP_WITH_INT(C_TYPE, BITS, SIGN)               \
+  AF_BIN_OP_WITH_INT(+, C_TYPE, (BITS), (SIGN), plus)     \
+  AF_BIN_OP_WITH_INT(-, C_TYPE, (BITS), (SIGN), minus)    \
+  AF_BIN_OP_WITH_INT(*, C_TYPE, (BITS), (SIGN), mult)     \
+  AF_BIN_OP_WITH_INT(/, C_TYPE, (BITS), (SIGN), div)      \
+  AF_BIN_OP_WITH_INT(&, C_TYPE, (BITS), (SIGN), logic)    \
+  AF_BIN_OP_WITH_INT(|, C_TYPE, (BITS), (SIGN), logic)    \
+  AF_BIN_OP_WITH_INT(^, C_TYPE, (BITS), (SIGN), logic)    \
+  AF_BIN_OP_WITH_INT_SF(>>, C_TYPE, (BITS), (SIGN), lhs)  \
+  AF_BIN_OP_WITH_INT_SF(<<, C_TYPE, (BITS), (SIGN), lhs)  \
+                                                          \
+  AF_ASSIGN_OP_WITH_INT(+=, C_TYPE, (BITS), (SIGN))     \
+  AF_ASSIGN_OP_WITH_INT(-=, C_TYPE, (BITS), (SIGN))     \
+  AF_ASSIGN_OP_WITH_INT(*=, C_TYPE, (BITS), (SIGN))     \
+  AF_ASSIGN_OP_WITH_INT(/=, C_TYPE, (BITS), (SIGN))     \
+  AF_ASSIGN_OP_WITH_INT(&=, C_TYPE, (BITS), (SIGN))     \
+  AF_ASSIGN_OP_WITH_INT(|=, C_TYPE, (BITS), (SIGN))     \
+  AF_ASSIGN_OP_WITH_INT(^=, C_TYPE, (BITS), (SIGN))     \
+  AF_ASSIGN_OP_WITH_INT_SF(>>=, C_TYPE, (BITS), (SIGN)) \
+  AF_ASSIGN_OP_WITH_INT_SF(<<=, C_TYPE, (BITS), (SIGN)) \
+                                                          \
+  AF_REL_OP_WITH_INT(>, C_TYPE, (BITS), (SIGN))           \
+  AF_REL_OP_WITH_INT(<, C_TYPE, (BITS), (SIGN))           \
+  AF_REL_OP_WITH_INT(>=, C_TYPE, (BITS), (SIGN))          \
+  AF_REL_OP_WITH_INT(<=, C_TYPE, (BITS), (SIGN))          \
+  AF_REL_OP_WITH_INT(==, C_TYPE, (BITS), (SIGN))          \
+  AF_REL_OP_WITH_INT(!=, C_TYPE, (BITS), (SIGN))
+
+ALL_AF_OP_WITH_INT(bool, 1, false)
+ALL_AF_OP_WITH_INT(char, 8, CHAR_IS_SIGNED)
+ALL_AF_OP_WITH_INT(signed char, 8, true)
+ALL_AF_OP_WITH_INT(unsigned char, 8, false)
+ALL_AF_OP_WITH_INT(short, _AP_SIZE_short, true)
+ALL_AF_OP_WITH_INT(unsigned short, _AP_SIZE_short, false)
+ALL_AF_OP_WITH_INT(int, _AP_SIZE_int, true)
+ALL_AF_OP_WITH_INT(unsigned int, _AP_SIZE_int, false)
+ALL_AF_OP_WITH_INT(long, _AP_SIZE_long, true)
+ALL_AF_OP_WITH_INT(unsigned long, _AP_SIZE_long, false)
+ALL_AF_OP_WITH_INT(ap_slong, _AP_SIZE_ap_slong, true)
+ALL_AF_OP_WITH_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef ALL_AF_OP_WITH_INT
+#undef AF_BIN_OP_WITH_INT
+#undef AF_BIN_OP_WITH_INT_SF
+#undef AF_ASSIGN_OP_WITH_INT
+#undef AF_ASSIGN_OP_WITH_INT_SF
+#undef AF_REL_OP_WITH_INT
+
+/*
+ * **********************************************************************
+ * TODO
+ * There is no operator defined with float/double/long double, so that
+ * code like
+ *   ap_fixed<8,4> a = 1.5f;
+ *   a += 0.5f;
+ * will fail in compilation.
+ * Operator with warning about conversion might be wanted.
+ * **********************************************************************
+ */
+
+#define AF_BIN_OP_WITH_AP_INT(BIN_OP, RTYPE)                                \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>            \
+  INLINE typename ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>::template RType<    \
+      _AP_W, _AP_I, _AP_S>::RTYPE                                           \
+  operator BIN_OP(                                                          \
+      const ap_int_base<_AP_W2, _AP_S2>& i_op,                              \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) {  \
+    return ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op).operator BIN_OP(op); \
+  }                                                                         \
+                                                                            \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>            \
+  INLINE typename ap_fixed_base<_AP_W, _AP_I, _AP_S>::template RType<       \
+      _AP_W2, _AP_W2, _AP_S2>::RTYPE                                        \
+  operator BIN_OP(                                                          \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,    \
+      const ap_int_base<_AP_W2, _AP_S2>& i_op) {                            \
+    return op.operator BIN_OP(ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op)); \
+  }
+
+#define AF_REL_OP_WITH_AP_INT(REL_OP)                                       \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>            \
+  INLINE bool operator REL_OP(                                              \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,    \
+      const ap_int_base<_AP_W2, _AP_S2>& i_op) {                            \
+    return op.operator REL_OP(ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op)); \
+  }                                                                         \
+                                                                            \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>            \
+  INLINE bool operator REL_OP(                                              \
+      const ap_int_base<_AP_W2, _AP_S2>& i_op,                              \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) {  \
+    return ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op).operator REL_OP(op); \
+  }
+
+#define AF_ASSIGN_OP_WITH_AP_INT(ASSIGN_OP)                                    \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,                 \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>               \
+  INLINE ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>&              \
+  operator ASSIGN_OP(                                                          \
+      ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,             \
+      const ap_int_base<_AP_W2, _AP_S2>& i_op) {                               \
+    return op.operator ASSIGN_OP(ap_fixed_base<_AP_W2, _AP_W2, _AP_S2>(i_op)); \
+  }                                                                            \
+                                                                               \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,                 \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>               \
+  INLINE ap_int_base<_AP_W2, _AP_S2>& operator ASSIGN_OP(                      \
+      ap_int_base<_AP_W2, _AP_S2>& i_op,                                       \
+      const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) {     \
+    return i_op.operator ASSIGN_OP(op.to_ap_int_base());                       \
+  }
+
+AF_BIN_OP_WITH_AP_INT(+, plus)
+AF_BIN_OP_WITH_AP_INT(-, minus)
+AF_BIN_OP_WITH_AP_INT(*, mult)
+AF_BIN_OP_WITH_AP_INT(/, div)
+AF_BIN_OP_WITH_AP_INT(&, logic)
+AF_BIN_OP_WITH_AP_INT(|, logic)
+AF_BIN_OP_WITH_AP_INT(^, logic)
+
+#undef AF_BIN_OP_WITH_AP_INT
+
+AF_ASSIGN_OP_WITH_AP_INT(+=)
+AF_ASSIGN_OP_WITH_AP_INT(-=)
+AF_ASSIGN_OP_WITH_AP_INT(*=)
+AF_ASSIGN_OP_WITH_AP_INT(/=)
+AF_ASSIGN_OP_WITH_AP_INT(&=)
+AF_ASSIGN_OP_WITH_AP_INT(|=)
+AF_ASSIGN_OP_WITH_AP_INT(^=)
+
+#undef AF_ASSIGN_OP_WITH_AP_INT
+
+AF_REL_OP_WITH_AP_INT(==)
+AF_REL_OP_WITH_AP_INT(!=)
+AF_REL_OP_WITH_AP_INT(>)
+AF_REL_OP_WITH_AP_INT(>=)
+AF_REL_OP_WITH_AP_INT(<)
+AF_REL_OP_WITH_AP_INT(<=)
+
+#undef AF_REL_OP_WITH_AP_INT
+
+// Relational Operators with double
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE bool operator==(
+    double op1,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op2) {
+  return op2.operator==(op1);
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE bool operator!=(
+    double op1,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op2) {
+  return op2.operator!=(op1);
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE bool operator>(
+    double op1,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op2) {
+  return op2.operator<(op1);
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE bool operator>=(
+    double op1,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op2) {
+  return op2.operator<=(op1);
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE bool operator<(
+    double op1,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op2) {
+  return op2.operator>(op1);
+}
+
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE bool operator<=(
+    double op1,
+    const ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op2) {
+  return op2.operator>=(op1);
+}
+
+#endif // ifndef __cplusplus else
+
+#endif // ifndef __AP_FIXED_BASE_H__ else
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_fixed_ref.h b/model_1/hls4ml_prj/firmware/ap_types/ap_fixed_ref.h
new file mode 100644
index 0000000..aefda0a
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_fixed_ref.h
@@ -0,0 +1,718 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_FIXED_REF_H__
+#define __AP_FIXED_REF_H__
+
+#ifndef __AP_FIXED_H__
+#error "Only ap_fixed.h and ap_int.h can be included directly in user code."
+#endif
+
+#ifndef __cplusplus
+#error "C++ is required to include this header file"
+
+#else
+#ifndef __SYNTHESIS__
+#include <iostream>
+#endif
+/// Proxy class, which allows bit selection  to be used as both rvalue (for
+/// reading) and lvalue (for writing)
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+struct af_bit_ref {
+#ifdef _MSC_VER
+#pragma warning(disable : 4521 4522)
+#endif
+  typedef ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> ref_type;
+  ref_type& d_bv;
+  int d_index;
+
+ public:
+  INLINE af_bit_ref(
+      const af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& ref)
+      : d_bv(ref.d_bv), d_index(ref.d_index) {
+#ifndef __SYNTHESIS__
+    _AP_WARNING(d_index < 0, "Index of bit vector  (%d) cannot be negative.",
+                d_index);
+    _AP_WARNING(d_index >= _AP_W, "Index of bit vector (%d) out of range (%d).",
+                d_index, _AP_W);
+#endif
+  }
+
+  INLINE af_bit_ref(ref_type* bv, int index = 0) : d_bv(*bv), d_index(index) {}
+
+  INLINE af_bit_ref(const ref_type* bv, int index = 0)
+      : d_bv(*const_cast<ref_type*>(bv)), d_index(index) {}
+
+  /// convert operators.
+  INLINE operator bool() const { return _AP_ROOT_op_get_bit(d_bv.V, d_index); }
+
+  /// @name assign operators
+  //  @{
+  INLINE af_bit_ref& operator=(bool val) {
+    d_bv.V = _AP_ROOT_op_set_bit(d_bv.V, d_index, val);
+    return *this;
+  }
+
+  // Be explicit to prevent it from being deleted, as field d_bv
+  // is of reference type.
+  INLINE af_bit_ref& operator=(const af_bit_ref& val) {
+    return operator=(bool(val));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE af_bit_ref& operator=(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    return operator=(bool(val));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE af_bit_ref& operator=(const ap_bit_ref<_AP_W2, _AP_S2>& val) {
+    return operator=(bool(val));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE af_bit_ref& operator=(const ap_int_base<_AP_W2, _AP_S2>& val) {
+    return operator=(val != 0);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE af_bit_ref& operator=(const ap_range_ref<_AP_W2, _AP_S2>& val) {
+    return operator=(ap_int_base<_AP_W2, false>(val));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE af_bit_ref& operator=(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    return operator=(ap_int_base<_AP_W2, false>(val));
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE af_bit_ref& operator=(
+      const ap_concat_ref<_AP_W2, _AP_T3, _AP_W3, _AP_T3>& val) {
+    return operator=(ap_int_base<_AP_W2 + _AP_W3, false>(val));
+  }
+  //  @}
+
+  /// @name concatenate operators
+  //  @{
+  template <int _AP_W2, int _AP_S2>
+  INLINE ap_concat_ref<1, af_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  operator,(ap_int_base<_AP_W2, _AP_S2> &op) {
+    return ap_concat_ref<1, af_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >(
+        *this, op);
+  }
+
+  template <int _AP_W2, int _AP_S2>
+  INLINE ap_concat_ref<1, af_bit_ref, 1, ap_bit_ref<_AP_W2, _AP_S2> > operator,(
+      const ap_bit_ref<_AP_W2, _AP_S2> &op) {
+    return ap_concat_ref<1, af_bit_ref, 1, ap_bit_ref<_AP_W2, _AP_S2> >(*this,
+                                                                        op);
+  }
+
+  template <int _AP_W2, int _AP_S2>
+  INLINE ap_concat_ref<1, af_bit_ref, _AP_W2, ap_range_ref<_AP_W2, _AP_S2> >
+  operator,(const ap_range_ref<_AP_W2, _AP_S2> &op) {
+    return ap_concat_ref<1, af_bit_ref, _AP_W2, ap_range_ref<_AP_W2, _AP_S2> >(
+        *this, op);
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_concat_ref<1, af_bit_ref, _AP_W2 + _AP_W3,
+                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+  operator,(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &op) {
+    return ap_concat_ref<1, af_bit_ref, _AP_W2 + _AP_W3,
+                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(*this,
+                                                                         op);
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_concat_ref<
+      1, af_bit_ref, _AP_W2,
+      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+  operator,(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &op) {
+    return ap_concat_ref<
+        1, af_bit_ref, _AP_W2,
+        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(*this,
+                                                                       op);
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_concat_ref<1, af_bit_ref, 1, af_bit_ref<_AP_W2, _AP_I2, _AP_S2,
+                                                    _AP_Q2, _AP_O2, _AP_N2> >
+  operator,(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &op) {
+    return ap_concat_ref<1, af_bit_ref, 1, af_bit_ref<_AP_W2, _AP_I2, _AP_S2,
+                                                      _AP_Q2, _AP_O2, _AP_N2> >(
+        *this,
+        const_cast<af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(
+            op));
+  }
+  //  @}
+
+  /// @name comparison
+  //  @{
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE bool operator==(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    return get() == op.get();
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE bool operator!=(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    return get() != op.get();
+  }
+  //  @}
+
+  INLINE bool operator~() const {
+    bool bit = _AP_ROOT_op_get_bit(d_bv.V, d_index);
+    return bit ? false : true;
+  }
+
+  INLINE bool get() const { return _AP_ROOT_op_get_bit(d_bv.V, d_index); }
+
+  INLINE int length() const { return 1; }
+
+#ifndef __SYNTHESIS__
+  std::string to_string() const { return get() ? "1" : "0"; }
+#else
+  // XXX HLS will delete this in synthesis
+  INLINE char* to_string() const { return 0; }
+#endif
+}; // struct af_bit_ref
+
+// XXX apcc cannot handle global std::ios_base::Init() brought in by <iostream>
+#ifndef AP_AUTOCC
+#ifndef __SYNTHESIS__
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE std::ostream& operator<<(
+    std::ostream& os,
+    const af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& x) {
+  os << x.to_string();
+  return os;
+}
+#endif // ifndef __SYNTHESIS__
+#endif // ifndef AP_AUTOCC
+
+/// Range (slice) reference.
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+struct af_range_ref {
+#ifdef _MSC_VER
+#pragma warning(disable : 4521 4522)
+#endif
+  typedef ap_fixed_base<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> ref_type;
+  ref_type& d_bv;
+  int l_index;
+  int h_index;
+
+ public:
+  /// copy ctor
+  INLINE af_range_ref(
+      const af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& ref)
+      : d_bv(ref.d_bv), l_index(ref.l_index), h_index(ref.h_index) {}
+
+  /// ctor from ap_fixed_base, higher and lower bound.
+  /** if h is less than l, the bits selected will be returned in reverse order.
+   */
+  INLINE af_range_ref(ref_type* bv, int h, int l)
+      : d_bv(*bv), l_index(l), h_index(h) {
+#ifndef __SYNTHESIS__
+    _AP_WARNING(h < 0 || l < 0,
+                "Higher bound(%d) and lower(%d) bound cannot be negative.", h,
+                l);
+    _AP_WARNING(h >= _AP_W || l >= _AP_W,
+                "Higher bound(%d) or lower(%d) bound out of range.", h, l);
+    _AP_WARNING(h < l, "The bits selected will be returned in reverse order.");
+#endif
+  }
+
+  INLINE af_range_ref(const ref_type* bv, int h, int l)
+      : d_bv(*const_cast<ref_type*>(bv)), l_index(l), h_index(h) {
+#ifndef __SYNTHESIS__
+    _AP_WARNING(h < 0 || l < 0,
+                "Higher bound(%d) and lower(%d) bound cannot be negative.", h,
+                l);
+    _AP_WARNING(h >= _AP_W || l >= _AP_W,
+                "Higher bound(%d) or lower(%d) bound out of range.", h, l);
+    _AP_WARNING(h < l, "The bits selected will be returned in reverse order.");
+#endif
+  }
+
+  /// @name assign operators
+  //  @{
+
+#define ASSIGN_CTYPE_TO_AF_RANGE(DATA_TYPE)                          \
+  INLINE af_range_ref& operator=(const DATA_TYPE val) {              \
+    ap_int_base<_AP_W, false> loc(val);                              \
+    d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, loc.V); \
+    return *this;                                                    \
+  }
+
+  ASSIGN_CTYPE_TO_AF_RANGE(bool)
+  ASSIGN_CTYPE_TO_AF_RANGE(char)
+  ASSIGN_CTYPE_TO_AF_RANGE(signed char)
+  ASSIGN_CTYPE_TO_AF_RANGE(unsigned char)
+  ASSIGN_CTYPE_TO_AF_RANGE(short)
+  ASSIGN_CTYPE_TO_AF_RANGE(unsigned short)
+  ASSIGN_CTYPE_TO_AF_RANGE(int)
+  ASSIGN_CTYPE_TO_AF_RANGE(unsigned int)
+  ASSIGN_CTYPE_TO_AF_RANGE(long)
+  ASSIGN_CTYPE_TO_AF_RANGE(unsigned long)
+  ASSIGN_CTYPE_TO_AF_RANGE(ap_slong)
+  ASSIGN_CTYPE_TO_AF_RANGE(ap_ulong)
+#if _AP_ENABLE_HALF_ == 1
+  ASSIGN_CTYPE_TO_AF_RANGE(half)
+#endif
+  ASSIGN_CTYPE_TO_AF_RANGE(float)
+  ASSIGN_CTYPE_TO_AF_RANGE(double)
+#undef ASSIGN_CTYPE_TO_AF_RANGE
+
+  /// assgin using a string. XXX crucial for cosim.
+  INLINE af_range_ref& operator=(const char* val) {
+    const ap_int_base<_AP_W, false> tmp(val); // XXX figure out radix
+    d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, tmp.V);
+    return *this;
+  }
+
+  /// assign from ap_int_base.
+  // NOTE Base of other assgin operators.
+  template <int _AP_W3, bool _AP_S3>
+  INLINE af_range_ref& operator=(const ap_int_base<_AP_W3, _AP_S3>& val) {
+    d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, val.V);
+    return *this;
+  }
+
+  /// assign from range reference to ap_int_base.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE af_range_ref& operator=(const ap_range_ref<_AP_W2, _AP_S2>& val) {
+    const ap_int_base<_AP_W2, false> tmp(val);
+    return operator=(tmp);
+  }
+
+  /// assign from bit reference to ap_int_base..
+  template <int _AP_W2, bool _AP_S2>
+  INLINE af_range_ref& operator=(const ap_bit_ref<_AP_W2, _AP_S2>& val) {
+    const ap_int_base<1, false> tmp((bool)val);
+    return operator=(tmp);
+  }
+
+  /// assgin from ap_fixed_base.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE af_range_ref& operator=(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&
+          val) {
+    d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, val.V);
+    return *this;
+  }
+
+  /// copy assgin.
+  // XXX This has to be explicit, otherwise it will be deleted, as d_bv is
+  // of reference type.
+  INLINE af_range_ref& operator=(const af_range_ref& val) {
+    ap_int_base<_AP_W, false> tmp(val);
+    return operator=(tmp);
+  }
+
+  /// assign from range reference to ap_fixed_base.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE af_range_ref& operator=(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    ap_int_base<_AP_W2, false> tmp(val);
+    return operator=(tmp);
+  }
+
+  /// assign from bit reference to ap_fixed_base.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE af_range_ref& operator=(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    ap_int_base<1, false> tmp((bool)val);
+    return operator=(tmp);
+  }
+
+  /// assign from compound reference.
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE af_range_ref& operator=(
+      const ap_concat_ref<_AP_W2, _AP_T3, _AP_W3, _AP_T3>& val) {
+    const ap_int_base<_AP_W2 + _AP_W3, false> tmp(val);
+    return operator=(tmp);
+  }
+  //  @}
+
+  /// @name comparison operators with ap_range_ref.
+  //  @{
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator==(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_int_base<_AP_W, false> lop(*this);
+    ap_int_base<_AP_W2, false> rop(op2);
+    return lop == rop;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator!=(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    return !(operator==(op2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_int_base<_AP_W, false> lop(*this);
+    ap_int_base<_AP_W2, false> rop(op2);
+    return lop < rop;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_int_base<_AP_W, false> lop(*this);
+    ap_int_base<_AP_W2, false> rop(op2);
+    return lop > rop;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<=(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    return !(operator>(op2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>=(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    return !(operator<(op2));
+  }
+  //  @}
+
+  /// @name comparison operators with af_range_ref.
+  //  @{
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE bool operator==(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op2) {
+    ap_int_base<_AP_W, false> lop(*this);
+    ap_int_base<_AP_W2, false> rop(op2);
+    return lop == rop;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE bool operator!=(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op2) {
+    return !(operator==(op2));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE bool operator<(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op2) {
+    ap_int_base<_AP_W, false> lop(*this);
+    ap_int_base<_AP_W2, false> rop(op2);
+    return lop < rop;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE bool operator>(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op2) {
+    ap_int_base<_AP_W, false> lop(*this);
+    ap_int_base<_AP_W2, false> rop(op2);
+    return lop > rop;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE bool operator<=(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op2) {
+    return !(operator>(op2));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE bool operator>=(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op2) {
+    return !(operator<(op2));
+  }
+  //  @}
+
+  /// @name concatenate operators.
+  /// @{
+  /// concatenate with ap_int_base.
+  template <int _AP_W2, int _AP_S2>
+  INLINE
+      ap_concat_ref<_AP_W, af_range_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+      operator,(ap_int_base<_AP_W2, _AP_S2> &op) {
+    return ap_concat_ref<_AP_W, af_range_ref, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(*this, op);
+  }
+
+  /// concatenate with ap_bit_ref.
+  template <int _AP_W2, int _AP_S2>
+  INLINE ap_concat_ref<_AP_W, af_range_ref, 1, ap_bit_ref<_AP_W2, _AP_S2> >
+  operator,(const ap_bit_ref<_AP_W2, _AP_S2> &op) {
+    return ap_concat_ref<_AP_W, af_range_ref, 1, ap_bit_ref<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_bit_ref<_AP_W2, _AP_S2>&>(op));
+  }
+
+  /// concatenate with ap_bit_ref.
+  template <int _AP_W2, int _AP_S2>
+  INLINE ap_concat_ref<_AP_W, af_range_ref, _AP_W2, ap_range_ref<_AP_W2, _AP_S2> >
+  operator,(const ap_range_ref<_AP_W2, _AP_S2> &op) {
+    return ap_concat_ref<_AP_W, af_range_ref, _AP_W2,
+                         ap_range_ref<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_range_ref<_AP_W2, _AP_S2>&>(op));
+  }
+
+  /// concatenate with ap_concat_ref.
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_concat_ref<_AP_W, af_range_ref, _AP_W2 + _AP_W3,
+                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+  operator,(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &op) {
+    return ap_concat_ref<_AP_W, af_range_ref, _AP_W2 + _AP_W3,
+                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(
+        *this, const_cast<ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>&>(op));
+  }
+
+  /// concatenate with another af_range_ref.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE
+      ap_concat_ref<_AP_W, af_range_ref, _AP_W2,
+                    af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+      operator,(const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>
+                    &op) {
+    return ap_concat_ref<
+        _AP_W, af_range_ref, _AP_W2,
+        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+        *this,
+        const_cast<af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(
+            op));
+  }
+
+  /// concatenate with another af_bit_ref.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE
+      ap_concat_ref<_AP_W, af_range_ref, 1,
+                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+      operator,(
+          const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &op) {
+    return ap_concat_ref<
+        _AP_W, af_range_ref, 1,
+        af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+        *this,
+        const_cast<af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(
+            op));
+  }
+  //  @}
+
+  INLINE operator ap_ulong() const {
+    ap_int_base<_AP_W, false> ret;
+    ret.V = _AP_ROOT_op_get_range(d_bv.V, l_index, h_index);
+    return ret.to_uint64();
+  }
+
+  INLINE operator ap_int_base<_AP_W, false>() const {
+    ap_int_base<_AP_W, false> ret;
+    ret.V = _AP_ROOT_op_get_range(d_bv.V, l_index, h_index);
+    return ret;
+  }
+
+  INLINE ap_int_base<_AP_W, false> to_ap_int_base() const {
+    ap_int_base<_AP_W, false> ret;
+    ret.V = _AP_ROOT_op_get_range(d_bv.V, l_index, h_index);
+    return ret;
+  }
+
+  // used in ap_fixed_base::to_string()
+  INLINE char to_char() const {
+    return (char)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE int to_int() const {
+    return (int)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE unsigned to_uint() const {
+    return (unsigned)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE long to_long() const {
+    return (long)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE unsigned long to_ulong() const {
+    return (unsigned long)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE ap_slong to_int64() const {
+    return (ap_slong)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE ap_ulong to_uint64() const {
+    return (ap_ulong)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE ap_int_base<_AP_W, false> get() const {
+    ap_int_base<_AP_W, false> ret;
+    ret.V = _AP_ROOT_op_get_range(d_bv.V, l_index, h_index);
+    return ret;
+  }
+
+  template <int _AP_W2>
+  INLINE void set(const ap_int_base<_AP_W2, false>& val) {
+    d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, val.V);
+  }
+
+  INLINE int length() const {
+    return h_index >= l_index ? h_index - l_index + 1 : l_index - h_index + 1;
+  }
+
+#ifndef __SYNTHESIS__
+  std::string to_string(signed char rd = 2) const {
+    ap_int_base<_AP_W, false> ret;
+    ret.V = _AP_ROOT_op_get_range(d_bv.V, l_index, h_index);
+    return ret.to_string(rd);
+  }
+#else
+  // XXX HLS will delete this in synthesis
+  INLINE char* to_string(signed char rd = 2) const {
+    return 0;
+  }
+#endif
+}; // struct af_range_ref
+
+// XXX apcc cannot handle global std::ios_base::Init() brought in by <iostream>
+#ifndef AP_AUTOCC
+#ifndef __SYNTHESIS__
+template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q, ap_o_mode _AP_O,
+          int _AP_N>
+INLINE std::ostream& operator<<(
+    std::ostream& os,
+    const af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& x) {
+  os << x.to_string();
+  return os;
+}
+#endif
+#endif // ifndef AP_AUTOCC
+
+#define AF_REF_REL_OP_WITH_INT(REL_OP, C_TYPE, _AP_W2, _AP_S2)            \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,            \
+            ap_o_mode _AP_O, int _AP_N>                                   \
+  INLINE bool operator REL_OP(                                            \
+      const af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,   \
+      C_TYPE op2) {                                                       \
+    return ap_int_base<_AP_W, false>(op)                                  \
+        REL_OP ap_int_base<_AP_W2, _AP_S2>(op2);                          \
+  }                                                                       \
+                                                                          \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,            \
+            ap_o_mode _AP_O, int _AP_N>                                   \
+  INLINE bool operator REL_OP(                                            \
+      C_TYPE op2,                                                         \
+      const af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) { \
+    return ap_int_base<_AP_W2, _AP_S2>(op2)                               \
+        REL_OP ap_int_base<_AP_W, false>(op);                             \
+  }                                                                       \
+                                                                          \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,            \
+            ap_o_mode _AP_O, int _AP_N>                                   \
+  INLINE bool operator REL_OP(                                            \
+      const af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,     \
+      C_TYPE op2) {                                                       \
+    return bool(op) REL_OP op2;                                           \
+  }                                                                       \
+                                                                          \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,            \
+            ap_o_mode _AP_O, int _AP_N>                                   \
+  INLINE bool operator REL_OP(                                            \
+      C_TYPE op2,                                                         \
+      const af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) {   \
+    return op2 REL_OP bool(op);                                           \
+  }
+
+#define AF_REF_REL_OPS_WITH_INT(C_TYPE, _AP_W2, _AP_S2)  \
+  AF_REF_REL_OP_WITH_INT(>, C_TYPE, (_AP_W2), (_AP_S2))  \
+  AF_REF_REL_OP_WITH_INT(<, C_TYPE, (_AP_W2), (_AP_S2))  \
+  AF_REF_REL_OP_WITH_INT(>=, C_TYPE, (_AP_W2), (_AP_S2)) \
+  AF_REF_REL_OP_WITH_INT(<=, C_TYPE, (_AP_W2), (_AP_S2)) \
+  AF_REF_REL_OP_WITH_INT(==, C_TYPE, (_AP_W2), (_AP_S2)) \
+  AF_REF_REL_OP_WITH_INT(!=, C_TYPE, (_AP_W2), (_AP_S2))
+
+AF_REF_REL_OPS_WITH_INT(bool, 1, false)
+AF_REF_REL_OPS_WITH_INT(char, 8, CHAR_IS_SIGNED)
+AF_REF_REL_OPS_WITH_INT(signed char, 8, true)
+AF_REF_REL_OPS_WITH_INT(unsigned char, 8, false)
+AF_REF_REL_OPS_WITH_INT(short, _AP_SIZE_short, true)
+AF_REF_REL_OPS_WITH_INT(unsigned short, _AP_SIZE_short, false)
+AF_REF_REL_OPS_WITH_INT(int, _AP_SIZE_int, true)
+AF_REF_REL_OPS_WITH_INT(unsigned int, _AP_SIZE_int, false)
+AF_REF_REL_OPS_WITH_INT(long, _AP_SIZE_long, true)
+AF_REF_REL_OPS_WITH_INT(unsigned long, _AP_SIZE_long, false)
+AF_REF_REL_OPS_WITH_INT(ap_slong, _AP_SIZE_ap_slong, true)
+AF_REF_REL_OPS_WITH_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef AF_REF_REL_OP_INT
+#undef AF_REF_REL_OPS_WITH_INT
+
+#define AF_REF_REL_OP_WITH_AP_INT(REL_OP)                                 \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,            \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>          \
+  INLINE bool operator REL_OP(                                            \
+      const af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,   \
+      const ap_int_base<_AP_W2, _AP_S>& op2) {                            \
+    return ap_int_base<_AP_W, false>(op) REL_OP op2;                      \
+  }                                                                       \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,            \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>          \
+  INLINE bool operator REL_OP(                                            \
+      const ap_int_base<_AP_W2, _AP_S2>& op2,                             \
+      const af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) { \
+    return op2 REL_OP ap_int_base<_AP_W, false>(op);                      \
+  }                                                                       \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,            \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>          \
+  INLINE bool operator REL_OP(                                            \
+      const af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op,     \
+      const ap_int_base<_AP_W2, _AP_S2>& op2) {                           \
+    return ap_int_base<1, false>(op) REL_OP op2;                          \
+  }                                                                       \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,            \
+            ap_o_mode _AP_O, int _AP_N, int _AP_W2, bool _AP_S2>          \
+  INLINE bool operator REL_OP(                                            \
+      const ap_int_base<_AP_W2, _AP_S2>& op2,                             \
+      const af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N>& op) {   \
+    return op2 REL_OP ap_int_base<1, false>(op);                          \
+  }
+
+AF_REF_REL_OP_WITH_AP_INT(>)
+AF_REF_REL_OP_WITH_AP_INT(<)
+AF_REF_REL_OP_WITH_AP_INT(>=)
+AF_REF_REL_OP_WITH_AP_INT(<=)
+AF_REF_REL_OP_WITH_AP_INT(==)
+AF_REF_REL_OP_WITH_AP_INT(!=)
+
+#endif // ifndef __cplusplus
+
+#endif // ifndef __AP_FIXED_REF_H__
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_fixed_special.h b/model_1/hls4ml_prj/firmware/ap_types/ap_fixed_special.h
new file mode 100644
index 0000000..0f7a9f7
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_fixed_special.h
@@ -0,0 +1,230 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_FIXED_SPECIAL_H__
+#define __AP_FIXED_SPECIAL_H__
+
+#ifndef __AP_FIXED_H__
+#error "Only ap_fixed.h and ap_int.h can be included directly in user code."
+#endif
+
+#ifndef __SYNTHESIS__
+#include <cstdio>
+#include <cstdlib>
+#endif
+// FIXME AP_AUTOCC cannot handle many standard headers, so declare instead of
+// include.
+// #include <complex>
+namespace std {
+template<typename _Tp> class complex;
+}
+
+/*
+  TODO: Modernize the code using C++11/C++14
+  1. constexpr http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2016/p0415r0.html
+  2. move constructor
+*/
+
+namespace std {
+/*
+   Specialize std::complex<ap_fixed> to zero initialization ap_fixed.
+
+   To reduce the area cost, ap_fixed is not zero initialized, just like basic
+   types float or double. However, libstdc++ provides specialization for float,
+   double and long double, initializing image part to 0 when not specified.
+
+   This has become a difficulty in switching legacy code from these C types to
+   ap_fixed. To ease the tranform of legacy code, we have to implement
+   specialization of std::complex<> for our type.
+
+   As ap_fixed is a template, it is impossible to specialize only the methods
+   that causes default initialization of value type in std::complex<>. An
+   explicit full specialization of the template class has to be done, covering
+   all the member functions and operators of std::complex<> as specified
+   in standard 26.2.4 and 26.2.5.
+*/
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q, ap_o_mode _AP_O, int _AP_N>
+class complex<ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> > {
+ public:
+  typedef ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> _Tp;
+  typedef _Tp value_type;
+
+  // 26.2.4/1
+  // Constructor without argument
+  // Default initialize, so that in dataflow, the variable is only written once.
+  complex() : _M_real(_Tp()), _M_imag(_Tp()) {}
+  // Constructor with ap_fixed.
+  // Zero initialize image part when not specified, so that `C(1) == C(1,0)`
+  complex(const _Tp &__r, const _Tp &__i = _Tp(0))
+      : _M_real(__r), _M_imag(__i) {}
+
+  // Constructor with another complex number
+  template <typename _Up>
+  complex(const complex<_Up> &__z) : _M_real(__z.real()), _M_imag(__z.imag()) {}
+
+#if __cplusplus >= 201103L
+  const _Tp& real() const { return _M_real; }
+  const _Tp& imag() const { return _M_imag; }
+#else
+  _Tp& real() { return _M_real; }
+  const _Tp& real() const { return _M_real; }
+  _Tp& imag() { return _M_imag; }
+  const _Tp& imag() const { return _M_imag; }
+#endif
+ 
+  void real(_Tp __val) { _M_real = __val; }
+
+  void imag(_Tp __val) { _M_imag = __val; }
+
+  // Assign this complex number with ap_fixed.
+  // Zero initialize image poarrt, so that `C c; c = 1; c == C(1,0);`
+  complex<_Tp> &operator=(const _Tp __t) {
+    _M_real = __t;
+    _M_imag = _Tp(0);
+    return *this;
+  }
+
+  // 26.2.5/1
+  // Add ap_fixed to this complex number.
+  complex<_Tp> &operator+=(const _Tp &__t) {
+    _M_real += __t;
+    return *this;
+  }
+
+  // 26.2.5/3
+  // Subtract ap_fixed from this complex number.
+  complex<_Tp> &operator-=(const _Tp &__t) {
+    _M_real -= __t;
+    return *this;
+  }
+
+  // 26.2.5/5
+  // Multiply this complex number by ap_fixed.
+  complex<_Tp> &operator*=(const _Tp &__t) {
+    _M_real *= __t;
+    _M_imag *= __t;
+    return *this;
+  }
+
+  // 26.2.5/7
+  // Divide this complex number by ap_fixed.
+  complex<_Tp> &operator/=(const _Tp &__t) {
+    _M_real /= __t;
+    _M_imag /= __t;
+    return *this;
+  }
+
+  // Assign complex number to this complex number.
+  template <typename _Up>
+  complex<_Tp> &operator=(const complex<_Up> &__z) {
+    _M_real = __z.real();
+    _M_imag = __z.imag();
+    return *this;
+  }
+
+  // 26.2.5/9
+  // Add complex number to this.
+  template <typename _Up>
+  complex<_Tp> &operator+=(const complex<_Up> &__z) {
+    _M_real += __z.real();
+    _M_imag += __z.imag();
+    return *this;
+  }
+
+  // 26.2.5/11
+  // Subtract complex number from this.
+  template <typename _Up>
+  complex<_Tp> &operator-=(const complex<_Up> &__z) {
+    _M_real -= __z.real();
+    _M_imag -= __z.imag();
+    return *this;
+  }
+
+  // 26.2.5/13
+  // Multiply this by complex number.
+  template <typename _Up>
+  complex<_Tp> &operator*=(const complex<_Up> &__z) {
+    const _Tp __r = _M_real * __z.real() - _M_imag * __z.imag();
+    _M_imag = _M_real * __z.imag() + _M_imag * __z.real();
+    _M_real = __r;
+    return *this;
+  }
+
+  // 26.2.5/15
+  // Divide this by complex number.
+  template <typename _Up>
+  complex<_Tp> &operator/=(const complex<_Up> &__z) {
+    complex<_Tp> cj (__z.real(), -__z.imag());
+    complex<_Tp> a = (*this) * cj;
+    complex<_Tp> b = cj * __z;
+    _M_real = a.real() / b.real();
+    _M_imag = a.imag() / b.real();
+    return *this;
+  }
+
+ private:
+  _Tp _M_real;
+  _Tp _M_imag;
+
+}; // class complex<ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> >
+
+/*
+   Non-member operations
+   These operations are not required by standard in 26.2.6, but libstdc++
+   defines them for
+   float, double or long double's specialization.
+*/
+// Compare complex number with ap_fixed.
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q, ap_o_mode _AP_O, int _AP_N>
+inline bool operator==(
+    const complex<ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> > &__x,
+    const ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> &__y) {
+  return __x.real() == __y &&
+         __x.imag() == 0;
+}
+
+// Compare ap_fixed with complex number.
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q, ap_o_mode _AP_O, int _AP_N>
+inline bool operator==(
+    const ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> &__x,
+    const complex<ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> > &__y) {
+  return __x == __y.real() &&
+         0 == __y.imag();
+}
+
+// Compare complex number with ap_fixed.
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q, ap_o_mode _AP_O, int _AP_N>
+inline bool operator!=(
+    const complex<ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> > &__x,
+    const ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> &__y) {
+  return __x.real() != __y ||
+         __x.imag() != 0;
+}
+
+// Compare ap_fixed with complex number.
+template <int _AP_W, int _AP_I, ap_q_mode _AP_Q, ap_o_mode _AP_O, int _AP_N>
+inline bool operator!=(
+    const ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> &__x,
+    const complex<ap_fixed<_AP_W, _AP_I, _AP_Q, _AP_O, _AP_N> > &__y) {
+  return __x != __y.real() ||
+         0 != __y.imag();
+}
+
+}  // namespace std
+
+#endif  // ifndef __AP_FIXED_SPECIAL_H__
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_int.h b/model_1/hls4ml_prj/firmware/ap_types/ap_int.h
new file mode 100644
index 0000000..db3044d
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_int.h
@@ -0,0 +1,330 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_INT_H__
+#define __AP_INT_H__
+
+#include <ap_common.h>
+#include <ap_int_base.h>
+#include <ap_int_ref.h>
+
+//---------------------------------------------------------------
+
+/// Sign Arbitrary Precision Type.
+template <int _AP_W>
+struct ap_int : ap_int_base<_AP_W, true> {
+  typedef ap_int_base<_AP_W, true> Base;
+  // Constructor
+  INLINE ap_int() : Base() {}
+
+  // Copy ctor
+  INLINE ap_int(const ap_int& op) { Base::V = op.V; }
+
+  template <int _AP_W2>
+  INLINE ap_int(const ap_int<_AP_W2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2>
+  INLINE ap_int(const volatile ap_int<_AP_W2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2>
+  INLINE ap_int(const ap_uint<_AP_W2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2>
+  INLINE ap_int(const volatile ap_uint<_AP_W2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int(const ap_range_ref<_AP_W2, _AP_S2>& ref) : Base(ref) {}
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int(const ap_bit_ref<_AP_W2, _AP_S2>& ref) : Base(ref) {}
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_int(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& ref)
+      : Base(ref) {}
+
+  template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+            int _AP_N2>
+  INLINE ap_int(const ap_fixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base((ap_fixed_base<_AP_W2, _AP_I2, true, _AP_Q2, _AP_O2, _AP_N2>)op) {}
+
+  template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+            int _AP_N2>
+  INLINE ap_int(const ap_ufixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base((ap_fixed_base<_AP_W2, _AP_I2, false, _AP_Q2, _AP_O2, _AP_N2>)op) {
+  }
+
+  template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+            int _AP_N2>
+  INLINE ap_int(
+      const volatile ap_fixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base((ap_fixed_base<_AP_W2, _AP_I2, true, _AP_Q2, _AP_O2, _AP_N2>)op) {}
+
+  template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+            int _AP_N2>
+  INLINE ap_int(
+      const volatile ap_ufixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base((ap_fixed_base<_AP_W2, _AP_I2, false, _AP_Q2, _AP_O2, _AP_N2>)op) {
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int(const ap_int_base<_AP_W2, _AP_S2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_int(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_int(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_int(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+#define CTOR(TYPE) \
+  INLINE ap_int(TYPE val) { Base::V = val; }
+  CTOR(bool)
+  CTOR(char)
+  CTOR(signed char)
+  CTOR(unsigned char)
+  CTOR(short)
+  CTOR(unsigned short)
+  CTOR(int)
+  CTOR(unsigned int)
+  CTOR(long)
+  CTOR(unsigned long)
+  CTOR(ap_slong)
+  CTOR(ap_ulong)
+#undef CTOR
+  ap_int(double val) : Base(val) {}
+  ap_int(float val) : Base(val) {}
+#if _AP_ENABLE_HALF_ == 1
+  ap_int(half val) : Base(val) {}
+#endif
+
+  // ap_int_base will guess radix if radix is not provided.
+  INLINE ap_int(const char* s) : Base(s) {}
+
+  INLINE ap_int(const char* s, signed char rd) : Base(s, rd) {}
+
+  // Assignment
+  /* ctor will be used when right is not of proper type. */
+
+  INLINE ap_int& operator=(const ap_int<_AP_W>& op2) {
+    Base::V = op2.V;
+    return *this;
+  }
+
+  /* cannot bind volatile reference to non-volatile type. */
+  INLINE ap_int& operator=(const volatile ap_int<_AP_W>& op2) {
+    Base::V = op2.V;
+    return *this;
+  }
+
+  /* cannot return volatile *this. */
+  INLINE void operator=(const ap_int<_AP_W>& op2) volatile { Base::V = op2.V; }
+
+  INLINE void operator=(const volatile ap_int<_AP_W>& op2) volatile {
+    Base::V = op2.V;
+  }
+
+}; // struct ap_int.
+
+//---------------------------------------------------------------
+
+/// Unsigned Arbitrary Precision Type.
+template <int _AP_W>
+struct ap_uint : ap_int_base<_AP_W, false> {
+  typedef ap_int_base<_AP_W, false> Base;
+  // Constructor
+  INLINE ap_uint() : Base() {}
+
+  // Copy ctor
+  INLINE ap_uint(const ap_uint& op) { Base::V = op.V; }
+
+  template <int _AP_W2>
+  INLINE ap_uint(const ap_uint<_AP_W2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2>
+  INLINE ap_uint(const ap_int<_AP_W2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2>
+  INLINE ap_uint(const volatile ap_uint<_AP_W2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2>
+  INLINE ap_uint(const volatile ap_int<_AP_W2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_uint(const ap_range_ref<_AP_W2, _AP_S2>& ref) : Base(ref) {}
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_uint(const ap_bit_ref<_AP_W2, _AP_S2>& ref) : Base(ref) {}
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_uint(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& ref)
+      : Base(ref) {}
+
+  template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+            int _AP_N2>
+  INLINE ap_uint(const ap_fixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base((ap_fixed_base<_AP_W2, _AP_I2, true, _AP_Q2, _AP_O2, _AP_N2>)op) {}
+
+  template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+            int _AP_N2>
+  INLINE ap_uint(const ap_ufixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base((ap_fixed_base<_AP_W2, _AP_I2, false, _AP_Q2, _AP_O2, _AP_N2>)op) {
+  }
+
+  template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+            int _AP_N2>
+  INLINE ap_uint(
+      const volatile ap_fixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base((ap_fixed_base<_AP_W2, _AP_I2, true, _AP_Q2, _AP_O2, _AP_N2>)op) {}
+
+  template <int _AP_W2, int _AP_I2, ap_q_mode _AP_Q2, ap_o_mode _AP_O2,
+            int _AP_N2>
+  INLINE ap_uint(
+      const volatile ap_ufixed<_AP_W2, _AP_I2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base((ap_fixed_base<_AP_W2, _AP_I2, false, _AP_Q2, _AP_O2, _AP_N2>)op) {
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_uint(const ap_int_base<_AP_W2, _AP_S2>& op) {
+    Base::V = op.V;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_uint(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_uint(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_uint(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op)
+      : Base(op) {}
+
+#define CTOR(TYPE) \
+  INLINE ap_uint(TYPE val) { Base::V = val; }
+  CTOR(bool)
+  CTOR(char)
+  CTOR(signed char)
+  CTOR(unsigned char)
+  CTOR(short)
+  CTOR(unsigned short)
+  CTOR(int)
+  CTOR(unsigned int)
+  CTOR(long)
+  CTOR(unsigned long)
+  CTOR(ap_slong)
+  CTOR(ap_ulong)
+#undef CTOR
+  ap_uint(double val) : Base(val) {}
+  ap_uint(float val) : Base(val) {}
+#if _AP_ENABLE_HALF_ == 1
+  ap_uint(half val) : Base(val) {}
+#endif
+
+  // ap_int_base will guess radix if radix is not provided.
+  INLINE ap_uint(const char* s) : Base(s) {}
+
+  INLINE ap_uint(const char* s, signed char rd) : Base(s, rd) {}
+
+  // Assignment
+  /* XXX ctor will be used when right is not of proper type. */
+
+  INLINE ap_uint& operator=(const ap_uint<_AP_W>& op2) {
+    Base::V = op2.V;
+    return *this;
+  }
+
+  /* cannot bind volatile reference to non-volatile type. */
+  INLINE ap_uint& operator=(const volatile ap_uint<_AP_W>& op2) {
+    Base::V = op2.V;
+    return *this;
+  }
+
+  /* cannot return volatile *this. */
+  INLINE void operator=(const ap_uint<_AP_W>& op2) volatile { Base::V = op2.V; }
+
+  INLINE void operator=(const volatile ap_uint<_AP_W>& op2) volatile {
+    Base::V = op2.V;
+  }
+
+}; // struct ap_uint.
+
+#define ap_bigint ap_int
+#define ap_biguint ap_uint
+
+#if !defined(__SYNTHESIS__) && (defined(SYSTEMC_H) || defined(SYSTEMC_INCLUDED))
+// XXX sc_trace overload for ap_fixed is already included in
+// "ap_sysc/ap_sc_extras.h", so do not define in synthesis.
+template <int _AP_W>
+INLINE void sc_trace(sc_core::sc_trace_file* tf, const ap_int<_AP_W>& op,
+                     const std::string& name) {
+  if (tf) tf->trace(sc_dt::sc_lv<_AP_W>(op.to_string(2).c_str()), name);
+}
+
+template <int _AP_W>
+INLINE void sc_trace(sc_core::sc_trace_file* tf, const ap_uint<_AP_W>& op,
+                     const std::string& name) {
+  if (tf) tf->trace(sc_dt::sc_lv<_AP_W>(op.to_string(2).c_str()), name);
+}
+#endif // System C sim
+
+#include <ap_int_special.h>
+
+#endif // ifndef __AP_INT_H__ else
+
+// FIXME user should include ap_fixed.h when using ap_fixed.
+// to avoid circular inclusion, must check whether this is required by
+// ap_fixed.h
+#ifndef __AP_FIXED_H__
+#include <ap_fixed.h>
+#endif
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_int_base.h b/model_1/hls4ml_prj/firmware/ap_types/ap_int_base.h
new file mode 100644
index 0000000..091552a
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_int_base.h
@@ -0,0 +1,1885 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_INT_BASE_H__
+#define __AP_INT_BASE_H__
+
+#ifndef __AP_INT_H__
+#error "Only ap_fixed.h and ap_int.h can be included directly in user code."
+#endif
+
+#ifndef __cplusplus
+#error "C++ is required to include this header file"
+#else
+
+#include <ap_common.h>
+#ifndef __SYNTHESIS__
+#if _AP_ENABLE_HALF_ == 1
+#include <hls_half.h>
+#endif
+#include <iostream>
+#include <string.h>
+#endif
+
+/* ----------------------------------------------------------------
+ * ap_int_base: AutoPilot integer/Arbitrary precision integer.
+ * ----------------------------------------------------------------
+ */
+
+/* helper trait. Selecting the smallest C type that can hold the value,
+ * return 64 bit C type if not possible.
+ */
+template <int _AP_N, bool _AP_S>
+struct retval;
+
+// at least 64 bit
+template <int _AP_N>
+struct retval<_AP_N, true> {
+  typedef ap_slong Type;
+};
+
+template <int _AP_N>
+struct retval<_AP_N, false> {
+  typedef ap_ulong Type;
+};
+
+// at least 8 bit
+template <>
+struct retval<1, true> {
+  typedef signed char Type;
+};
+
+template <>
+struct retval<1, false> {
+  typedef unsigned char Type;
+};
+
+// at least 16 bit
+template <>
+struct retval<2, true> {
+  typedef short Type;
+};
+
+template <>
+struct retval<2, false> {
+  typedef unsigned short Type;
+};
+
+// at least 32 bit
+template <>
+struct retval<3, true> {
+  typedef long Type;
+};
+
+template <>
+struct retval<3, false> {
+  typedef unsigned long Type;
+};
+
+template <>
+struct retval<4, true> {
+  typedef long Type;
+};
+
+template <>
+struct retval<4, false> {
+  typedef unsigned long Type;
+};
+
+// trait for letting base class to return derived class.
+// Notice that derived class template is incomplete, and we cannot use
+// the member of the derived class.
+template <int _AP_W2, bool _AP_S2>
+struct _ap_int_factory;
+template <int _AP_W2>
+struct _ap_int_factory<_AP_W2,true> { typedef ap_int<_AP_W2> type; };
+template <int _AP_W2>
+struct _ap_int_factory<_AP_W2,false> { typedef ap_uint<_AP_W2> type; };
+
+template <int _AP_W, bool _AP_S>
+struct ap_int_base : public _AP_ROOT_TYPE<_AP_W, _AP_S> {
+ public:
+  typedef _AP_ROOT_TYPE<_AP_W, _AP_S> Base;
+
+  /* ap_int_base<_AP_W, _AP_S, true>
+   * typedef typename retval<(_AP_W + 7) / 8, _AP_S>::Type RetType;
+   *
+   * ap_int_base<_AP_W, _AP_S, false>
+   * typedef typename retval<8, _AP_S>::Type RetType;
+   */
+  typedef typename retval<AP_MAX((_AP_W + 7) / 8, 8), _AP_S>::Type RetType;
+
+  static const int width = _AP_W;
+
+  template <int _AP_W2, bool _AP_S2>
+  struct RType {
+    enum {
+      mult_w = _AP_W + _AP_W2,
+      mult_s = _AP_S || _AP_S2,
+      plus_w =
+          AP_MAX(_AP_W + (_AP_S2 && !_AP_S), _AP_W2 + (_AP_S && !_AP_S2)) + 1,
+      plus_s = _AP_S || _AP_S2,
+      minus_w =
+          AP_MAX(_AP_W + (_AP_S2 && !_AP_S), _AP_W2 + (_AP_S && !_AP_S2)) + 1,
+      minus_s = true,
+      div_w = _AP_W + _AP_S2,
+      div_s = _AP_S || _AP_S2,
+      mod_w = AP_MIN(_AP_W, _AP_W2 + (!_AP_S2 && _AP_S)),
+      mod_s = _AP_S,
+      logic_w = AP_MAX(_AP_W + (_AP_S2 && !_AP_S), _AP_W2 + (_AP_S && !_AP_S2)),
+      logic_s = _AP_S || _AP_S2
+    };
+
+
+    typedef ap_int_base<mult_w, mult_s> mult_base;
+    typedef ap_int_base<plus_w, plus_s> plus_base;
+    typedef ap_int_base<minus_w, minus_s> minus_base;
+    typedef ap_int_base<logic_w, logic_s> logic_base;
+    typedef ap_int_base<div_w, div_s> div_base;
+    typedef ap_int_base<mod_w, mod_s> mod_base;
+    typedef ap_int_base<_AP_W, _AP_S> arg1_base;
+
+    typedef typename _ap_int_factory<mult_w, mult_s>::type mult;
+    typedef typename _ap_int_factory<plus_w, plus_s>::type plus;
+    typedef typename _ap_int_factory<minus_w, minus_s>::type minus;
+    typedef typename _ap_int_factory<logic_w, logic_s>::type logic;
+    typedef typename _ap_int_factory<div_w, div_s>::type div;
+    typedef typename _ap_int_factory<mod_w, mod_s>::type mod;
+    typedef typename _ap_int_factory<_AP_W, _AP_S>::type arg1;
+    typedef bool reduce;
+  };
+
+  /* Constructors.
+   * ----------------------------------------------------------------
+   */
+  /// default ctor
+  INLINE ap_int_base() {
+    /*
+      #ifdef __SC_COMPATIBLE__
+      Base::V = 0;
+      #endif
+    */
+  }
+
+  /// copy ctor
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base(const ap_int_base<_AP_W2, _AP_S2>& op) {
+    Base::V = op.V;
+  }
+
+  /// volatile copy ctor
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base(const volatile ap_int_base<_AP_W2, _AP_S2>& op) {
+    Base::V = op.V;
+  }
+
+// XXX C++11 feature.
+// The explicit specifier specifies that a constructor or conversion function
+// (since C++11) doesn't allow implicit conversions or copy-initialization.
+//   ap_int_base<W,S> x = 1;
+//   ap_int_base<W,S> foo() { return 1; }
+// but allows
+//   ap_int_base<W,S> x(1);
+//   ap_int_base<W,S> y {1};
+
+/// from all c types.
+#define CTOR_FROM_INT(Type, Size, Signed) \
+  INLINE ap_int_base(const Type op) { Base::V = op; }
+
+  CTOR_FROM_INT(bool, 1, false)
+  CTOR_FROM_INT(char, 8, CHAR_IS_SIGNED)
+  CTOR_FROM_INT(signed char, 8, true)
+  CTOR_FROM_INT(unsigned char, 8, false)
+  CTOR_FROM_INT(short, _AP_SIZE_short, true)
+  CTOR_FROM_INT(unsigned short, _AP_SIZE_short, false)
+  CTOR_FROM_INT(int, _AP_SIZE_int, true)
+  CTOR_FROM_INT(unsigned int, _AP_SIZE_int, false)
+  CTOR_FROM_INT(long, _AP_SIZE_long, true)
+  CTOR_FROM_INT(unsigned long, _AP_SIZE_long, false)
+  CTOR_FROM_INT(ap_slong, _AP_SIZE_ap_slong, true)
+  CTOR_FROM_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+#undef CTOR_FROM_INT
+
+#if _AP_ENABLE_HALF_ == 1
+  /// ctor from half.
+  //  TODO optimize
+  INLINE ap_int_base(half op) {
+    ap_int_base<_AP_W, _AP_S> t((float)op);
+    Base::V = t.V;
+  }
+#endif
+
+  /// ctor from float.
+  INLINE ap_int_base(float op) {
+    const int BITS = FLOAT_MAN + FLOAT_EXP + 1;
+    ap_int_base<BITS, false> reg;
+    reg.V = floatToRawBits(op);
+    bool is_neg = _AP_ROOT_op_get_bit(reg.V, BITS - 1);
+
+    ap_int_base<FLOAT_EXP + 1, true> exp = 0;
+    exp.V = _AP_ROOT_op_get_range(reg.V, FLOAT_MAN, BITS - 2);
+    exp = exp - FLOAT_BIAS;
+
+    ap_int_base<FLOAT_MAN + 2, true> man;
+    man.V = _AP_ROOT_op_get_range(reg.V, 0, FLOAT_MAN - 1);
+    // check for NaN
+    _AP_WARNING(exp == ((unsigned char)(FLOAT_BIAS + 1)) && man.V != 0,
+                "assign NaN to ap integer value");
+    // set leading 1.
+    man.V = _AP_ROOT_op_set_bit(man.V, FLOAT_MAN, 1);
+    //if (is_neg) man = -man;
+
+    if ((reg.V & 0x7ffffffful) == 0) {
+      Base::V = 0;
+    } else {
+      int sh_amt = FLOAT_MAN - exp.V;
+      if (sh_amt == 0) {
+        Base::V = man.V;
+      } else if (sh_amt > 0) {
+        if (sh_amt < FLOAT_MAN + 2) {
+          Base::V = man.V >> sh_amt;
+        } else {
+          if (is_neg)
+            Base::V = -1;
+          else
+            Base::V = 0;
+        }
+      } else {
+        sh_amt = -sh_amt;
+        if (sh_amt < _AP_W) {
+          Base::V = man.V;
+          Base::V <<= sh_amt;
+        } else {
+          Base::V = 0;
+        }
+      }
+    }
+    if (is_neg) *this = -(*this);
+  }
+
+  /// ctor from double.
+  INLINE ap_int_base(double op) {
+    const int BITS = DOUBLE_MAN + DOUBLE_EXP + 1;
+    ap_int_base<BITS, false> reg;
+    reg.V = doubleToRawBits(op);
+    bool is_neg = _AP_ROOT_op_get_bit(reg.V, BITS - 1);
+
+    ap_int_base<DOUBLE_EXP + 1, true> exp = 0;
+    exp.V = _AP_ROOT_op_get_range(reg.V, DOUBLE_MAN, BITS - 2);
+    exp = exp - DOUBLE_BIAS;
+
+    ap_int_base<DOUBLE_MAN + 2, true> man;
+    man.V = _AP_ROOT_op_get_range(reg.V, 0, DOUBLE_MAN - 1);
+    // check for NaN
+    _AP_WARNING(exp == ((unsigned char)(DOUBLE_BIAS + 1)) && man.V != 0,
+                "assign NaN to ap integer value");
+    // set leading 1.
+    man.V = _AP_ROOT_op_set_bit(man.V, DOUBLE_MAN, 1);
+    //if (is_neg) man = -man;
+
+    if ((reg.V & 0x7fffffffffffffffull) == 0) {
+      Base::V = 0;
+    } else {
+      int sh_amt = DOUBLE_MAN - exp.V;
+      if (sh_amt == 0) {
+        Base::V = man.V;
+      } else if (sh_amt > 0) {
+        if (sh_amt < DOUBLE_MAN + 2) {
+          Base::V = man.V >> sh_amt;
+        } else {
+          if (is_neg)
+            Base::V = -1;
+          else
+            Base::V = 0;
+        }
+      } else {
+        sh_amt = -sh_amt;
+        if (sh_amt < _AP_W) {
+          Base::V = man.V;
+          Base::V <<= sh_amt;
+        } else {
+          Base::V = 0;
+        }
+      }
+    }
+    if (is_neg) *this = -(*this);
+  }
+
+  /// from higer rank type.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_int_base(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    Base::V = op.to_ap_int_base().V;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base(const ap_range_ref<_AP_W2, _AP_S2>& ref) {
+    Base::V = (ref.get()).V;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base(const ap_bit_ref<_AP_W2, _AP_S2>& ref) {
+    Base::V = ref.operator bool();
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_int_base(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& ref) {
+    const ap_int_base<ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>::_AP_WR,
+                      false>
+        tmp = ref.get();
+    Base::V = tmp.V;
+  }
+
+  /* radix has default value in set */
+
+#ifndef __SYNTHESIS__
+  INLINE ap_int_base(const char* s, signed char rd = 0) {
+    if (rd == 0)
+      rd = guess_radix(s);
+    unsigned int length = strlen(s);
+    Base::V.fromString(s, length, rd);
+  }
+#else
+  // XXX __builtin_bit_from_string(...) requires const C string and radix.
+  INLINE ap_int_base(const char* s) {
+    typeof(Base::V) t;
+    _ssdm_string2bits((void*)(&t), (const char*)(s), 10, _AP_W, _AP_S,
+                      AP_TRN, AP_WRAP, 0, _AP_C99);
+    Base::V = t;
+  }
+  INLINE ap_int_base(const char* s, signed char rd) {
+    typeof(Base::V) t;
+    _ssdm_string2bits((void*)(&t), (const char*)(s), rd, _AP_W, _AP_S,
+                      AP_TRN, AP_WRAP, 0, _AP_C99);
+    Base::V = t;
+  }
+#endif
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_int_base(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    Base::V = (val.get()).V;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_int_base(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    Base::V = val.operator bool();
+  }
+
+  INLINE ap_int_base read() volatile {
+    /*AP_DEBUG(printf("call read %d\n", Base::V););*/
+    ap_int_base ret;
+    ret.V = Base::V;
+    return ret;
+  }
+
+  INLINE void write(const ap_int_base<_AP_W, _AP_S>& op2) volatile {
+    /*AP_DEBUG(printf("call write %d\n", op2.V););*/
+    Base::V = op2.V;
+  }
+
+  /* Another form of "write".*/
+  template <int _AP_W2, bool _AP_S2>
+  INLINE void operator=(
+      const volatile ap_int_base<_AP_W2, _AP_S2>& op2) volatile {
+    Base::V = op2.V;
+  }
+
+  INLINE void operator=(
+      const volatile ap_int_base<_AP_W, _AP_S>& op2) volatile {
+    Base::V = op2.V;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE void operator=(const ap_int_base<_AP_W2, _AP_S2>& op2) volatile {
+    Base::V = op2.V;
+  }
+
+  INLINE void operator=(const ap_int_base<_AP_W, _AP_S>& op2) volatile {
+    Base::V = op2.V;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base& operator=(
+      const volatile ap_int_base<_AP_W2, _AP_S2>& op2) {
+    Base::V = op2.V;
+    return *this;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base& operator=(const ap_int_base<_AP_W2, _AP_S2>& op2) {
+    Base::V = op2.V;
+    return *this;
+  }
+
+  INLINE ap_int_base& operator=(const volatile ap_int_base<_AP_W, _AP_S>& op2) {
+    Base::V = op2.V;
+    return *this;
+  }
+
+  INLINE ap_int_base& operator=(const ap_int_base<_AP_W, _AP_S>& op2) {
+    Base::V = op2.V;
+    return *this;
+  }
+
+
+#define ASSIGN_OP_FROM_INT(Type, Size, Signed) \
+  INLINE ap_int_base& operator=(Type op) {     \
+    Base::V = op;                              \
+    return *this;                              \
+  }
+
+  ASSIGN_OP_FROM_INT(bool, 1, false)
+  ASSIGN_OP_FROM_INT(char, 8, CHAR_IS_SIGNED)
+  ASSIGN_OP_FROM_INT(signed char, 8, true)
+  ASSIGN_OP_FROM_INT(unsigned char, 8, false)
+  ASSIGN_OP_FROM_INT(short, _AP_SIZE_short, true)
+  ASSIGN_OP_FROM_INT(unsigned short, _AP_SIZE_short, false)
+  ASSIGN_OP_FROM_INT(int, _AP_SIZE_int, true)
+  ASSIGN_OP_FROM_INT(unsigned int, _AP_SIZE_int, false)
+  ASSIGN_OP_FROM_INT(long, _AP_SIZE_long, true)
+  ASSIGN_OP_FROM_INT(unsigned long, _AP_SIZE_long, false)
+  ASSIGN_OP_FROM_INT(ap_slong, _AP_SIZE_ap_slong, true)
+  ASSIGN_OP_FROM_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef ASSIGN_OP_FROM_INT
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base& operator=(const ap_bit_ref<_AP_W2, _AP_S2>& op2) {
+    Base::V = (bool)op2;
+    return *this;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base& operator=(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    Base::V = (ap_int_base<_AP_W2, false>(op2)).V;
+    return *this;
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_int_base& operator=(
+      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& op2) {
+    Base::V = op2.get().V;
+    return *this;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_int_base& operator=(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    Base::V = op.to_ap_int_base().V;
+    return *this;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_int_base& operator=(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    Base::V = (bool)op;
+    return *this;
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_int_base& operator=(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& op) {
+    Base::V = ((const ap_int_base<_AP_W2, false>)(op)).V;
+    return *this;
+  }
+
+  // FIXME: UG902 has clearly required user to use to_int() to convert to built-in
+  // types, but this implicit conversion is relied on in hls_cordic.h and hls_rsr.h.
+  // For example:
+  //     int d_exp = fps_x.exp - fps_y.exp;
+  INLINE operator RetType() const { return (RetType)(Base::V); }
+
+  /* Explicit conversions to C types.
+   * ----------------------------------------------------------------
+   */
+  INLINE bool to_bool() const { return (bool)(Base::V); }
+  INLINE char to_char() const { return (char)(Base::V); }
+  INLINE signed char to_schar() const { return (signed char)(Base::V); }
+  INLINE unsigned char to_uchar() const { return (unsigned char)(Base::V); }
+  INLINE short to_short() const { return (short)(Base::V); }
+  INLINE unsigned short to_ushort() const { return (unsigned short)(Base::V); }
+  INLINE int to_int() const { return (int)(Base::V); }
+  INLINE unsigned to_uint() const { return (unsigned)(Base::V); }
+  INLINE long to_long() const { return (long)(Base::V); }
+  INLINE unsigned long to_ulong() const { return (unsigned long)(Base::V); }
+  INLINE ap_slong to_int64() const { return (ap_slong)(Base::V); }
+  INLINE ap_ulong to_uint64() const { return (ap_ulong)(Base::V); }
+  INLINE float to_float() const { return (float)(Base::V); }
+  INLINE double to_double() const { return (double)(Base::V); }
+
+  // TODO decide if user-defined conversion should be provided.
+#if 0
+  INLINE operator char() const { return (char)(Base::V); }
+  INLINE operator signed char() const { return (signed char)(Base::V); }
+  INLINE operator unsigned char() const { return (unsigned char)(Base::V); }
+  INLINE operator short() const { return (short)(Base::V); }
+  INLINE operator unsigned short() const { return (unsigned short)(Base::V); }
+  INLINE operator int() const { return (int)(Base::V); }
+  INLINE operator unsigned int () const { return (unsigned)(Base::V); }
+  INLINE operator long () const { return (long)(Base::V); }
+  INLINE operator unsigned long () const { return (unsigned long)(Base::V); }
+  INLINE operator ap_slong () { return (ap_slong)(Base::V); }
+  INLINE operator ap_ulong () { return (ap_ulong)(Base::V); }
+#endif
+
+  /* Helper methods.
+     ----------------------------------------------------------------
+  */
+  /* we cannot call a non-volatile function on a volatile instance.
+   * but calling a volatile function is ok.
+   * XXX deleted non-volatile version.
+   */
+  INLINE int length() const volatile { return _AP_W; }
+
+  /*Return true if the value of ap_int_base instance is zero*/
+  INLINE bool iszero() const { return Base::V == 0; }
+
+  /*Return true if the value of ap_int_base instance is zero*/
+  INLINE bool is_zero() const { return Base::V == 0; }
+
+  /* x < 0 */
+  INLINE bool sign() const {
+    if (_AP_S &&
+        _AP_ROOT_op_get_bit(Base::V, _AP_W - 1))
+      return true;
+    else
+      return false;
+  }
+
+  /* x[i] = 0 */
+  INLINE void clear(int i) {
+    AP_ASSERT(i >= 0 && i < _AP_W, "position out of range");
+    Base::V = _AP_ROOT_op_set_bit(Base::V, i, 0);
+  }
+
+  /* x[i] = !x[i]*/
+  INLINE void invert(int i) {
+    AP_ASSERT(i >= 0 && i < _AP_W, "position out of range");
+    bool val = _AP_ROOT_op_get_bit(Base::V, i);
+    if (val)
+      Base::V = _AP_ROOT_op_set_bit(Base::V, i, 0);
+    else
+      Base::V = _AP_ROOT_op_set_bit(Base::V, i, 1);
+  }
+
+  INLINE bool test(int i) const {
+    AP_ASSERT(i >= 0 && i < _AP_W, "position out of range");
+    return _AP_ROOT_op_get_bit(Base::V, i);
+  }
+
+  // Get self. For ap_concat_ref expansion.
+  INLINE ap_int_base& get() { return *this; }
+
+  // Set the ith bit into 1
+  INLINE void set(int i) {
+    AP_ASSERT(i >= 0 && i < _AP_W, "position out of range");
+    Base::V = _AP_ROOT_op_set_bit(Base::V, i, 1);
+  }
+
+  // Set the ith bit into v
+  INLINE void set(int i, bool v) {
+    AP_ASSERT(i >= 0 && i < _AP_W, "position out of range");
+    Base::V = _AP_ROOT_op_set_bit(Base::V, i, v);
+  }
+
+  // This is used for sc_lv and sc_bv, which is implemented by sc_uint
+  // Rotate an ap_int_base object n places to the left
+  INLINE ap_int_base& lrotate(int n) {
+    AP_ASSERT(n >= 0 && n < _AP_W, "shift value out of range");
+    // TODO unify this.
+#ifdef __SYNTHESIS__
+    typeof(Base::V) l_p = Base::V << n;
+    typeof(Base::V) r_p = Base::V >> (_AP_W - n);
+    Base::V = l_p | r_p;
+#else
+    Base::V.lrotate(n);
+#endif
+    return *this;
+  }
+
+  // This is used for sc_lv and sc_bv, which is implemented by sc_uint
+  // Rotate an ap_int_base object n places to the right
+  INLINE ap_int_base& rrotate(int n) {
+    AP_ASSERT(n >= 0 && n < _AP_W, "shift value out of range");
+    // TODO unify this.
+#ifdef __SYNTHESIS__
+    typeof(Base::V) l_p = Base::V << (_AP_W - n);
+    typeof(Base::V) r_p = Base::V >> n;
+    Base::V = l_p | r_p;
+#else
+    Base::V.rrotate(n);
+#endif
+    return *this;
+  }
+
+  // Reverse the contents of ap_int_base instance.
+  // I.e. LSB becomes MSB and vise versa.
+  INLINE ap_int_base& reverse() {
+    Base::V = _AP_ROOT_op_get_range(Base::V, _AP_W - 1, 0);
+    return *this;
+  }
+
+  // Set the ith bit into v
+  INLINE void set_bit(int i, bool v) {
+    Base::V = _AP_ROOT_op_set_bit(Base::V, i, v);
+  }
+
+  // Get the value of ith bit
+  INLINE bool get_bit(int i) const {
+    return (bool)_AP_ROOT_op_get_bit(Base::V, i);
+  }
+
+  // complements every bit
+  INLINE void b_not() { Base::V = ~Base::V; }
+
+#define OP_ASSIGN_AP(Sym)                                                    \
+  template <int _AP_W2, bool _AP_S2>                                         \
+  INLINE ap_int_base& operator Sym(const ap_int_base<_AP_W2, _AP_S2>& op2) { \
+    Base::V Sym op2.V;                                                       \
+    return *this;                                                            \
+  }
+
+  /* Arithmetic assign.
+   * ----------------------------------------------------------------
+   */
+  OP_ASSIGN_AP(*=)
+  OP_ASSIGN_AP(+=)
+  OP_ASSIGN_AP(-=)
+  OP_ASSIGN_AP(/=)
+  OP_ASSIGN_AP(%=)
+#undef OP_ASSIGN_AP
+
+  /* Bitwise assign: and, or, xor.
+   * ----------------------------------------------------------------
+   */
+#define OP_ASSIGN_AP_CHK(Sym)                                                \
+  template <int _AP_W2, bool _AP_S2>                                         \
+  INLINE ap_int_base& operator Sym(const ap_int_base<_AP_W2, _AP_S2>& op2) { \
+    _AP_WARNING((_AP_W != _AP_W2),                                           \
+                "Bitsize mismatch for ap_[u]int" #Sym "ap_[u]int.");         \
+    Base::V Sym op2.V;                                                       \
+    return *this;                                                            \
+  }
+  OP_ASSIGN_AP_CHK(&=)
+  OP_ASSIGN_AP_CHK(|=)
+  OP_ASSIGN_AP_CHK(^=)
+#undef OP_ASSIGN_AP_CHK
+
+  /* Prefix increment, decrement.
+   * ----------------------------------------------------------------
+   */
+  INLINE ap_int_base& operator++() {
+    operator+=((ap_int_base<1, false>)1);
+    return *this;
+  }
+  INLINE ap_int_base& operator--() {
+    operator-=((ap_int_base<1, false>)1);
+    return *this;
+  }
+
+  /* Postfix increment, decrement
+   * ----------------------------------------------------------------
+   */
+  INLINE const typename RType<_AP_W,_AP_S>::arg1 operator++(int) {
+    ap_int_base t = *this;
+    operator+=((ap_int_base<1, false>)1);
+    return t;
+  }
+  INLINE const typename RType<_AP_W,_AP_S>::arg1 operator--(int) {
+    ap_int_base t = *this;
+    operator-=((ap_int_base<1, false>)1);
+    return t;
+  }
+
+  /* Unary arithmetic.
+   * ----------------------------------------------------------------
+   */
+  INLINE typename RType<_AP_W,_AP_S>::arg1 operator+() const { return *this; }
+
+  // TODO used to be W>64 only... need check.
+  INLINE typename RType<1, false>::minus operator-() const {
+    return ap_int_base<1, false>(0) - *this;
+  }
+
+  /* Not (!)
+   * ----------------------------------------------------------------
+   */
+  INLINE bool operator!() const { return Base::V == 0; }
+
+  /* Bitwise (arithmetic) unary: complement
+     ----------------------------------------------------------------
+  */
+  // XXX different from Mentor's ac_int!
+  INLINE typename RType<_AP_W,_AP_S>::arg1 operator~() const {
+    ap_int_base<_AP_W, _AP_S> r;
+    r.V = ~Base::V;
+    return r;
+  }
+
+  /* Shift (result constrained by left operand).
+   * ----------------------------------------------------------------
+   */
+  template <int _AP_W2>
+  INLINE typename RType<_AP_W,_AP_S>::arg1 operator<<(const ap_int_base<_AP_W2, true>& op2) const {
+    bool isNeg = _AP_ROOT_op_get_bit(op2.V, _AP_W2 - 1);
+    ap_int_base<_AP_W2, false> sh = op2;
+    if (isNeg) {
+      sh = -op2;
+      return operator>>(sh);
+    } else
+      return operator<<(sh);
+  }
+
+  template <int _AP_W2>
+  INLINE typename RType<_AP_W,_AP_S>::arg1 operator<<(const ap_int_base<_AP_W2, false>& op2) const {
+    ap_int_base r;
+    r.V = Base::V << op2.to_uint();
+    return r;
+  }
+
+  template <int _AP_W2>
+  INLINE typename RType<_AP_W,_AP_S>::arg1 operator>>(const ap_int_base<_AP_W2, true>& op2) const {
+    bool isNeg = _AP_ROOT_op_get_bit(op2.V, _AP_W2 - 1);
+    ap_int_base<_AP_W2, false> sh = op2;
+    if (isNeg) {
+      sh = -op2;
+      return operator<<(sh);
+    }
+    return operator>>(sh);
+  }
+
+  template <int _AP_W2>
+  INLINE typename RType<_AP_W,_AP_S>::arg1 operator>>(const ap_int_base<_AP_W2, false>& op2) const {
+    ap_int_base r;
+    r.V = Base::V >> op2.to_uint();
+    return r;
+  }
+
+  // FIXME we standalone operator>> for ap_int_base and ap_range_ref.
+#if 0
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base operator<<(const ap_range_ref<_AP_W2, _AP_S2>& op2) const {
+    return *this << (op2.operator ap_int_base<_AP_W2, false>());
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base operator>>(const ap_range_ref<_AP_W2, _AP_S2>& op2) const {
+    return *this >> (op2.operator ap_int_base<_AP_W2, false>());
+  }
+#endif
+
+  /* Shift assign
+   * ----------------------------------------------------------------
+   */
+  template <int _AP_W2>
+  INLINE ap_int_base& operator<<=(const ap_int_base<_AP_W2, true>& op2) {
+    bool isNeg = _AP_ROOT_op_get_bit(op2.V, _AP_W2 - 1);
+    ap_int_base<_AP_W2, false> sh = op2;
+    if (isNeg) {
+      sh = -op2;
+      return operator>>=(sh);
+    } else
+      return operator<<=(sh);
+  }
+
+  template <int _AP_W2>
+  INLINE ap_int_base& operator<<=(const ap_int_base<_AP_W2, false>& op2) {
+    Base::V <<= op2.to_uint();
+    return *this;
+  }
+
+  template <int _AP_W2>
+  INLINE ap_int_base& operator>>=(const ap_int_base<_AP_W2, true>& op2) {
+    bool isNeg = _AP_ROOT_op_get_bit(op2.V, _AP_W2 - 1);
+    ap_int_base<_AP_W2, false> sh = op2;
+    if (isNeg) {
+      sh = -op2;
+      return operator<<=(sh);
+    }
+    return operator>>=(sh);
+  }
+
+  template <int _AP_W2>
+  INLINE ap_int_base& operator>>=(const ap_int_base<_AP_W2, false>& op2) {
+    Base::V >>= op2.to_uint();
+    return *this;
+  }
+
+  // FIXME we standalone operator>> for ap_int_base and ap_range_ref.
+#if 0
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base& operator<<=(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    return *this <<= (op2.operator ap_int_base<_AP_W2, false>());
+  }
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_int_base& operator>>=(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    return *this >>= (op2.operator ap_int_base<_AP_W2, false>());
+  }
+#endif
+
+  /* Equality and Relational.
+   * ----------------------------------------------------------------
+   */
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator==(const ap_int_base<_AP_W2, _AP_S2>& op2) const {
+    return Base::V == op2.V;
+  }
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator!=(const ap_int_base<_AP_W2, _AP_S2>& op2) const {
+    return !(Base::V == op2.V);
+  }
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<(const ap_int_base<_AP_W2, _AP_S2>& op2) const {
+    return Base::V < op2.V;
+  }
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>=(const ap_int_base<_AP_W2, _AP_S2>& op2) const {
+    return Base::V >= op2.V;
+  }
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>(const ap_int_base<_AP_W2, _AP_S2>& op2) const {
+    return Base::V > op2.V;
+  }
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<=(const ap_int_base<_AP_W2, _AP_S2>& op2) const {
+    return Base::V <= op2.V;
+  }
+
+  /* Bit and Part Select
+   * ----------------------------------------------------------------
+   */
+  INLINE ap_range_ref<_AP_W, _AP_S> range(int Hi, int Lo) {
+    _AP_ERROR(Hi >= _AP_W, "Hi(%d)out of bound(%d) in range()", Hi, _AP_W);
+    _AP_ERROR(Lo >= _AP_W, "Lo(%d)out of bound(%d) in range()", Lo, _AP_W);
+    return ap_range_ref<_AP_W, _AP_S>(this, Hi, Lo);
+  }
+
+  // This is a must to strip constness to produce reference type.
+  INLINE ap_range_ref<_AP_W, _AP_S> range(int Hi, int Lo) const {
+    _AP_ERROR(Hi >= _AP_W, "Hi(%d)out of bound(%d) in range()", Hi, _AP_W);
+    _AP_ERROR(Lo >= _AP_W, "Lo(%d)out of bound(%d) in range()", Lo, _AP_W);
+    return ap_range_ref<_AP_W, _AP_S>(const_cast<ap_int_base*>(this), Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE ap_range_ref<_AP_W, _AP_S> range(
+      const ap_int_base<_AP_W2, _AP_S2>& HiIdx,
+      const ap_int_base<_AP_W3, _AP_S3>& LoIdx) {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return this->range(Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE ap_range_ref<_AP_W, _AP_S> range(
+      const ap_int_base<_AP_W2, _AP_S2>& HiIdx,
+      const ap_int_base<_AP_W3, _AP_S3>& LoIdx) const {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return this->range(Hi, Lo);
+  }
+
+  INLINE ap_range_ref<_AP_W, _AP_S> range() {
+    return this->range(_AP_W - 1, 0);
+  }
+
+  INLINE ap_range_ref<_AP_W, _AP_S> range() const {
+    return this->range(_AP_W - 1, 0);
+  }
+
+  INLINE ap_range_ref<_AP_W, _AP_S> operator()(int Hi, int Lo) {
+    return this->range(Hi, Lo);
+  }
+
+  INLINE ap_range_ref<_AP_W, _AP_S> operator()(int Hi, int Lo) const {
+    return this->range(Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE ap_range_ref<_AP_W, _AP_S> operator()(
+      const ap_int_base<_AP_W2, _AP_S2>& HiIdx,
+      const ap_int_base<_AP_W3, _AP_S3>& LoIdx) {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return this->range(Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE ap_range_ref<_AP_W, _AP_S> operator()(
+      const ap_int_base<_AP_W2, _AP_S2>& HiIdx,
+      const ap_int_base<_AP_W3, _AP_S3>& LoIdx) const {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return this->range(Hi, Lo);
+  }
+
+#if 0
+  template<int Hi, int Lo>
+  INLINE ap_int_base<Hi-Lo+1, false> slice() const {
+    AP_ASSERT(Hi >= Lo && Hi < _AP_W && Lo < _AP_W, "Out of bounds in slice()");
+    ap_int_base<Hi-Lo+1, false> tmp ;
+    tmp.V = _AP_ROOT_op_get_range(Base::V, Lo, Hi);
+    return tmp;
+  }
+
+  INLINE ap_bit_ref<_AP_W,_AP_S> operator [] ( unsigned int uindex) {
+    AP_ASSERT(uindex < _AP_W, "Attempting to read bit beyond MSB");
+    ap_bit_ref<_AP_W,_AP_S> bvh( this, uindex );
+    return bvh;
+  }
+#endif
+
+  INLINE ap_bit_ref<_AP_W, _AP_S> operator[](int index) {
+    AP_ASSERT(index >= 0, "Attempting to read bit with negative index");
+    AP_ASSERT(index < _AP_W, "Attempting to read bit beyond MSB");
+    ap_bit_ref<_AP_W, _AP_S> bvh(this, index);
+    return bvh;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_bit_ref<_AP_W, _AP_S> operator[](
+      const ap_int_base<_AP_W2, _AP_S2>& index) {
+    AP_ASSERT(index >= 0, "Attempting to read bit with negative index");
+    AP_ASSERT(index < _AP_W, "Attempting to read bit beyond MSB");
+    ap_bit_ref<_AP_W, _AP_S> bvh(this, index.to_int());
+    return bvh;
+  }
+
+  INLINE bool operator[](int index) const {
+    AP_ASSERT(index >= 0, "Attempting to read bit with negative index");
+    AP_ASSERT(index < _AP_W, "Attempting to read bit beyond MSB");
+    ap_bit_ref<_AP_W, _AP_S> br(this, index);
+    return br.to_bool();
+  }
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator[](const ap_int_base<_AP_W2, _AP_S2>& index) const {
+    AP_ASSERT(index < _AP_W, "Attempting to read bit beyond MSB");
+    ap_bit_ref<_AP_W, _AP_S> br(this, index.to_int());
+    return br.to_bool();
+  }
+
+  INLINE ap_bit_ref<_AP_W, _AP_S> bit(int index) {
+    AP_ASSERT(index >= 0, "Attempting to read bit with negative index");
+    AP_ASSERT(index < _AP_W, "Attempting to read bit beyond MSB");
+    ap_bit_ref<_AP_W, _AP_S> bvh(this, index);
+    return bvh;
+  }
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_bit_ref<_AP_W, _AP_S> bit(
+      const ap_int_base<_AP_W2, _AP_S2>& index) {
+    AP_ASSERT(index >= 0, "Attempting to read bit with negative index");
+    AP_ASSERT(index < _AP_W, "Attempting to read bit beyond MSB");
+    ap_bit_ref<_AP_W, _AP_S> bvh(this, index.to_int());
+    return bvh;
+  }
+
+  INLINE bool bit(int index) const {
+    AP_ASSERT(index >= 0, "Attempting to read bit with negative index");
+    AP_ASSERT(index < _AP_W, "Attempting to read bit beyond MSB");
+    ap_bit_ref<_AP_W, _AP_S> br(this, index);
+    return br.to_bool();
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool bit(const ap_int_base<_AP_W2, _AP_S2>& index) const {
+    return bit(index.to_int());
+  }
+
+#if 0
+  template<typename _AP_T>
+  INLINE bool operator[](_AP_T index) const {
+    AP_ASSERT(index < _AP_W, "Attempting to read bit beyond MSB");
+    ap_bit_ref<_AP_W,_AP_S> br = operator[](index);
+    return br.to_bool();
+  }
+#endif
+
+  // Count the number of zeros from the most significant bit
+  // to the first one bit.
+  INLINE int countLeadingZeros() {
+#ifdef __SYNTHESIS__
+    if (_AP_W <= 32) {
+      ap_int_base<32, false> t(-1UL), x;
+      x.V = _AP_ROOT_op_get_range(this->V, _AP_W - 1, 0); // reverse
+      t.V = _AP_ROOT_op_set_range(t.V, 0, _AP_W - 1, x.V);
+      return __builtin_ctz(t.V); // count trailing zeros.
+    } else if (_AP_W <= 64) {
+      ap_int_base<64, false> t(-1ULL);
+      ap_int_base<64, false> x;
+      x.V = _AP_ROOT_op_get_range(this->V, _AP_W - 1, 0); // reverse
+      t.V = _AP_ROOT_op_set_range(t.V, 0, _AP_W - 1, x.V);
+      return __builtin_ctzll(t.V); // count trailing zeros.
+    } else {
+      enum { __N = (_AP_W + 63) / 64 };
+      int NZeros = 0;
+      int i = 0;
+      bool hitNonZero = false;
+      for (i = 0; i < __N - 1; ++i) {
+        ap_int_base<64, false> t;
+        t.V = _AP_ROOT_op_get_range(this->V, _AP_W - i * 64 - 64, _AP_W - i * 64 - 1);
+        NZeros += hitNonZero ? 0 : __builtin_clzll(t.V); // count leading zeros.
+        hitNonZero |= (t.V != 0);
+      }
+      if (!hitNonZero) {
+        ap_int_base<64, false> t(-1ULL);
+        enum { REST = (_AP_W - 1) % 64 };
+        ap_int_base<64, false> x;
+        x.V = _AP_ROOT_op_get_range(this->V, 0, REST);
+        t.V = _AP_ROOT_op_set_range(t.V, 63 - REST, 63, x.V);
+        NZeros += __builtin_clzll(t.V);
+      }
+      return NZeros;
+    }
+#else
+    return (Base::V).countLeadingZeros();
+#endif
+  } // countLeadingZeros
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  concat(const ap_int_base<_AP_W2, _AP_S2>& a2) const {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(
+        const_cast<ap_int_base<_AP_W, _AP_S>&>(*this),
+        const_cast<ap_int_base<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  concat(ap_int_base<_AP_W2, _AP_S2>& a2) {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(*this, a2);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_int_base, _AP_W2, ap_range_ref<_AP_W2, _AP_S2> >
+      operator,(const ap_range_ref<_AP_W2, _AP_S2> &a2) const {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2,
+                         ap_range_ref<_AP_W2, _AP_S2> >(
+        const_cast<ap_int_base<_AP_W, _AP_S>&>(*this),
+        const_cast<ap_range_ref<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_int_base, _AP_W2, ap_range_ref<_AP_W2, _AP_S2> >
+      operator,(ap_range_ref<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2,
+                         ap_range_ref<_AP_W2, _AP_S2> >(*this, a2);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  operator,(const ap_int_base<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_int_base<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  operator,(ap_int_base<_AP_W2, _AP_S2> &a2) const {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(
+        const_cast<ap_int_base<_AP_W, _AP_S>&>(*this), a2);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  operator,(const ap_int_base<_AP_W2, _AP_S2> &a2) const {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(
+        const_cast<ap_int_base<_AP_W, _AP_S>&>(*this),
+        const_cast<ap_int_base<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  operator,(ap_int_base<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(*this, a2);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, 1, ap_bit_ref<_AP_W2, _AP_S2> >
+  operator,(const ap_bit_ref<_AP_W2, _AP_S2> &a2) const {
+    return ap_concat_ref<_AP_W, ap_int_base, 1, ap_bit_ref<_AP_W2, _AP_S2> >(
+        const_cast<ap_int_base<_AP_W, _AP_S>&>(*this),
+        const_cast<ap_bit_ref<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, 1, ap_bit_ref<_AP_W2, _AP_S2> >
+  operator,(ap_bit_ref<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_int_base, 1, ap_bit_ref<_AP_W2, _AP_S2> >(
+        *this, a2);
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, _AP_W2 + _AP_W3,
+                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+  operator,(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2 + _AP_W3,
+                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(
+        const_cast<ap_int_base<_AP_W, _AP_S>&>(*this),
+        const_cast<ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>&>(a2));
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_concat_ref<_AP_W, ap_int_base, _AP_W2 + _AP_W3,
+                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+  operator,(ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) {
+    return ap_concat_ref<_AP_W, ap_int_base, _AP_W2 + _AP_W3,
+                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(*this,
+                                                                         a2);
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_concat_ref<
+      _AP_W, ap_int_base, _AP_W2,
+      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+  operator,(const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>
+                &a2) const {
+    return ap_concat_ref<
+        _AP_W, ap_int_base, _AP_W2,
+        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+        const_cast<ap_int_base<_AP_W, _AP_S>&>(*this),
+        const_cast<
+            af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(a2));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_concat_ref<
+      _AP_W, ap_int_base, _AP_W2,
+      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+  operator,(af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &a2) {
+    return ap_concat_ref<
+        _AP_W, ap_int_base, _AP_W2,
+        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(*this,
+                                                                       a2);
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_int_base, 1,
+                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+      operator,(const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>
+                    &a2) const {
+    return ap_concat_ref<
+        _AP_W, ap_int_base, 1,
+        af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+        const_cast<ap_int_base<_AP_W, _AP_S>&>(*this),
+        const_cast<af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(
+            a2));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_int_base, 1,
+                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+      operator,(
+          af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &a2) {
+    return ap_concat_ref<
+        _AP_W, ap_int_base, 1,
+        af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(*this, a2);
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_int_base<AP_MAX(_AP_W2 + _AP_W3, _AP_W), _AP_S> operator&(
+      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& a2) {
+    return *this & a2.get();
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_int_base<AP_MAX(_AP_W2 + _AP_W3, _AP_W), _AP_S> operator|(
+      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& a2) {
+    return *this | a2.get();
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_int_base<AP_MAX(_AP_W2 + _AP_W3, _AP_W), _AP_S> operator^(
+      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& a2) {
+    return *this ^ a2.get();
+  }
+
+  template <int _AP_W3>
+  INLINE void set(const ap_int_base<_AP_W3, false>& val) {
+    Base::V = val.V;
+  }
+
+  /* Reduce operations.
+   * ----------------------------------------------------------------
+   */
+  // XXX non-const version deleted.
+  INLINE bool and_reduce() const { return _AP_ROOT_op_reduce(and, Base::V); }
+  INLINE bool nand_reduce() const { return _AP_ROOT_op_reduce(nand, Base::V); }
+  INLINE bool or_reduce() const { return _AP_ROOT_op_reduce(or, Base::V); }
+  INLINE bool nor_reduce() const { return !(_AP_ROOT_op_reduce(or, Base::V)); }
+  INLINE bool xor_reduce() const { return _AP_ROOT_op_reduce (xor, Base::V); }
+  INLINE bool xnor_reduce() const {
+    return !(_AP_ROOT_op_reduce (xor, Base::V));
+  }
+
+  /* Output as a string.
+   * ----------------------------------------------------------------
+   */
+#ifndef __SYNTHESIS__
+  std::string to_string(signed char rd = 2, bool sign = _AP_S) const {
+    // XXX in autosim/autowrap.tcl "(${name}).to_string(2).c_str()" is used to
+    // initialize sc_lv, which seems incapable of handling format "-0b".
+    if (rd == 2) sign = false;
+    return (Base::V).to_string(rd, sign);
+  }
+#else
+  INLINE char* to_string(signed char rd = 2, bool sign = _AP_S) const {
+    return 0;
+  }
+#endif
+}; // struct ap_int_base
+
+// XXX apcc cannot handle global std::ios_base::Init() brought in by <iostream>
+#ifndef AP_AUTOCC
+#ifndef __SYNTHESIS__
+template <int _AP_W, bool _AP_S>
+INLINE std::ostream& operator<<(std::ostream& os,
+                                const ap_int_base<_AP_W, _AP_S>& x) {
+  std::ios_base::fmtflags ff = std::cout.flags();
+  if (ff & std::cout.hex) {
+    os << x.to_string(16); // don't print sign
+  } else if (ff & std::cout.oct) {
+    os << x.to_string(8); // don't print sign
+  } else {
+    os << x.to_string(10);
+  }
+  return os;
+}
+#endif // ifndef __SYNTHESIS__
+
+#ifndef __SYNTHESIS__
+template <int _AP_W, bool _AP_S>
+INLINE std::istream& operator>>(std::istream& in,
+                                ap_int_base<_AP_W, _AP_S>& op) {
+  std::string str;
+  in >> str;
+  const std::ios_base::fmtflags basefield = in.flags() & std::ios_base::basefield;
+  unsigned radix = (basefield == std::ios_base::dec) ? 0 : (
+                     (basefield == std::ios_base::oct) ? 8 : (
+                       (basefield == std::ios_base::hex) ? 16 : 0));
+  op = ap_int_base<_AP_W, _AP_S>(str.c_str(), radix);
+  return in;
+}
+#endif // ifndef __SYNTHESIS__
+#endif // ifndef AP_AUTOCC
+
+/* Operators with another ap_int_base.
+ * ----------------------------------------------------------------
+ */
+#define OP_BIN_AP(Sym, Rty)                                                   \
+  template <int _AP_W, bool _AP_S, int _AP_W2, bool _AP_S2>                   \
+  INLINE                                                                      \
+      typename ap_int_base<_AP_W, _AP_S>::template RType<_AP_W2, _AP_S2>::Rty \
+      operator Sym(const ap_int_base<_AP_W, _AP_S>& op,                       \
+                   const ap_int_base<_AP_W2, _AP_S2>& op2) {                  \
+    typename ap_int_base<_AP_W, _AP_S>::template RType<                       \
+        _AP_W2, _AP_S2>::Rty##_base lhs(op);                                  \
+    typename ap_int_base<_AP_W, _AP_S>::template RType<                       \
+        _AP_W2, _AP_S2>::Rty##_base rhs(op2);                                 \
+    typename ap_int_base<_AP_W, _AP_S>::template RType<                       \
+        _AP_W2, _AP_S2>::Rty##_base ret;                                      \
+    ret.V = lhs.V Sym rhs.V;                                                  \
+    return ret;                                                               \
+  }
+
+OP_BIN_AP(*, mult)
+OP_BIN_AP(+, plus)
+OP_BIN_AP(-, minus)
+OP_BIN_AP(&, logic)
+OP_BIN_AP(|, logic)
+OP_BIN_AP(^, logic)
+
+#define OP_BIN_AP2(Sym, Rty)                                                  \
+  template <int _AP_W, bool _AP_S, int _AP_W2, bool _AP_S2>                   \
+  INLINE                                                                      \
+      typename ap_int_base<_AP_W, _AP_S>::template RType<_AP_W2, _AP_S2>::Rty \
+      operator Sym(const ap_int_base<_AP_W, _AP_S>& op,                       \
+                   const ap_int_base<_AP_W2, _AP_S2>& op2) {                  \
+    typename ap_int_base<_AP_W, _AP_S>::template RType<                       \
+        _AP_W2, _AP_S2>::Rty##_base ret;                                      \
+    ret.V = op.V Sym op2.V;                                                   \
+    return ret;                                                               \
+  }
+
+OP_BIN_AP2(/, div)
+OP_BIN_AP2(%, mod)
+
+// shift operators are defined inside class.
+// compound assignment operators are defined inside class.
+
+/* Operators with a pointer type.
+ * ----------------------------------------------------------------
+ *   char a[100];
+ *   char* ptr = a;
+ *   ap_int<2> n = 3;
+ *   char* ptr2 = ptr + n*2;
+ * avoid ambiguous errors.
+ */
+#define OP_BIN_WITH_PTR(BIN_OP)                                           \
+  template <typename PTR_TYPE, int _AP_W, bool _AP_S>                     \
+  INLINE PTR_TYPE* operator BIN_OP(PTR_TYPE* i_op,                        \
+                                   const ap_int_base<_AP_W, _AP_S>& op) { \
+    ap_slong op2 = op.to_int64(); /* Not all implementation */            \
+    return i_op BIN_OP op2;                                               \
+  }                                                                       \
+  template <typename PTR_TYPE, int _AP_W, bool _AP_S>                     \
+  INLINE PTR_TYPE* operator BIN_OP(const ap_int_base<_AP_W, _AP_S>& op,   \
+                                   PTR_TYPE* i_op) {                      \
+    ap_slong op2 = op.to_int64(); /* Not all implementation */            \
+    return op2 BIN_OP i_op;                                               \
+  }
+
+OP_BIN_WITH_PTR(+)
+OP_BIN_WITH_PTR(-)
+
+/* Operators with a native floating point types.
+ * ----------------------------------------------------------------
+ */
+// float OP ap_int
+// when ap_int<wa>'s width > 64, then trunc ap_int<w> to ap_int<64>
+#define OP_BIN_WITH_FLOAT(BIN_OP, C_TYPE)                              \
+  template <int _AP_W, bool _AP_S>                                     \
+  INLINE C_TYPE operator BIN_OP(C_TYPE i_op,                           \
+                                const ap_int_base<_AP_W, _AP_S>& op) { \
+    typename ap_int_base<_AP_W, _AP_S>::RetType op2 = op;              \
+    return i_op BIN_OP op2;                                            \
+  }                                                                    \
+  template <int _AP_W, bool _AP_S>                                     \
+  INLINE C_TYPE operator BIN_OP(const ap_int_base<_AP_W, _AP_S>& op,   \
+                                C_TYPE i_op) {                         \
+    typename ap_int_base<_AP_W, _AP_S>::RetType op2 = op;              \
+    return op2 BIN_OP i_op;                                            \
+  }
+
+#define ALL_OP_WITH_FLOAT(C_TYPE) \
+  OP_BIN_WITH_FLOAT(*, C_TYPE) \
+  OP_BIN_WITH_FLOAT(/, C_TYPE) \
+  OP_BIN_WITH_FLOAT(+, C_TYPE) \
+  OP_BIN_WITH_FLOAT(-, C_TYPE)
+
+#if _AP_ENABLE_HALF_ == 1
+ALL_OP_WITH_FLOAT(half)
+#endif
+ALL_OP_WITH_FLOAT(float)
+ALL_OP_WITH_FLOAT(double)
+
+// TODO no shift?
+
+/* Operators with a native integral types.
+ * ----------------------------------------------------------------
+ */
+// arithmetic and bitwise operators.
+#define OP_BIN_WITH_INT(BIN_OP, C_TYPE, _AP_W2, _AP_S2, RTYPE)             \
+  template <int _AP_W, bool _AP_S>                                         \
+  INLINE typename ap_int_base<_AP_W, _AP_S>::template RType<_AP_W2,        \
+                                                            _AP_S2>::RTYPE \
+  operator BIN_OP(C_TYPE i_op, const ap_int_base<_AP_W, _AP_S>& op) {      \
+    return ap_int_base<_AP_W2, _AP_S2>(i_op) BIN_OP(op);                   \
+  }                                                                        \
+  template <int _AP_W, bool _AP_S>                                         \
+  INLINE typename ap_int_base<_AP_W, _AP_S>::template RType<_AP_W2,        \
+                                                            _AP_S2>::RTYPE \
+  operator BIN_OP(const ap_int_base<_AP_W, _AP_S>& op, C_TYPE i_op) {      \
+    return op BIN_OP ap_int_base<_AP_W2, _AP_S2>(i_op);                    \
+  }
+
+#define ALL_OP_BIN_WITH_INT(C_TYPE, _AP_W2, _AP_S2)    \
+  OP_BIN_WITH_INT(*, C_TYPE, _AP_W2, _AP_S2, mult)  \
+  OP_BIN_WITH_INT(+, C_TYPE, _AP_W2, _AP_S2, plus)  \
+  OP_BIN_WITH_INT(-, C_TYPE, _AP_W2, _AP_S2, minus) \
+  OP_BIN_WITH_INT(/, C_TYPE, _AP_W2, _AP_S2, div)   \
+  OP_BIN_WITH_INT(%, C_TYPE, _AP_W2, _AP_S2, mod)   \
+  OP_BIN_WITH_INT(&, C_TYPE, _AP_W2, _AP_S2, logic) \
+  OP_BIN_WITH_INT(|, C_TYPE, _AP_W2, _AP_S2, logic) \
+  OP_BIN_WITH_INT(^, C_TYPE, _AP_W2, _AP_S2, logic)
+
+ALL_OP_BIN_WITH_INT(bool, 1, false)
+ALL_OP_BIN_WITH_INT(char, 8, CHAR_IS_SIGNED)
+ALL_OP_BIN_WITH_INT(signed char, 8, true)
+ALL_OP_BIN_WITH_INT(unsigned char, 8, false)
+ALL_OP_BIN_WITH_INT(short, _AP_SIZE_short, true)
+ALL_OP_BIN_WITH_INT(unsigned short, _AP_SIZE_short, false)
+ALL_OP_BIN_WITH_INT(int, _AP_SIZE_int, true)
+ALL_OP_BIN_WITH_INT(unsigned int, _AP_SIZE_int, false)
+ALL_OP_BIN_WITH_INT(long, _AP_SIZE_long, true)
+ALL_OP_BIN_WITH_INT(unsigned long, _AP_SIZE_long, false)
+ALL_OP_BIN_WITH_INT(ap_slong, _AP_SIZE_ap_slong, true)
+ALL_OP_BIN_WITH_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef OP_BIN_WITH_INT
+#undef ALL_OP_BIN_WITH_INT
+
+// shift operators.
+#define ALL_OP_SHIFT_WITH_INT(C_TYPE, _AP_W2, _AP_S2)    \
+  template <int _AP_W, bool _AP_S>                       \
+  INLINE typename ap_int_base<_AP_W, _AP_S>::template RType<_AP_W,_AP_S>::arg1 operator<<(           \
+      const ap_int_base<_AP_W, _AP_S>& op, C_TYPE op2) { \
+    ap_int_base<_AP_W, _AP_S> r;                         \
+    if (_AP_S2)                                          \
+      r.V = op2 >= 0 ? (op.V << op2) : (op.V >> (-op2)); \
+    else                                                 \
+      r.V = op.V << op2;                                 \
+    return r;                                            \
+  }                                                      \
+  template <int _AP_W, bool _AP_S>                       \
+  INLINE typename ap_int_base<_AP_W, _AP_S>::template RType<_AP_W,_AP_S>::arg1 operator>>(           \
+      const ap_int_base<_AP_W, _AP_S>& op, C_TYPE op2) { \
+    ap_int_base<_AP_W, _AP_S> r;                         \
+    if (_AP_S2)                                          \
+      r.V = op2 >= 0 ? (op.V >> op2) : (op.V << (-op2)); \
+    else                                                 \
+      r.V = op.V >> op2;                                 \
+    return r;                                            \
+  }
+
+ALL_OP_SHIFT_WITH_INT(char, 8, CHAR_IS_SIGNED)
+ALL_OP_SHIFT_WITH_INT(signed char, 8, true)
+ALL_OP_SHIFT_WITH_INT(short, _AP_SIZE_short, true)
+ALL_OP_SHIFT_WITH_INT(int, _AP_SIZE_int, true)
+ALL_OP_SHIFT_WITH_INT(long, _AP_SIZE_long, true)
+ALL_OP_SHIFT_WITH_INT(ap_slong, _AP_SIZE_ap_slong, true)
+
+#undef ALL_OP_SHIFT_WITH_INT
+
+#define ALL_OP_SHIFT_WITH_INT(C_TYPE, _AP_W2, _AP_S2)    \
+  template <int _AP_W, bool _AP_S>                       \
+  INLINE typename ap_int_base<_AP_W, _AP_S>::template RType<_AP_W,_AP_S>::arg1 operator<<(           \
+      const ap_int_base<_AP_W, _AP_S>& op, C_TYPE op2) { \
+    ap_int_base<_AP_W, _AP_S> r;                         \
+    r.V = op.V << op2;                                   \
+    return r;                                            \
+  }                                                      \
+  template <int _AP_W, bool _AP_S>                       \
+  INLINE typename ap_int_base<_AP_W, _AP_S>::template RType<_AP_W,_AP_S>::arg1 operator>>(           \
+      const ap_int_base<_AP_W, _AP_S>& op, C_TYPE op2) { \
+    ap_int_base<_AP_W, _AP_S> r;                         \
+    r.V = op.V >> op2;                                   \
+    return r;                                            \
+  }
+ALL_OP_SHIFT_WITH_INT(bool, 1, false)
+ALL_OP_SHIFT_WITH_INT(unsigned char, 8, false)
+ALL_OP_SHIFT_WITH_INT(unsigned short, _AP_SIZE_short, false)
+ALL_OP_SHIFT_WITH_INT(unsigned int, _AP_SIZE_int, false)
+ALL_OP_SHIFT_WITH_INT(unsigned long, _AP_SIZE_long, false)
+ALL_OP_SHIFT_WITH_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef ALL_OP_SHIFT_WITH_INT
+
+// compound assign operators.
+#define OP_ASSIGN_WITH_INT(ASSIGN_OP, C_TYPE, _AP_W2, _AP_S2)       \
+  template <int _AP_W, bool _AP_S>                                  \
+  INLINE ap_int_base<_AP_W, _AP_S>& operator ASSIGN_OP(             \
+      ap_int_base<_AP_W, _AP_S>& op, C_TYPE op2) {                  \
+    return op ASSIGN_OP ap_int_base<_AP_W2, _AP_S2>(op2);           \
+  }
+
+// TODO int a; ap_int<16> b; a += b;
+
+#define ALL_OP_ASSIGN_WITH_INT(C_TYPE, _AP_W2, _AP_S2) \
+  OP_ASSIGN_WITH_INT(+=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_ASSIGN_WITH_INT(-=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_ASSIGN_WITH_INT(*=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_ASSIGN_WITH_INT(/=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_ASSIGN_WITH_INT(%=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_ASSIGN_WITH_INT(&=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_ASSIGN_WITH_INT(|=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_ASSIGN_WITH_INT(^=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_ASSIGN_WITH_INT(>>=, C_TYPE, _AP_W2, _AP_S2)      \
+  OP_ASSIGN_WITH_INT(<<=, C_TYPE, _AP_W2, _AP_S2)
+
+ALL_OP_ASSIGN_WITH_INT(bool, 1, false)
+ALL_OP_ASSIGN_WITH_INT(char, 8, CHAR_IS_SIGNED)
+ALL_OP_ASSIGN_WITH_INT(signed char, 8, true)
+ALL_OP_ASSIGN_WITH_INT(unsigned char, 8, false)
+ALL_OP_ASSIGN_WITH_INT(short, _AP_SIZE_short, true)
+ALL_OP_ASSIGN_WITH_INT(unsigned short, _AP_SIZE_short, false)
+ALL_OP_ASSIGN_WITH_INT(int, _AP_SIZE_int, true)
+ALL_OP_ASSIGN_WITH_INT(unsigned int, _AP_SIZE_int, false)
+ALL_OP_ASSIGN_WITH_INT(long, _AP_SIZE_long, true)
+ALL_OP_ASSIGN_WITH_INT(unsigned long, _AP_SIZE_long, false)
+ALL_OP_ASSIGN_WITH_INT(ap_slong, _AP_SIZE_ap_slong, true)
+ALL_OP_ASSIGN_WITH_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef OP_ASSIGN_WITH_INT
+#undef ALL_OP_ASSIGN_WITH_INT
+
+// equality and relational operators.
+#define OP_REL_WITH_INT(REL_OP, C_TYPE, _AP_W2, _AP_S2)              \
+  template <int _AP_W, bool _AP_S>                                   \
+  INLINE bool operator REL_OP(C_TYPE i_op,                           \
+                              const ap_int_base<_AP_W, _AP_S>& op) { \
+    return ap_int_base<_AP_W2, _AP_S2>(i_op) REL_OP op;              \
+  }                                                                  \
+  template <int _AP_W, bool _AP_S>                                   \
+  INLINE bool operator REL_OP(const ap_int_base<_AP_W, _AP_S>& op,   \
+                              C_TYPE op2) {                          \
+    return op REL_OP ap_int_base<_AP_W2, _AP_S2>(op2);               \
+  }
+
+#define ALL_OP_REL_WITH_INT(C_TYPE, _AP_W2, _AP_S2) \
+  OP_REL_WITH_INT(>, C_TYPE, _AP_W2, _AP_S2)        \
+  OP_REL_WITH_INT(<, C_TYPE, _AP_W2, _AP_S2)        \
+  OP_REL_WITH_INT(>=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_REL_WITH_INT(<=, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_REL_WITH_INT(==, C_TYPE, _AP_W2, _AP_S2)       \
+  OP_REL_WITH_INT(!=, C_TYPE, _AP_W2, _AP_S2)
+
+ALL_OP_REL_WITH_INT(bool, 1, false)
+ALL_OP_REL_WITH_INT(char, 8, CHAR_IS_SIGNED)
+ALL_OP_REL_WITH_INT(signed char, 8, true)
+ALL_OP_REL_WITH_INT(unsigned char, 8, false)
+ALL_OP_REL_WITH_INT(short, _AP_SIZE_short, true)
+ALL_OP_REL_WITH_INT(unsigned short, _AP_SIZE_short, false)
+ALL_OP_REL_WITH_INT(int, _AP_SIZE_int, true)
+ALL_OP_REL_WITH_INT(unsigned int, _AP_SIZE_int, false)
+ALL_OP_REL_WITH_INT(long, _AP_SIZE_long, true)
+ALL_OP_REL_WITH_INT(unsigned long, _AP_SIZE_long, false)
+ALL_OP_REL_WITH_INT(ap_slong, _AP_SIZE_ap_slong, true)
+ALL_OP_REL_WITH_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef OP_REL_WITH_INT
+#undef ALL_OP_BIN_WITH_INT
+
+#define OP_REL_WITH_DOUBLE_OR_FLOAT(Sym)                            \
+  template <int _AP_W, bool _AP_S>                                  \
+  INLINE bool operator Sym(const ap_int_base<_AP_W, _AP_S>& op1,    \
+                           double op2) {                            \
+    return op1.to_double() Sym op2 ;                                \
+  }                                                                 \
+  template <int _AP_W, bool _AP_S>                                  \
+  INLINE bool operator Sym(double op1,                              \
+                           const ap_int_base<_AP_W, _AP_S>& op2) {  \
+    return op1 Sym op2.to_double() ;                                \
+  }                                                                 \
+  template <int _AP_W, bool _AP_S>                                  \
+  INLINE bool operator Sym(const ap_int_base<_AP_W, _AP_S>& op1,    \
+                           float op2) {                             \
+    return op1.to_double() Sym op2 ;                                \
+  }                                                                 \
+  template <int _AP_W, bool _AP_S>                                  \
+  INLINE bool operator Sym(float op1,                               \
+                           const ap_int_base<_AP_W, _AP_S>& op2) {  \
+    return op1 Sym op2.to_double() ;                                \
+  }
+  OP_REL_WITH_DOUBLE_OR_FLOAT(>)
+  OP_REL_WITH_DOUBLE_OR_FLOAT(<)
+  OP_REL_WITH_DOUBLE_OR_FLOAT(>=)
+  OP_REL_WITH_DOUBLE_OR_FLOAT(<=)
+  OP_REL_WITH_DOUBLE_OR_FLOAT(==)
+  OP_REL_WITH_DOUBLE_OR_FLOAT(!=)
+
+#undef OP_REL_WITH_DOUBLE_OR_FLOAT
+
+
+/* Operators with ap_bit_ref.
+ * ------------------------------------------------------------
+ */
+// arithmetic, bitwise and shift operators.
+#define OP_BIN_WITH_RANGE(BIN_OP, RTYPE)                                     \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                \
+  INLINE typename ap_int_base<_AP_W1, _AP_S1>::template RType<_AP_W2,        \
+                                                              _AP_S2>::RTYPE \
+  operator BIN_OP(const ap_range_ref<_AP_W1, _AP_S1>& op1,                   \
+                  const ap_int_base<_AP_W2, _AP_S2>& op2) {                  \
+    return ap_int_base<_AP_W1, false>(op1) BIN_OP op2;                       \
+  }                                                                          \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                \
+  INLINE typename ap_int_base<_AP_W1, _AP_S1>::template RType<_AP_W2,        \
+                                                              _AP_S2>::RTYPE \
+  operator BIN_OP(const ap_int_base<_AP_W1, _AP_S1>& op1,                    \
+                  const ap_range_ref<_AP_W2, _AP_S2>& op2) {                 \
+    return op1 BIN_OP ap_int_base<_AP_W2, false>(op2);                       \
+  }
+
+OP_BIN_WITH_RANGE(+, plus)
+OP_BIN_WITH_RANGE(-, minus)
+OP_BIN_WITH_RANGE(*, mult)
+OP_BIN_WITH_RANGE(/, div)
+OP_BIN_WITH_RANGE(%, mod)
+OP_BIN_WITH_RANGE(&, logic)
+OP_BIN_WITH_RANGE(|, logic)
+OP_BIN_WITH_RANGE(^, logic)
+OP_BIN_WITH_RANGE(>>, arg1)
+OP_BIN_WITH_RANGE(<<, arg1)
+
+#undef OP_BIN_WITH_RANGE
+
+// compound assignment operators.
+#define OP_ASSIGN_WITH_RANGE(ASSIGN_OP)                                      \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                \
+  INLINE ap_int_base<_AP_W1, _AP_S1>& operator ASSIGN_OP(                    \
+      ap_int_base<_AP_W1, _AP_S1>& op1, ap_range_ref<_AP_W2, _AP_S2>& op2) { \
+    return op1 ASSIGN_OP ap_int_base<_AP_W2, false>(op2);                    \
+  }                                                                          \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                \
+  INLINE ap_range_ref<_AP_W1, _AP_S1>& operator ASSIGN_OP(                   \
+      ap_range_ref<_AP_W1, _AP_S1>& op1, ap_int_base<_AP_W2, _AP_S2>& op2) { \
+    ap_int_base<_AP_W1, false> tmp(op1);                                     \
+    tmp ASSIGN_OP op2;                                                       \
+    op1 = tmp;                                                               \
+    return op1;                                                              \
+  }
+
+OP_ASSIGN_WITH_RANGE(+=)
+OP_ASSIGN_WITH_RANGE(-=)
+OP_ASSIGN_WITH_RANGE(*=)
+OP_ASSIGN_WITH_RANGE(/=)
+OP_ASSIGN_WITH_RANGE(%=)
+OP_ASSIGN_WITH_RANGE(&=)
+OP_ASSIGN_WITH_RANGE(|=)
+OP_ASSIGN_WITH_RANGE(^=)
+OP_ASSIGN_WITH_RANGE(>>=)
+OP_ASSIGN_WITH_RANGE(<<=)
+
+#undef OP_ASSIGN_WITH_RANGE
+
+// equality and relational operators
+#define OP_REL_WITH_RANGE(REL_OP)                                          \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>              \
+  INLINE bool operator REL_OP(const ap_range_ref<_AP_W1, _AP_S1>& op1,     \
+                              const ap_int_base<_AP_W2, _AP_S2>& op2) {    \
+    return ap_int_base<_AP_W1, false>(op1).operator REL_OP(op2);           \
+  }                                                                        \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>              \
+  INLINE bool operator REL_OP(const ap_int_base<_AP_W1, _AP_S1>& op1,      \
+                              const ap_range_ref<_AP_W2, _AP_S2>& op2) {   \
+    return op1.operator REL_OP(op2.operator ap_int_base<_AP_W2, false>()); \
+  }
+
+OP_REL_WITH_RANGE(==)
+OP_REL_WITH_RANGE(!=)
+OP_REL_WITH_RANGE(>)
+OP_REL_WITH_RANGE(>=)
+OP_REL_WITH_RANGE(<)
+OP_REL_WITH_RANGE(<=)
+
+#undef OP_REL_WITH_RANGE
+
+/* Operators with ap_bit_ref.
+ * ------------------------------------------------------------
+ */
+// arithmetic, bitwise and shift operators.
+#define OP_BIN_WITH_BIT(BIN_OP, RTYPE)                                         \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                  \
+  INLINE typename ap_int_base<_AP_W1, _AP_S1>::template RType<1, false>::RTYPE \
+  operator BIN_OP(const ap_int_base<_AP_W1, _AP_S1>& op1,                      \
+                  const ap_bit_ref<_AP_W2, _AP_S2>& op2) {                     \
+    return op1 BIN_OP ap_int_base<1, false>(op2);                              \
+  }                                                                            \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                  \
+  INLINE typename ap_int_base<1, false>::template RType<_AP_W2, _AP_S2>::RTYPE \
+  operator BIN_OP(const ap_bit_ref<_AP_W1, _AP_S1>& op1,                       \
+                  const ap_int_base<_AP_W2, _AP_S2>& op2) {                    \
+    return ap_int_base<1, false>(op1) BIN_OP op2;                              \
+  }
+
+OP_BIN_WITH_BIT(+, plus)
+OP_BIN_WITH_BIT(-, minus)
+OP_BIN_WITH_BIT(*, mult)
+OP_BIN_WITH_BIT(/, div)
+OP_BIN_WITH_BIT(%, mod)
+OP_BIN_WITH_BIT(&, logic)
+OP_BIN_WITH_BIT(|, logic)
+OP_BIN_WITH_BIT(^, logic)
+OP_BIN_WITH_BIT(>>, arg1)
+OP_BIN_WITH_BIT(<<, arg1)
+
+#undef OP_BIN_WITH_BIT
+
+// compound assignment operators.
+#define OP_ASSIGN_WITH_BIT(ASSIGN_OP)                                      \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>              \
+  INLINE ap_int_base<_AP_W1, _AP_S1>& operator ASSIGN_OP(                  \
+      ap_int_base<_AP_W1, _AP_S1>& op1, ap_bit_ref<_AP_W2, _AP_S2>& op2) { \
+    return op1 ASSIGN_OP ap_int_base<1, false>(op2);                       \
+  }                                                                        \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>              \
+  INLINE ap_bit_ref<_AP_W1, _AP_S1>& operator ASSIGN_OP(                   \
+      ap_bit_ref<_AP_W1, _AP_S1>& op1, ap_int_base<_AP_W2, _AP_S2>& op2) { \
+    ap_int_base<1, false> tmp(op1);                                        \
+    tmp ASSIGN_OP op2;                                                     \
+    op1 = tmp;                                                             \
+    return op1;                                                            \
+  }
+
+OP_ASSIGN_WITH_BIT(+=)
+OP_ASSIGN_WITH_BIT(-=)
+OP_ASSIGN_WITH_BIT(*=)
+OP_ASSIGN_WITH_BIT(/=)
+OP_ASSIGN_WITH_BIT(%=)
+OP_ASSIGN_WITH_BIT(&=)
+OP_ASSIGN_WITH_BIT(|=)
+OP_ASSIGN_WITH_BIT(^=)
+OP_ASSIGN_WITH_BIT(>>=)
+OP_ASSIGN_WITH_BIT(<<=)
+
+#undef OP_ASSIGN_WITH_BIT
+
+// equality and relational operators.
+#define OP_REL_WITH_BIT(REL_OP)                                         \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>           \
+  INLINE bool operator REL_OP(const ap_int_base<_AP_W1, _AP_S1>& op1,   \
+                              const ap_bit_ref<_AP_W2, _AP_S2>& op2) {  \
+    return op1 REL_OP ap_int_base<1, false>(op2);                       \
+  }                                                                     \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>           \
+  INLINE bool operator REL_OP(const ap_bit_ref<_AP_W1, _AP_S1>& op1,    \
+                              const ap_int_base<_AP_W2, _AP_S2>& op2) { \
+    return ap_int_base<1, false>(op1) REL_OP op2;                       \
+  }
+
+OP_REL_WITH_BIT(==)
+OP_REL_WITH_BIT(!=)
+OP_REL_WITH_BIT(>)
+OP_REL_WITH_BIT(>=)
+OP_REL_WITH_BIT(<)
+OP_REL_WITH_BIT(<=)
+
+#undef OP_REL_WITH_BIT
+
+
+/* Operators with ap_concat_ref.
+ * ------------------------------------------------------------
+ */
+// arithmetic, bitwise and shift operators.
+// bitwise operators are defined in struct.
+// TODO specify whether to define arithmetic and bitwise operators.
+#if 0
+#define OP_BIN_WITH_CONCAT(BIN_OP, RTYPE)                                      \
+  template <int _AP_W1, typename _AP_T1, int _AP_W2, typename _AP_T2,          \
+            int _AP_W3, bool _AP_S3>                                           \
+  INLINE typename ap_int_base<_AP_W3, _AP_S3>::template RType<_AP_W1 + _AP_W2, \
+                                                              false>::RTYPE    \
+  operator BIN_OP(const ap_int_base<_AP_W3, _AP_S3>& op1,                      \
+                  const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>& op2) {  \
+    /* convert ap_concat_ref to ap_int_base */                                 \
+    return op1 BIN_OP op2.get();                                               \
+  }                                                                            \
+  template <int _AP_W1, typename _AP_T1, int _AP_W2, typename _AP_T2,          \
+            int _AP_W3, bool _AP_S3>                                           \
+  INLINE typename ap_int_base<_AP_W1 + _AP_W2,                                 \
+                              false>::template RType<_AP_W3, _AP_S3>::RTYPE    \
+  operator BIN_OP(const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>& op1,    \
+                  const ap_int_base<_AP_W3, _AP_S3>& op2) {                    \
+    /* convert ap_concat_ref to ap_int_base */                                 \
+    return op1.get() BIN_OP op2;                                               \
+  }
+
+OP_BIN_WITH_CONCAT(+, plus)
+OP_BIN_WITH_CONCAT(-, minus)
+OP_BIN_WITH_CONCAT(*, mult)
+OP_BIN_WITH_CONCAT(/, div)
+OP_BIN_WITH_CONCAT(%, mod)
+OP_BIN_WITH_CONCAT(&, logic)
+OP_BIN_WITH_CONCAT(|, logic)
+OP_BIN_WITH_CONCAT(^, logic)
+OP_BIN_WITH_CONCAT(>>, arg1)
+OP_BIN_WITH_CONCAT(<<, arg1)
+
+#undef OP_BIN_WITH_CONCAT
+
+// compound assignment operators.
+#define OP_ASSIGN_WITH_CONCAT(ASSIGN_OP)                                       \
+  template <int _AP_W1, typename _AP_T1, int _AP_W2, typename _AP_T2,          \
+            int _AP_W3, bool _AP_S3>                                           \
+  INLINE typename ap_int_base<_AP_W3, _AP_S3>::template RType<_AP_W1 + _AP_W2, \
+                                                              false>::RTYPE    \
+  operator ASSIGN_OP(                                                          \
+      const ap_int_base<_AP_W3, _AP_S3>& op1,                                  \
+      const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>& op2) {              \
+    /* convert ap_concat_ref to ap_int_base */                                 \
+    return op1 ASSIGN_OP op2.get();                                            \
+  }                                                                            \
+  template <int _AP_W1, typename _AP_T1, int _AP_W2, typename _AP_T2,          \
+            int _AP_W3, bool _AP_S3>                                           \
+  INLINE typename ap_int_base<_AP_W1 + _AP_W2,                                 \
+                              false>::template RType<_AP_W3, _AP_S3>::RTYPE    \
+  operator ASSIGN_OP(const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>& op1, \
+                     const ap_int_base<_AP_W3, _AP_S3>& op2) {                 \
+    /* convert ap_concat_ref to ap_int_base */                                 \
+    ap_int_base<_AP_W1 + _AP_W2, false> tmp = op1.get();                       \
+    tmp ASSIGN_OP op2;                                                         \
+    op1 = tmp;                                                                 \
+    return op1;                                                                \
+  }
+
+OP_ASSIGN_WITH_CONCAT(+=)
+OP_ASSIGN_WITH_CONCAT(-=)
+OP_ASSIGN_WITH_CONCAT(*=)
+OP_ASSIGN_WITH_CONCAT(/=)
+OP_ASSIGN_WITH_CONCAT(%=)
+OP_ASSIGN_WITH_CONCAT(&=)
+OP_ASSIGN_WITH_CONCAT(|=)
+OP_ASSIGN_WITH_CONCAT(^=)
+OP_ASSIGN_WITH_CONCAT(>>=)
+OP_ASSIGN_WITH_CONCAT(<<=)
+
+#undef OP_ASSIGN_WITH_CONCAT
+#endif
+
+// equality and relational operators.
+#define OP_REL_WITH_CONCAT(REL_OP)                                    \
+  template <int _AP_W1, typename _AP_T1, int _AP_W2, typename _AP_T2, \
+            int _AP_W3, bool _AP_S3>                                  \
+  INLINE bool operator REL_OP(                                        \
+      const ap_int_base<_AP_W3, _AP_S3>& op1,                         \
+      const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>& op2) {     \
+    /* convert ap_concat_ref to ap_int_base */                        \
+    return op1 REL_OP op2.get();                                      \
+  }                                                                   \
+  template <int _AP_W1, typename _AP_T1, int _AP_W2, typename _AP_T2, \
+            int _AP_W3, bool _AP_S3>                                  \
+  INLINE bool operator REL_OP(                                        \
+      const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>& op1,       \
+      const ap_int_base<_AP_W3, _AP_S3>& op2) {                       \
+    /* convert ap_concat_ref to ap_int_base */                        \
+    return op1.get() REL_OP op2;                                      \
+  }
+
+OP_REL_WITH_CONCAT(==)
+OP_REL_WITH_CONCAT(!=)
+OP_REL_WITH_CONCAT(>)
+OP_REL_WITH_CONCAT(>=)
+OP_REL_WITH_CONCAT(<)
+OP_REL_WITH_CONCAT(<=)
+
+#undef OP_REL_WITH_CONCAT
+
+#endif // ifndef __cplusplus
+#endif // ifndef __AP_INT_BASE_H__
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_int_ref.h b/model_1/hls4ml_prj/firmware/ap_types/ap_int_ref.h
new file mode 100644
index 0000000..421f09f
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_int_ref.h
@@ -0,0 +1,1346 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_INT_REF_H__
+#define __AP_INT_REF_H__
+
+#ifndef __AP_INT_H__
+#error "Only ap_fixed.h and ap_int.h can be included directly in user code."
+#endif
+
+#ifndef __cplusplus
+#error "C++ is required to include this header file"
+
+#else
+
+#ifndef __SYNTHESIS__
+#include <iostream>
+#endif
+
+/* Concatination reference.
+   ----------------------------------------------------------------
+*/
+template <int _AP_W1, typename _AP_T1, int _AP_W2, typename _AP_T2>
+struct ap_concat_ref {
+  enum {
+    _AP_WR = _AP_W1 + _AP_W2,
+  };
+
+  _AP_T1& mbv1;
+  _AP_T2& mbv2;
+
+  INLINE ap_concat_ref(const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>& ref)
+      : mbv1(ref.mbv1), mbv2(ref.mbv2) {}
+
+  INLINE ap_concat_ref(_AP_T1& bv1, _AP_T2& bv2) : mbv1(bv1), mbv2(bv2) {}
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE ap_concat_ref& operator=(const ap_int_base<_AP_W3, _AP_S3>& val) {
+    ap_int_base<_AP_W1 + _AP_W2, false> vval(val);
+    int W_ref1 = mbv1.length();
+    int W_ref2 = mbv2.length();
+    ap_int_base<_AP_W1, false> Part1;
+    Part1.V = _AP_ROOT_op_get_range(vval.V, W_ref2, W_ref1 + W_ref2 - 1);
+    mbv1.set(Part1);
+    ap_int_base<_AP_W2, false> Part2;
+    Part2.V = _AP_ROOT_op_get_range(vval.V, 0, W_ref2 - 1);
+    mbv2.set(Part2);
+    return *this;
+  }
+
+  // assign op from hls supported C integral types.
+  // FIXME disabled to support legacy code directly assign from sc_signal<T>
+  //template <typename T>
+  //INLINE typename _ap_type::enable_if<_ap_type::is_integral<T>::value,
+  //                                    ap_concat_ref&>::type
+  //operator=(T val) {
+  //  ap_int_base<_AP_W1 + _AP_W2, false> tmpVal(val);
+  //  return operator=(tmpVal);
+  //}
+#define ASSIGN_WITH_CTYPE(_Tp)                       \
+  INLINE ap_concat_ref& operator=(_Tp val) {         \
+    ap_int_base<_AP_W1 + _AP_W2, false> tmpVal(val); \
+    return operator=(tmpVal);                        \
+  }
+
+  ASSIGN_WITH_CTYPE(bool)
+  ASSIGN_WITH_CTYPE(char)
+  ASSIGN_WITH_CTYPE(signed char)
+  ASSIGN_WITH_CTYPE(unsigned char)
+  ASSIGN_WITH_CTYPE(short)
+  ASSIGN_WITH_CTYPE(unsigned short)
+  ASSIGN_WITH_CTYPE(int)
+  ASSIGN_WITH_CTYPE(unsigned int)
+  ASSIGN_WITH_CTYPE(long)
+  ASSIGN_WITH_CTYPE(unsigned long)
+  ASSIGN_WITH_CTYPE(ap_slong)
+  ASSIGN_WITH_CTYPE(ap_ulong)
+#if _AP_ENABLE_HALF_ == 1
+  ASSIGN_WITH_CTYPE(half)
+#endif
+  ASSIGN_WITH_CTYPE(float)
+  ASSIGN_WITH_CTYPE(double)
+
+#undef ASSIGN_WITH_CTYPE
+
+  // Be explicit to prevent it from being deleted, as field d_bv
+  // is of reference type.
+  INLINE ap_concat_ref& operator=(
+      const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>& val) {
+    ap_int_base<_AP_W1 + _AP_W2, false> tmpVal(val);
+    return operator=(tmpVal);
+  }
+
+  template <int _AP_W3, typename _AP_T3, int _AP_W4, typename _AP_T4>
+  INLINE ap_concat_ref& operator=(
+      const ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4>& val) {
+    ap_int_base<_AP_W1 + _AP_W2, false> tmpVal(val);
+    return operator=(tmpVal);
+  }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE ap_concat_ref& operator=(const ap_bit_ref<_AP_W3, _AP_S3>& val) {
+    ap_int_base<_AP_W1 + _AP_W2, false> tmpVal(val);
+    return operator=(tmpVal);
+  }
+  template <int _AP_W3, bool _AP_S3>
+  INLINE ap_concat_ref& operator=(const ap_range_ref<_AP_W3, _AP_S3>& val) {
+    ap_int_base<_AP_W1 + _AP_W2, false> tmpVal(val);
+    return operator=(tmpVal);
+  }
+
+  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+            ap_o_mode _AP_O3, int _AP_N3>
+  INLINE ap_concat_ref& operator=(
+      const af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>& val) {
+    return operator=((const ap_int_base<_AP_W3, false>)(val));
+  }
+
+  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+            ap_o_mode _AP_O3, int _AP_N3>
+  INLINE ap_concat_ref& operator=(
+      const ap_fixed_base<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>&
+          val) {
+    return operator=(val.to_ap_int_base());
+  }
+
+  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+            ap_o_mode _AP_O3, int _AP_N3>
+  INLINE ap_concat_ref& operator=(
+      const af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>& val) {
+    return operator=((ap_ulong)(bool)(val));
+  }
+
+  INLINE operator ap_int_base<_AP_WR, false>() const { return get(); }
+
+  INLINE operator ap_ulong() const { return get().to_uint64(); }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+                       ap_range_ref<_AP_W3, _AP_S3> >
+  operator,(const ap_range_ref<_AP_W3, _AP_S3> &a2) {
+    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+                         ap_range_ref<_AP_W3, _AP_S3> >(
+        *this, const_cast<ap_range_ref<_AP_W3, _AP_S3>&>(a2));
+  }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE
+      ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3, ap_int_base<_AP_W3, _AP_S3> >
+      operator,(ap_int_base<_AP_W3, _AP_S3> &a2) {
+    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+                         ap_int_base<_AP_W3, _AP_S3> >(*this, a2);
+  }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE
+      ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3, ap_int_base<_AP_W3, _AP_S3> >
+      operator,(volatile ap_int_base<_AP_W3, _AP_S3> &a2) {
+    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+                         ap_int_base<_AP_W3, _AP_S3> >(
+        *this, const_cast<ap_int_base<_AP_W3, _AP_S3>&>(a2));
+  }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE
+      ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3, ap_int_base<_AP_W3, _AP_S3> >
+      operator,(const ap_int_base<_AP_W3, _AP_S3> &a2) {
+    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+                         ap_int_base<_AP_W3, _AP_S3> >(
+        *this, const_cast<ap_int_base<_AP_W3, _AP_S3>&>(a2));
+  }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE
+      ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3, ap_int_base<_AP_W3, _AP_S3> >
+      operator,(const volatile ap_int_base<_AP_W3, _AP_S3> &a2) {
+    // FIXME op's life does not seem long enough
+    ap_int_base<_AP_W3, _AP_S3> op(a2);
+    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+                         ap_int_base<_AP_W3, _AP_S3> >(
+        *this, const_cast<ap_int_base<_AP_W3, _AP_S3>&>(op));
+  }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE ap_concat_ref<_AP_WR, ap_concat_ref, 1, ap_bit_ref<_AP_W3, _AP_S3> >
+  operator,(const ap_bit_ref<_AP_W3, _AP_S3> &a2) {
+    return ap_concat_ref<_AP_WR, ap_concat_ref, 1, ap_bit_ref<_AP_W3, _AP_S3> >(
+        *this, const_cast<ap_bit_ref<_AP_W3, _AP_S3>&>(a2));
+  }
+
+  template <int _AP_W3, typename _AP_T3, int _AP_W4, typename _AP_T4>
+  INLINE ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3 + _AP_W4,
+                       ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4> >
+  operator,(const ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4> &a2) {
+    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3 + _AP_W4,
+                         ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4> >(
+        *this, const_cast<ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4>&>(a2));
+  }
+
+  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+            ap_o_mode _AP_O3, int _AP_N3>
+  INLINE ap_concat_ref<
+      _AP_WR, ap_concat_ref, _AP_W3,
+      af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3> >
+  operator,(
+      const af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3> &a2) {
+    return ap_concat_ref<
+        _AP_WR, ap_concat_ref, _AP_W3,
+        af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3> >(
+        *this,
+        const_cast<
+            af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>&>(a2));
+  }
+
+  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+            ap_o_mode _AP_O3, int _AP_N3>
+  INLINE
+      ap_concat_ref<_AP_WR, ap_concat_ref, 1,
+                    af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3> >
+      operator,(const af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>
+                    &a2) {
+    return ap_concat_ref<
+        _AP_WR, ap_concat_ref, 1,
+        af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3> >(
+        *this,
+        const_cast<af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>&>(
+            a2));
+  }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE ap_int_base<AP_MAX(_AP_WR, _AP_W3), _AP_S3> operator&(
+      const ap_int_base<_AP_W3, _AP_S3>& a2) {
+    return get() & a2;
+  }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE ap_int_base<AP_MAX(_AP_WR, _AP_W3), _AP_S3> operator|(
+      const ap_int_base<_AP_W3, _AP_S3>& a2) {
+    return get() | a2;
+  }
+
+  template <int _AP_W3, bool _AP_S3>
+  INLINE ap_int_base<AP_MAX(_AP_WR, _AP_W3), _AP_S3> operator^(
+      const ap_int_base<_AP_W3, _AP_S3>& a2) {
+    return get() ^ a2;
+  }
+
+#if 0
+  template<int Hi, int Lo>
+  INLINE ap_int_base<Hi-Lo+1, false> slice() {
+    ap_int_base<_AP_WR, false> bv = get();
+    return bv.slice<Hi,Lo>();
+  }
+#endif
+
+  INLINE ap_int_base<_AP_WR, false> get() const {
+    ap_int_base<_AP_WR, false> tmpVal(0);
+    int W_ref1 = mbv1.length();
+    int W_ref2 = mbv2.length();
+    ap_int_base<_AP_W2, false> v2(mbv2);
+    ap_int_base<_AP_W1, false> v1(mbv1);
+    tmpVal.V = _AP_ROOT_op_set_range(tmpVal.V, 0, W_ref2 - 1, v2.V);
+    tmpVal.V =
+        _AP_ROOT_op_set_range(tmpVal.V, W_ref2, W_ref1 + W_ref2 - 1, v1.V);
+    return tmpVal;
+  }
+
+  template <int _AP_W3>
+  INLINE void set(const ap_int_base<_AP_W3, false>& val) {
+    ap_int_base<_AP_W1 + _AP_W2, false> vval(val);
+    int W_ref1 = mbv1.length();
+    int W_ref2 = mbv2.length();
+    ap_int_base<_AP_W1, false> tmpVal1;
+    tmpVal1.V = _AP_ROOT_op_get_range(vval.V, W_ref2, W_ref1 + W_ref2 - 1);
+    mbv1.set(tmpVal1);
+    ap_int_base<_AP_W2, false> tmpVal2;
+    tmpVal2.V = _AP_ROOT_op_get_range(vval.V, 0, W_ref2 - 1);
+    mbv2.set(tmpVal2);
+  }
+
+  INLINE int length() const { return mbv1.length() + mbv2.length(); }
+}; // struct ap_concat_ref
+
+/* Range (slice) reference.
+   ----------------------------------------------------------------
+*/
+template <int _AP_W, bool _AP_S>
+struct ap_range_ref {
+  // struct ssdm_int or its sim model.
+  // TODO make it possible to reference to ap_fixed_base/ap_fixed/ap_ufixed
+  //      and then we can retire af_range_ref.
+  typedef ap_int_base<_AP_W, _AP_S> ref_type;
+  ref_type& d_bv;
+  int l_index;
+  int h_index;
+
+ public:
+  INLINE ap_range_ref(const ap_range_ref<_AP_W, _AP_S>& ref)
+      : d_bv(ref.d_bv), l_index(ref.l_index), h_index(ref.h_index) {}
+
+  INLINE ap_range_ref(ref_type* bv, int h, int l)
+      : d_bv(*bv), l_index(l), h_index(h) {}
+
+  INLINE ap_range_ref(const ref_type* bv, int h, int l)
+      : d_bv(*const_cast<ref_type*>(bv)), l_index(l), h_index(h) {}
+
+  INLINE operator ap_int_base<_AP_W, false>() const {
+    ap_int_base<_AP_W, false> ret;
+    ret.V = _AP_ROOT_op_get_range(d_bv.V, l_index, h_index);
+    return ret;
+  }
+
+  INLINE operator ap_ulong() const { return to_uint64(); }
+
+  /// @name assign operators
+  //  @{
+
+  // FIXME disabled to work-around lagacy code assigning from sc_signal<T>,
+  // which dependes on implicit type conversion.
+  //
+  //   /// assign from hls supported C integral types.
+  //   template <typename T>
+  //   INLINE typename _ap_type::enable_if<_ap_type::is_integral<T>::value,
+  //                                       ap_range_ref&>::type
+  //   operator=(T val) {
+  //     ap_int_base<_AP_W, false> tmp(val);
+  //     d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, tmp.V);
+  //     return *this;
+  //   }
+#define ASSIGN_WITH_CTYPE(_Tp)                                       \
+  INLINE ap_range_ref& operator=(_Tp val) {                          \
+    ap_int_base<_AP_W, false> tmp(val);                              \
+    d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, tmp.V); \
+    return *this;                                                    \
+  }
+
+  ASSIGN_WITH_CTYPE(bool)
+  ASSIGN_WITH_CTYPE(char)
+  ASSIGN_WITH_CTYPE(signed char)
+  ASSIGN_WITH_CTYPE(unsigned char)
+  ASSIGN_WITH_CTYPE(short)
+  ASSIGN_WITH_CTYPE(unsigned short)
+  ASSIGN_WITH_CTYPE(int)
+  ASSIGN_WITH_CTYPE(unsigned int)
+  ASSIGN_WITH_CTYPE(long)
+  ASSIGN_WITH_CTYPE(unsigned long)
+  ASSIGN_WITH_CTYPE(ap_slong)
+  ASSIGN_WITH_CTYPE(ap_ulong)
+#if _AP_ENABLE_HALF_ == 1
+  ASSIGN_WITH_CTYPE(half)
+#endif
+  ASSIGN_WITH_CTYPE(float)
+  ASSIGN_WITH_CTYPE(double)
+
+#undef ASSIGN_WITH_CTYPE
+
+  /// assign using string. XXX crucial for cosim.
+  INLINE ap_range_ref& operator=(const char* val) {
+    const ap_int_base<_AP_W, false> tmp(val); // XXX figure out radix
+    d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, tmp.V);
+    return *this;
+  }
+
+  /// assign from ap_int_base.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_range_ref& operator=(const ap_int_base<_AP_W2, _AP_S2>& val) {
+    ap_int_base<_AP_W, false> tmp(val);
+    d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, tmp.V);
+    return *this;
+  }
+
+  /// copy assign operator
+  // XXX Be explicit to prevent it from being deleted, as field d_bv
+  // is of reference type.
+  INLINE ap_range_ref& operator=(const ap_range_ref& val) {
+    return operator=((const ap_int_base<_AP_W, false>)val);
+  }
+
+  /// assign from range reference to ap_int_base.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_range_ref& operator=(const ap_range_ref<_AP_W2, _AP_S2>& val) {
+    return operator=((const ap_int_base<_AP_W2, false>)val);
+  }
+
+  /// assign from bit reference to ap_int_base.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_range_ref& operator=(const ap_bit_ref<_AP_W2, _AP_S2>& val) {
+    return operator=((ap_ulong)(bool)(val));
+  }
+
+  /// assign from ap_fixed_base.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_range_ref& operator=(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&
+          val) {
+    return operator=(val.to_ap_int_base());
+  }
+
+  /// assign from range reference to ap_fixed_base.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_range_ref& operator=(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    return operator=((const ap_int_base<_AP_W2, false>)val);
+  }
+
+  /// assign from bit reference to ap_fixed_base.
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_range_ref& operator=(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    return operator=((ap_ulong)(bool)(val));
+  }
+
+  /// assign from compound reference.
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_range_ref& operator=(
+      const ap_concat_ref<_AP_W2, _AP_T3, _AP_W3, _AP_T3>& val) {
+    return operator=((const ap_int_base<_AP_W2 + _AP_W3, false>)(val));
+  }
+  //  @}
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_range_ref, _AP_W2, ap_range_ref<_AP_W2, _AP_S2> >
+      operator,(const ap_range_ref<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_range_ref, _AP_W2,
+                         ap_range_ref<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_range_ref<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_range_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+      operator,(ap_int_base<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_range_ref, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(*this, a2);
+  }
+
+  INLINE
+  ap_concat_ref<_AP_W, ap_range_ref, _AP_W, ap_int_base<_AP_W, _AP_S> >
+  operator,(ap_int_base<_AP_W, _AP_S>& a2) {
+    return ap_concat_ref<_AP_W, ap_range_ref, _AP_W,
+                         ap_int_base<_AP_W, _AP_S> >(*this, a2);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_range_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+      operator,(volatile ap_int_base<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_range_ref, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_int_base<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_range_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+      operator,(const ap_int_base<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_range_ref, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_int_base<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_range_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+      operator,(const volatile ap_int_base<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_range_ref, _AP_W2,
+                         ap_int_base<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_int_base<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<_AP_W, ap_range_ref, 1, ap_bit_ref<_AP_W2, _AP_S2> >
+  operator,(const ap_bit_ref<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<_AP_W, ap_range_ref, 1, ap_bit_ref<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_bit_ref<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_concat_ref<_AP_W, ap_range_ref, _AP_W2 + _AP_W3,
+                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+  operator,(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) {
+    return ap_concat_ref<_AP_W, ap_range_ref, _AP_W2 + _AP_W3,
+                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(
+        *this, const_cast<ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>&>(a2));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_concat_ref<
+      _AP_W, ap_range_ref, _AP_W2,
+      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+  operator,(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> a2) {
+    return ap_concat_ref<
+        _AP_W, ap_range_ref, _AP_W2,
+        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+        *this,
+        const_cast<
+            af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(a2));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE
+      ap_concat_ref<_AP_W, ap_range_ref, 1,
+                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+      operator,(const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>
+                    &a2) {
+    return ap_concat_ref<
+        _AP_W, ap_range_ref, 1,
+        af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+        *this,
+        const_cast<af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(
+            a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator==(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_int_base<_AP_W, false> lop(*this);
+    ap_int_base<_AP_W2, false> hop(op2);
+    return lop == hop;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator!=(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    return !(operator==(op2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_int_base<_AP_W, false> lop(*this);
+    ap_int_base<_AP_W2, false> hop(op2);
+    return lop < hop;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<=(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_int_base<_AP_W, false> lop(*this);
+    ap_int_base<_AP_W2, false> hop(op2);
+    return lop <= hop;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    return !(operator<=(op2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>=(const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    return !(operator<(op2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_range_ref<_AP_W, _AP_S>& operator|=(
+      const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    (this->d_bv).V |= (op2.d_bv).V;
+    return *this;
+  };
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_range_ref<_AP_W, _AP_S>& operator|=(
+      const ap_int_base<_AP_W2, _AP_S2>& op2) {
+    (this->d_bv).V |= op2.V;
+    return *this;
+  };
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_range_ref<_AP_W, _AP_S>& operator&=(
+      const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    (this->d_bv).V &= (op2.d_bv).V;
+    return *this;
+  };
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_range_ref<_AP_W, _AP_S>& operator&=(
+      const ap_int_base<_AP_W2, _AP_S2>& op2) {
+    (this->d_bv).V &= op2.V;
+    return *this;
+  };
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_range_ref<_AP_W, _AP_S>& operator^=(
+      const ap_range_ref<_AP_W2, _AP_S2>& op2) {
+    (this->d_bv).V ^= (op2.d_bv).V;
+    return *this;
+  };
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_range_ref<_AP_W, _AP_S>& operator^=(
+      const ap_int_base<_AP_W2, _AP_S2>& op2) {
+    (this->d_bv).V ^= op2.V;
+    return *this;
+  };
+
+  INLINE ap_int_base<_AP_W, false> get() const {
+    ap_int_base<_AP_W, false> ret;
+    ret.V = _AP_ROOT_op_get_range(d_bv.V, l_index, h_index);
+    return ret;
+  }
+
+  template <int _AP_W2>
+  INLINE void set(const ap_int_base<_AP_W2, false>& val) {
+    d_bv.V = _AP_ROOT_op_set_range(d_bv.V, l_index, h_index, val.V);
+  }
+
+  INLINE int length() const {
+    return h_index >= l_index ? h_index - l_index + 1 : l_index - h_index + 1;
+  }
+
+  INLINE int to_int() const {
+    return (int)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE unsigned to_uint() const {
+    return (unsigned)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE long to_long() const {
+    return (long)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE unsigned long to_ulong() const {
+    return (unsigned long)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE ap_slong to_int64() const {
+    return (ap_slong)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE ap_ulong to_uint64() const {
+    return (ap_ulong)(_AP_ROOT_op_get_range(d_bv.V, l_index, h_index));
+  }
+
+  INLINE bool and_reduce() const {
+    bool ret = true;
+    bool reverse = l_index > h_index;
+    unsigned low = reverse ? h_index : l_index;
+    unsigned high = reverse ? l_index : h_index;
+    for (unsigned i = low; i != high; ++i) {
+#ifdef __SYNTHESIS__
+#pragma HLS unroll
+#endif
+      ret &= _AP_ROOT_op_get_bit(d_bv.V, i);
+    }
+    return ret;
+  }
+
+  INLINE bool or_reduce() const {
+    bool ret = false;
+    bool reverse = l_index > h_index;
+    unsigned low = reverse ? h_index : l_index;
+    unsigned high = reverse ? l_index : h_index;
+    for (unsigned i = low; i != high; ++i) {
+#ifdef __SYNTHESIS__
+#pragma HLS unroll
+#endif
+      ret |= _AP_ROOT_op_get_bit(d_bv.V, i);
+    }
+    return ret;
+  }
+
+  INLINE bool xor_reduce() const {
+    bool ret = false;
+    bool reverse = l_index > h_index;
+    unsigned low = reverse ? h_index : l_index;
+    unsigned high = reverse ? l_index : h_index;
+    for (unsigned i = low; i != high; ++i) {
+#ifdef __SYNTHESIS__
+#pragma HLS unroll
+#endif
+      ret ^= _AP_ROOT_op_get_bit(d_bv.V, i);
+    }
+    return ret;
+  }
+#ifndef __SYNTHESIS__
+  std::string to_string(signed char radix = 2) const {
+    ap_int_base<_AP_W, false> ret;
+    ret.V = _AP_ROOT_op_get_range(d_bv.V, l_index, h_index);
+    return ret.to_string(radix);
+  }
+#else
+  // XXX HLS will delete this in synthesis
+  INLINE char* to_string(signed char radix = 2) const {
+    return 0;
+  }
+#endif
+}; // struct ap_range_ref
+
+// XXX apcc cannot handle global std::ios_base::Init() brought in by <iostream>
+#ifndef AP_AUTOCC
+#ifndef __SYNTHESIS__
+template <int _AP_W, bool _AP_S>
+INLINE std::ostream& operator<<(std::ostream& os,
+                                const ap_range_ref<_AP_W, _AP_S>& x) {
+  std::ios_base::fmtflags ff = std::cout.flags();
+  if (ff & std::cout.hex) {
+    os << x.to_string(16); // don't print sign
+  } else if (ff & std::cout.oct) {
+    os << x.to_string(8); // don't print sign
+  } else {
+    os << x.to_string(10);
+  }
+  return os;
+}
+#endif // ifndef __SYNTHESIS__
+
+#ifndef __SYNTHESIS__
+template <int _AP_W, bool _AP_S>
+INLINE std::istream& operator>>(std::istream& in,
+                                ap_range_ref<_AP_W, _AP_S>& op) {
+  std::string str;
+  in >> str;
+  op = ap_int_base<_AP_W, _AP_S>(str.c_str());
+  return in;
+}
+#endif // ifndef __SYNTHESIS__
+#endif // ifndef AP_AUTOCC
+
+/* Bit reference.
+   ----------------------------------------------------------------
+*/
+template <int _AP_W, bool _AP_S>
+struct ap_bit_ref {
+  // struct ssdm_int or its sim model.
+  // TODO make it possible to reference to ap_fixed_base/ap_fixed/ap_ufixed
+  //      and then we can retire af_bit_ref.
+  typedef ap_int_base<_AP_W, _AP_S> ref_type;
+  ref_type& d_bv;
+  int d_index;
+
+ public:
+  // copy ctor
+  INLINE ap_bit_ref(const ap_bit_ref<_AP_W, _AP_S>& ref)
+      : d_bv(ref.d_bv), d_index(ref.d_index) {}
+
+  INLINE ap_bit_ref(ref_type* bv, int index = 0) : d_bv(*bv), d_index(index) {}
+
+  INLINE ap_bit_ref(const ref_type* bv, int index = 0)
+      : d_bv(*const_cast<ref_type*>(bv)), d_index(index) {}
+
+  INLINE operator bool() const { return _AP_ROOT_op_get_bit(d_bv.V, d_index); }
+  INLINE bool to_bool() const { return _AP_ROOT_op_get_bit(d_bv.V, d_index); }
+
+  // assign op from hls supported C integral types.
+  // FIXME disabled to support sc_signal<bool>.
+  // NOTE this used to be unsigned long long.
+  //template <typename T>
+  //INLINE typename _ap_type::enable_if<_ap_type::is_integral<T>::value,
+  //                                    ap_bit_ref&>::type
+  //operator=(T val) {
+  //  d_bv.V = _AP_ROOT_op_set_bit(d_bv.V, d_index, val);
+  //  return *this;
+  //}
+#define ASSIGN_WITH_CTYPE(_Tp)                          \
+  INLINE ap_bit_ref& operator=(_Tp val) {               \
+    d_bv.V = _AP_ROOT_op_set_bit(d_bv.V, d_index, val); \
+    return *this;                                       \
+  }
+
+  ASSIGN_WITH_CTYPE(bool)
+  ASSIGN_WITH_CTYPE(char)
+  ASSIGN_WITH_CTYPE(signed char)
+  ASSIGN_WITH_CTYPE(unsigned char)
+  ASSIGN_WITH_CTYPE(short)
+  ASSIGN_WITH_CTYPE(unsigned short)
+  ASSIGN_WITH_CTYPE(int)
+  ASSIGN_WITH_CTYPE(unsigned int)
+  ASSIGN_WITH_CTYPE(long)
+  ASSIGN_WITH_CTYPE(unsigned long)
+  ASSIGN_WITH_CTYPE(ap_slong)
+  ASSIGN_WITH_CTYPE(ap_ulong)
+
+#undef ASSIGN_WITH_CTYPE
+
+#define ASSIGN_WITH_CTYPE_FP(_Tp)                           \
+  INLINE ap_bit_ref& operator=(_Tp val) {                   \
+    bool tmp_val = val;                                     \
+    d_bv.V = _AP_ROOT_op_set_bit(d_bv.V, d_index,tmp_val);  \
+    return *this;                                           \
+  }
+
+#if _AP_ENABLE_HALF_ == 1
+  ASSIGN_WITH_CTYPE_FP(half)
+#endif
+  ASSIGN_WITH_CTYPE_FP(float)
+  ASSIGN_WITH_CTYPE_FP(double)
+
+#undef ASSIGN_WITH_CTYPE_FP
+
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_bit_ref& operator=(const ap_int_base<_AP_W2, _AP_S2>& val) {
+    return operator=((ap_ulong)(val.V != 0));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_bit_ref& operator=(const ap_range_ref<_AP_W2, _AP_S2>& val) {
+    return operator=((ap_int_base<_AP_W2, false>)val);
+  }
+
+  // Be explicit to prevent it from being deleted, as field d_bv
+  // is of reference type.
+  INLINE ap_bit_ref& operator=(const ap_bit_ref& val) {
+    return operator=((ap_ulong)(bool)val);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_bit_ref& operator=(const ap_bit_ref<_AP_W2, _AP_S2>& val) {
+    return operator=((ap_ulong)(bool)val);
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_bit_ref& operator=(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    return operator=((const ap_int_base<_AP_W2, false>)val);
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_bit_ref& operator=(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    return operator=((ap_ulong)(bool)val);
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_bit_ref& operator=(
+      const ap_concat_ref<_AP_W2, _AP_T3, _AP_W3, _AP_T3>& val) {
+    return operator=((const ap_int_base<_AP_W2 + _AP_W3, false>)val);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  operator,(ap_int_base<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >(
+        *this, a2);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  operator,(volatile ap_int_base<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_int_base<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  operator,(const ap_int_base<_AP_W2, _AP_S2> &a2) {
+    ap_int_base<_AP_W2, _AP_S2> op(a2);
+    return ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_int_base<_AP_W2, _AP_S2>&>(op));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >
+  operator,(const volatile ap_int_base<_AP_W2, _AP_S2> &a2) {
+    ap_int_base<_AP_W2, _AP_S2> op(a2);
+    return ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_int_base<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_int_base<_AP_W2, _AP_S2>&>(op));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_range_ref<_AP_W2, _AP_S2> >
+  operator,(const ap_range_ref<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<1, ap_bit_ref, _AP_W2, ap_range_ref<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_range_ref<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_concat_ref<1, ap_bit_ref, 1, ap_bit_ref<_AP_W2, _AP_S2> > operator,(
+      const ap_bit_ref<_AP_W2, _AP_S2> &a2) {
+    return ap_concat_ref<1, ap_bit_ref, 1, ap_bit_ref<_AP_W2, _AP_S2> >(
+        *this, const_cast<ap_bit_ref<_AP_W2, _AP_S2>&>(a2));
+  }
+
+  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+  INLINE ap_concat_ref<1, ap_bit_ref, _AP_W2 + _AP_W3,
+                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+  operator,(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) {
+    return ap_concat_ref<1, ap_bit_ref, _AP_W2 + _AP_W3,
+                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(
+        *this, const_cast<ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>&>(a2));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_concat_ref<
+      1, ap_bit_ref, _AP_W2,
+      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+  operator,(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &a2) {
+    return ap_concat_ref<
+        1, ap_bit_ref, _AP_W2,
+        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+        *this,
+        const_cast<
+            af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(a2));
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE ap_concat_ref<1, ap_bit_ref, 1, af_bit_ref<_AP_W2, _AP_I2, _AP_S2,
+                                                    _AP_Q2, _AP_O2, _AP_N2> >
+  operator,(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &a2) {
+    return ap_concat_ref<1, ap_bit_ref, 1, af_bit_ref<_AP_W2, _AP_I2, _AP_S2,
+                                                      _AP_Q2, _AP_O2, _AP_N2> >(
+        *this,
+        const_cast<af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(
+            a2));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator==(const ap_bit_ref<_AP_W2, _AP_S2>& op) {
+    return get() == op.get();
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator!=(const ap_bit_ref<_AP_W2, _AP_S2>& op) {
+    return get() != op.get();
+  }
+
+  INLINE bool get() const { return _AP_ROOT_op_get_bit(d_bv.V, d_index); }
+
+  INLINE bool get() { return _AP_ROOT_op_get_bit(d_bv.V, d_index); }
+
+  template <int _AP_W3>
+  INLINE void set(const ap_int_base<_AP_W3, false>& val) {
+    operator=(val);
+  }
+
+  INLINE bool operator~() const {
+    bool bit = _AP_ROOT_op_get_bit(d_bv.V, d_index);
+    return bit ? false : true;
+  }
+
+  INLINE int length() const { return 1; }
+
+#ifndef __SYNTHESIS__
+  std::string to_string() const { return get() ? "1" : "0"; }
+#else
+  // XXX HLS will delete this in synthesis
+  INLINE char* to_string() const { return 0; }
+#endif
+}; // struct ap_bit_ref
+
+/* ap_range_ref with int.
+ * ------------------------------------------------------------
+ */
+// equality and relational operators.
+#define REF_REL_OP_WITH_INT(REL_OP, C_TYPE, _AP_W2, _AP_S2)                \
+  template <int _AP_W, bool _AP_S>                                         \
+  INLINE bool operator REL_OP(const ap_range_ref<_AP_W, _AP_S>& op,        \
+                              C_TYPE op2) {                                \
+    return ap_int_base<_AP_W, false>(op)                                   \
+        REL_OP ap_int_base<_AP_W2, _AP_S2>(op2);                           \
+  }                                                                        \
+  template <int _AP_W, bool _AP_S>                                         \
+  INLINE bool operator REL_OP(const ap_bit_ref<_AP_W, _AP_S>& op,          \
+                              C_TYPE op2) {                                \
+    return bool(op) REL_OP op2;                                            \
+  }                                                                        \
+  template <int _AP_W, bool _AP_S>                                         \
+  INLINE bool operator REL_OP(C_TYPE op2,                                  \
+                              const ap_bit_ref<_AP_W, _AP_S>& op) {        \
+    return op2 REL_OP bool(op);                                            \
+  }                                                                        \
+  template <int _AP_W, typename _AP_T, int _AP_W1, typename _AP_T1>        \
+  INLINE bool operator REL_OP(                                             \
+      const ap_concat_ref<_AP_W, _AP_T, _AP_W1, _AP_T1>& op, C_TYPE op2) { \
+    return ap_int_base<_AP_W + _AP_W1, false>(op)                          \
+        REL_OP ap_int_base<_AP_W2, _AP_S2>(op2);                           \
+  }
+
+// Make the line shorter than 5000 chars
+#define REF_REL_WITH_INT_1(C_TYPE, _AP_WI, _AP_SI) \
+  REF_REL_OP_WITH_INT(>, C_TYPE, _AP_WI, _AP_SI)   \
+  REF_REL_OP_WITH_INT(<, C_TYPE, _AP_WI, _AP_SI)   \
+  REF_REL_OP_WITH_INT(>=, C_TYPE, _AP_WI, _AP_SI)  \
+  REF_REL_OP_WITH_INT(<=, C_TYPE, _AP_WI, _AP_SI)
+
+REF_REL_WITH_INT_1(bool, 1, false)
+REF_REL_WITH_INT_1(char, 8, CHAR_IS_SIGNED)
+REF_REL_WITH_INT_1(signed char, 8, true)
+REF_REL_WITH_INT_1(unsigned char, 8, false)
+REF_REL_WITH_INT_1(short, _AP_SIZE_short, true)
+REF_REL_WITH_INT_1(unsigned short, _AP_SIZE_short, false)
+REF_REL_WITH_INT_1(int, _AP_SIZE_int, true)
+REF_REL_WITH_INT_1(unsigned int, _AP_SIZE_int, false)
+REF_REL_WITH_INT_1(long, _AP_SIZE_long, true)
+REF_REL_WITH_INT_1(unsigned long, _AP_SIZE_long, false)
+REF_REL_WITH_INT_1(ap_slong, _AP_SIZE_ap_slong, true)
+REF_REL_WITH_INT_1(ap_ulong, _AP_SIZE_ap_slong, false)
+
+// Make the line shorter than 5000 chars
+#define REF_REL_WITH_INT_2(C_TYPE, _AP_WI, _AP_SI) \
+  REF_REL_OP_WITH_INT(==, C_TYPE, _AP_WI, _AP_SI)  \
+  REF_REL_OP_WITH_INT(!=, C_TYPE, _AP_WI, _AP_SI)
+
+REF_REL_WITH_INT_2(bool, 1, false)
+REF_REL_WITH_INT_2(char, 8, CHAR_IS_SIGNED)
+REF_REL_WITH_INT_2(signed char, 8, true)
+REF_REL_WITH_INT_2(unsigned char, 8, false)
+REF_REL_WITH_INT_2(short, _AP_SIZE_short, true)
+REF_REL_WITH_INT_2(unsigned short, _AP_SIZE_short, false)
+REF_REL_WITH_INT_2(int, _AP_SIZE_int, true)
+REF_REL_WITH_INT_2(unsigned int, _AP_SIZE_int, false)
+REF_REL_WITH_INT_2(long, _AP_SIZE_long, true)
+REF_REL_WITH_INT_2(unsigned long, _AP_SIZE_long, false)
+REF_REL_WITH_INT_2(ap_slong, _AP_SIZE_ap_slong, true)
+REF_REL_WITH_INT_2(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef REF_REL_OP_WITH_INT
+#undef REF_REL_WITH_INT_1
+#undef REF_REL_WITH_INT_2
+
+#define REF_BIN_OP_WITH_INT(BIN_OP, RTYPE, C_TYPE, _AP_W2, _AP_S2)          \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE typename ap_int_base<_AP_W, false>::template RType<_AP_W2,         \
+                                                            _AP_S2>::RTYPE  \
+  operator BIN_OP(const ap_range_ref<_AP_W, _AP_S>& op, C_TYPE op2) {       \
+    return ap_int_base<_AP_W, false>(op)                                    \
+        BIN_OP ap_int_base<_AP_W2, _AP_S2>(op2);                            \
+  }                                                                         \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE typename ap_int_base<_AP_W2, _AP_S2>::template RType<_AP_W,        \
+                                                              false>::RTYPE \
+  operator BIN_OP(C_TYPE op2, const ap_range_ref<_AP_W, _AP_S>& op) {       \
+    return ap_int_base<_AP_W2, _AP_S2>(op2)                                 \
+        BIN_OP ap_int_base<_AP_W, false>(op);                               \
+  }
+
+// arithmetic operators.
+#define REF_BIN_OP_WITH_INT_ARITH(C_TYPE, _AP_W2, _AP_S2)   \
+  REF_BIN_OP_WITH_INT(+, plus, C_TYPE, (_AP_W2), (_AP_S2))  \
+  REF_BIN_OP_WITH_INT(-, minus, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_BIN_OP_WITH_INT(*, mult, C_TYPE, (_AP_W2), (_AP_S2))  \
+  REF_BIN_OP_WITH_INT(/, div, C_TYPE, (_AP_W2), (_AP_S2))   \
+  REF_BIN_OP_WITH_INT(%, mod, C_TYPE, (_AP_W2), (_AP_S2))
+
+REF_BIN_OP_WITH_INT_ARITH(bool, 1, false)
+REF_BIN_OP_WITH_INT_ARITH(char, 8, CHAR_IS_SIGNED)
+REF_BIN_OP_WITH_INT_ARITH(signed char, 8, true)
+REF_BIN_OP_WITH_INT_ARITH(unsigned char, 8, false)
+REF_BIN_OP_WITH_INT_ARITH(short, _AP_SIZE_short, true)
+REF_BIN_OP_WITH_INT_ARITH(unsigned short, _AP_SIZE_short, false)
+REF_BIN_OP_WITH_INT_ARITH(int, _AP_SIZE_int, true)
+REF_BIN_OP_WITH_INT_ARITH(unsigned int, _AP_SIZE_int, false)
+REF_BIN_OP_WITH_INT_ARITH(long, _AP_SIZE_long, true)
+REF_BIN_OP_WITH_INT_ARITH(unsigned long, _AP_SIZE_long, false)
+REF_BIN_OP_WITH_INT_ARITH(ap_slong, _AP_SIZE_ap_slong, true)
+REF_BIN_OP_WITH_INT_ARITH(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef REF_BIN_OP_WITH_INT_ARITH
+
+// bitwise and shift operators
+#define REF_BIN_OP_WITH_INT_BITS(C_TYPE, _AP_W2, _AP_S2)     \
+  REF_BIN_OP_WITH_INT(&, logic, C_TYPE, (_AP_W2), (_AP_S2))  \
+  REF_BIN_OP_WITH_INT(|, logic, C_TYPE, (_AP_W2), (_AP_S2))  \
+  REF_BIN_OP_WITH_INT(^, logic, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_BIN_OP_WITH_INT(>>, arg1, C_TYPE, (_AP_W2), (_AP_S2))  \
+  REF_BIN_OP_WITH_INT(<<, arg1, C_TYPE, (_AP_W2), (_AP_S2))
+
+REF_BIN_OP_WITH_INT_BITS(bool, 1, false)
+REF_BIN_OP_WITH_INT_BITS(char, 8, CHAR_IS_SIGNED)
+REF_BIN_OP_WITH_INT_BITS(signed char, 8, true)
+REF_BIN_OP_WITH_INT_BITS(unsigned char, 8, false)
+REF_BIN_OP_WITH_INT_BITS(short, _AP_SIZE_short, true)
+REF_BIN_OP_WITH_INT_BITS(unsigned short, _AP_SIZE_short, false)
+REF_BIN_OP_WITH_INT_BITS(int, _AP_SIZE_int, true)
+REF_BIN_OP_WITH_INT_BITS(unsigned int, _AP_SIZE_int, false)
+REF_BIN_OP_WITH_INT_BITS(long, _AP_SIZE_long, true)
+REF_BIN_OP_WITH_INT_BITS(unsigned long, _AP_SIZE_long, false)
+REF_BIN_OP_WITH_INT_BITS(ap_slong, _AP_SIZE_ap_slong, true)
+REF_BIN_OP_WITH_INT_BITS(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef REF_BIN_OP_WITH_INT_BITS
+
+/* ap_range_ref with ap_range_ref
+ *  ------------------------------------------------------------
+ */
+#define REF_BIN_OP(BIN_OP, RTYPE)                                              \
+  template <int _AP_W, bool _AP_S, int _AP_W2, bool _AP_S2>                    \
+  INLINE                                                                       \
+      typename ap_int_base<_AP_W, false>::template RType<_AP_W2, false>::RTYPE \
+      operator BIN_OP(const ap_range_ref<_AP_W, _AP_S>& lhs,                   \
+                      const ap_range_ref<_AP_W2, _AP_S2>& rhs) {               \
+    return (lhs.operator ap_int_base<_AP_W, false>())BIN_OP(                   \
+        rhs.operator ap_int_base<_AP_W2, false>());                            \
+  }
+
+REF_BIN_OP(+, plus)
+REF_BIN_OP(-, minus)
+REF_BIN_OP(*, mult)
+REF_BIN_OP(/, div)
+REF_BIN_OP(%, mod)
+REF_BIN_OP(&, logic)
+REF_BIN_OP(|, logic)
+REF_BIN_OP(^, logic)
+REF_BIN_OP(>>, arg1)
+REF_BIN_OP(<<, arg1)
+
+/* ap_concat_ref with ap_concat_ref.
+ *  ------------------------------------------------------------
+ */
+
+//************************************************************************
+//  Implement
+//      ap_int_base<M+N> = ap_concat_ref<M> OP ap_concat_ref<N>
+//  for operators  +, -, *, /, %, >>, <<, &, |, ^
+//  Without these operators the operands are converted to int64 and
+//  larger results lose informations (higher order bits).
+//
+//                       operand OP
+//                      /          |
+//              left-concat         right-concat
+//                /     |            /         |
+//         <LW1,LT1>  <LW2,LT2>   <RW1,RT1>    <RW2,RT2>
+//
+//      _AP_LW1, _AP_LT1 (width and type of left-concat's left side)
+//      _AP_LW2, _AP_LT2 (width and type of left-concat's right side)
+//  Similarly for RHS of operand OP: _AP_RW1, AP_RW2, _AP_RT1, _AP_RT2
+//
+//  In Verilog 2001 result of concatenation is always unsigned even
+//  when both sides are signed.
+//************************************************************************
+
+#undef SYN_CONCAT_REF_BIN_OP
+
+#define SYN_CONCAT_REF_BIN_OP(BIN_OP, RTYPE)                              \
+  template <int _AP_LW1, typename _AP_LT1, int _AP_LW2, typename _AP_LT2, \
+            int _AP_RW1, typename _AP_RT1, int _AP_RW2, typename _AP_RT2> \
+  INLINE typename ap_int_base<_AP_LW1 + _AP_LW2, false>::template RType<  \
+      _AP_RW1 + _AP_RW2, false>::RTYPE                                    \
+  operator BIN_OP(                                                        \
+      const ap_concat_ref<_AP_LW1, _AP_LT1, _AP_LW2, _AP_LT2>& lhs,       \
+      const ap_concat_ref<_AP_RW1, _AP_RT1, _AP_RW2, _AP_RT2>& rhs) {     \
+    return lhs.get() BIN_OP rhs.get();                                    \
+  }
+
+SYN_CONCAT_REF_BIN_OP(+, plus)
+SYN_CONCAT_REF_BIN_OP(-, minus)
+SYN_CONCAT_REF_BIN_OP(*, mult)
+SYN_CONCAT_REF_BIN_OP(/, div)
+SYN_CONCAT_REF_BIN_OP(%, mod)
+SYN_CONCAT_REF_BIN_OP(&, logic)
+SYN_CONCAT_REF_BIN_OP(|, logic)
+SYN_CONCAT_REF_BIN_OP(^, logic)
+SYN_CONCAT_REF_BIN_OP(>>, arg1)
+SYN_CONCAT_REF_BIN_OP(<<, arg1)
+
+#undef SYN_CONCAT_REF_BIN_OP
+
+#define CONCAT_OP_WITH_INT(C_TYPE, _AP_WI, _AP_SI)                          \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE ap_int_base<_AP_W + _AP_WI, false> operator,(                      \
+      const ap_int_base<_AP_W, _AP_S> &op1, C_TYPE op2) {                   \
+    ap_int_base<_AP_WI + _AP_W, false> val(op2);                            \
+    ap_int_base<_AP_WI + _AP_W, false> ret(op1);                            \
+    ret <<= _AP_WI;                                                         \
+    if (_AP_SI) {                                                           \
+      val <<= _AP_W;                                                        \
+      val >>= _AP_W;                                                        \
+    }                                                                       \
+    ret |= val;                                                             \
+    return ret;                                                             \
+  }                                                                         \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE ap_int_base<_AP_W + _AP_WI, false> operator,(                      \
+      C_TYPE op1, const ap_int_base<_AP_W, _AP_S> &op2) {                   \
+    ap_int_base<_AP_WI + _AP_W, false> val(op1);                            \
+    ap_int_base<_AP_WI + _AP_W, false> ret(op2);                            \
+    if (_AP_S) {                                                            \
+      ret <<= _AP_WI;                                                       \
+      ret >>= _AP_WI;                                                       \
+    }                                                                       \
+    ret |= val << _AP_W;                                                    \
+    return ret;                                                             \
+  }                                                                         \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE ap_int_base<_AP_W + _AP_WI, false> operator,(                      \
+      const ap_range_ref<_AP_W, _AP_S> &op1, C_TYPE op2) {                  \
+    ap_int_base<_AP_WI + _AP_W, false> val(op2);                            \
+    ap_int_base<_AP_WI + _AP_W, false> ret(op1);                            \
+    ret <<= _AP_WI;                                                         \
+    if (_AP_SI) {                                                           \
+      val <<= _AP_W;                                                        \
+      val >>= _AP_W;                                                        \
+    }                                                                       \
+    ret |= val;                                                             \
+    return ret;                                                             \
+  }                                                                         \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE ap_int_base<_AP_W + _AP_WI, false> operator,(                      \
+      C_TYPE op1, const ap_range_ref<_AP_W, _AP_S> &op2) {                  \
+    ap_int_base<_AP_WI + _AP_W, false> val(op1);                            \
+    ap_int_base<_AP_WI + _AP_W, false> ret(op2);                            \
+    int len = op2.length();                                                 \
+    val <<= len;                                                            \
+    ret |= val;                                                             \
+    return ret;                                                             \
+  }                                                                         \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE ap_int_base<_AP_WI + 1, false> operator,(                          \
+      const ap_bit_ref<_AP_W, _AP_S> &op1, C_TYPE op2) {                    \
+    ap_int_base<_AP_WI + 1, false> val(op2);                                \
+    val[_AP_WI] = op1;                                                      \
+    return val;                                                             \
+  }                                                                         \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE ap_int_base<_AP_WI + 1, false> operator,(                          \
+      C_TYPE op1, const ap_bit_ref<_AP_W, _AP_S> &op2) {                    \
+    ap_int_base<_AP_WI + 1, false> val(op1);                                \
+    val <<= 1;                                                              \
+    val[0] = op2;                                                           \
+    return val;                                                             \
+  }                                                                         \
+  template <int _AP_W, typename _AP_T, int _AP_W2, typename _AP_T2>         \
+  INLINE ap_int_base<_AP_W + _AP_W2 + _AP_WI, false> operator,(             \
+      const ap_concat_ref<_AP_W, _AP_T, _AP_W2, _AP_T2> &op1, C_TYPE op2) { \
+    ap_int_base<_AP_WI + _AP_W + _AP_W2, _AP_SI> val(op2);                  \
+    ap_int_base<_AP_WI + _AP_W + _AP_W2, _AP_SI> ret(op1);                  \
+    if (_AP_SI) {                                                           \
+      val <<= _AP_W + _AP_W2;                                               \
+      val >>= _AP_W + _AP_W2;                                               \
+    }                                                                       \
+    ret <<= _AP_WI;                                                         \
+    ret |= val;                                                             \
+    return ret;                                                             \
+  }                                                                         \
+  template <int _AP_W, typename _AP_T, int _AP_W2, typename _AP_T2>         \
+  INLINE ap_int_base<_AP_W + _AP_W2 + _AP_WI, false> operator,(             \
+      C_TYPE op1, const ap_concat_ref<_AP_W, _AP_T, _AP_W2, _AP_T2> &op2) { \
+    ap_int_base<_AP_WI + _AP_W + _AP_W2, _AP_SI> val(op1);                  \
+    ap_int_base<_AP_WI + _AP_W + _AP_W2, _AP_SI> ret(op2);                  \
+    int len = op2.length();                                                 \
+    val <<= len;                                                            \
+    ret |= val;                                                             \
+    return ret;                                                             \
+  }                                                                         \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N>                                     \
+  INLINE ap_int_base<_AP_W + _AP_WI, false> operator,(                      \
+      const af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> &op1,    \
+      C_TYPE op2) {                                                         \
+    ap_int_base<_AP_WI + _AP_W, false> val(op2);                            \
+    ap_int_base<_AP_WI + _AP_W, false> ret(op1);                            \
+    if (_AP_SI) {                                                           \
+      val <<= _AP_W;                                                        \
+      val >>= _AP_W;                                                        \
+    }                                                                       \
+    ret <<= _AP_WI;                                                         \
+    ret |= val;                                                             \
+    return ret;                                                             \
+  }                                                                         \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N>                                     \
+  INLINE ap_int_base<_AP_W + _AP_WI, false> operator,(                      \
+      C_TYPE op1,                                                           \
+      const af_range_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> &op2) {  \
+    ap_int_base<_AP_WI + _AP_W, false> val(op1);                            \
+    ap_int_base<_AP_WI + _AP_W, false> ret(op2);                            \
+    int len = op2.length();                                                 \
+    val <<= len;                                                            \
+    ret |= val;                                                             \
+    return ret;                                                             \
+  }                                                                         \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N>                                     \
+  INLINE ap_int_base<1 + _AP_WI, false> operator,(                          \
+      const af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> &op1,      \
+      C_TYPE op2) {                                                         \
+    ap_int_base<_AP_WI + 1, _AP_SI> val(op2);                               \
+    val[_AP_WI] = op1;                                                      \
+    return val;                                                             \
+  }                                                                         \
+  template <int _AP_W, int _AP_I, bool _AP_S, ap_q_mode _AP_Q,              \
+            ap_o_mode _AP_O, int _AP_N>                                     \
+  INLINE ap_int_base<1 + _AP_WI, false> operator,(                          \
+      C_TYPE op1,                                                           \
+      const af_bit_ref<_AP_W, _AP_I, _AP_S, _AP_Q, _AP_O, _AP_N> &op2) {    \
+    ap_int_base<_AP_WI + 1, _AP_SI> val(op1);                               \
+    val <<= 1;                                                              \
+    val[0] = op2;                                                           \
+    return val;                                                             \
+  }
+
+CONCAT_OP_WITH_INT(bool, 1, false)
+CONCAT_OP_WITH_INT(char, 8, CHAR_IS_SIGNED)
+CONCAT_OP_WITH_INT(signed char, 8, true)
+CONCAT_OP_WITH_INT(unsigned char, 8, false)
+CONCAT_OP_WITH_INT(short, _AP_SIZE_short, true)
+CONCAT_OP_WITH_INT(unsigned short, _AP_SIZE_short, false)
+CONCAT_OP_WITH_INT(int, _AP_SIZE_int, true)
+CONCAT_OP_WITH_INT(unsigned int, _AP_SIZE_int, false)
+CONCAT_OP_WITH_INT(long, _AP_SIZE_long, true)
+CONCAT_OP_WITH_INT(unsigned long, _AP_SIZE_long, false)
+CONCAT_OP_WITH_INT(ap_slong, _AP_SIZE_ap_slong, true)
+CONCAT_OP_WITH_INT(ap_ulong, _AP_SIZE_ap_slong, false)
+
+#undef CONCAT_OP_WITH_INT
+
+#define CONCAT_SHIFT_WITH_INT(C_TYPE, OP)                                  \
+  template <int _AP_W, typename _AP_T, int _AP_W1, typename _AP_T1>        \
+  INLINE ap_uint<_AP_W + _AP_W1> operator OP(                              \
+      const ap_concat_ref<_AP_W, _AP_T, _AP_W1, _AP_T1> lhs, C_TYPE rhs) { \
+    return ap_uint<_AP_W + _AP_W1>(lhs).get() OP int(rhs);                 \
+  }
+
+// FIXME int(rhs) may loose precision.
+
+CONCAT_SHIFT_WITH_INT(int, <<)
+CONCAT_SHIFT_WITH_INT(unsigned int, <<)
+CONCAT_SHIFT_WITH_INT(long, <<)
+CONCAT_SHIFT_WITH_INT(unsigned long, <<)
+CONCAT_SHIFT_WITH_INT(ap_slong, <<)
+CONCAT_SHIFT_WITH_INT(ap_ulong, <<)
+
+CONCAT_SHIFT_WITH_INT(int, >>)
+CONCAT_SHIFT_WITH_INT(unsigned int, >>)
+CONCAT_SHIFT_WITH_INT(long, >>)
+CONCAT_SHIFT_WITH_INT(unsigned long, >>)
+CONCAT_SHIFT_WITH_INT(ap_slong, >>)
+CONCAT_SHIFT_WITH_INT(ap_ulong, >>)
+
+#endif // ifndef __cplusplus
+#endif // ifndef __AP_INT_REF_H__
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_int_special.h b/model_1/hls4ml_prj/firmware/ap_types/ap_int_special.h
new file mode 100644
index 0000000..3afc619
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_int_special.h
@@ -0,0 +1,223 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_INT_SPECIAL_H__
+#define __AP_INT_SPECIAL_H__
+
+#ifndef __AP_INT_H__
+#error "Only ap_fixed.h and ap_int.h can be included directly in user code."
+#endif
+
+#ifndef __SYNTHESIS__
+#include <cstdio>
+#include <cstdlib>
+#endif
+// FIXME AP_AUTOCC cannot handle many standard headers, so declare instead of
+// include.
+// #include <complex>
+namespace std {
+template<typename _Tp> class complex;
+}
+
+/*
+  TODO: Modernize the code using C++11/C++14
+  1. constexpr http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2016/p0415r0.html
+  2. move constructor
+*/
+
+namespace std {
+/*
+   Specialize std::complex<ap_int> to zero initialization ap_int.
+
+   To reduce the area cost, ap_int is not zero initialized, just like basic
+   types float or double. However, libstdc++ provides specialization for float,
+   double and long double, initializing image part to 0 when not specified.
+
+   This has become a difficulty in switching legacy code from these C types to
+   ap_int. To ease the tranform of legacy code, we have to implement
+   specialization of std::complex<> for our type.
+
+   As ap_int is a template, it is impossible to specialize only the methods
+   that causes default initialization of value type in std::complex<>. An
+   explicit full specialization of the template class has to be done, covering
+   all the member functions and operators of std::complex<> as specified
+   in standard 26.2.4 and 26.2.5.
+*/
+template <int _AP_W>
+class complex<ap_int<_AP_W> > {
+ public:
+  typedef ap_int<_AP_W> _Tp;
+  typedef _Tp value_type;
+
+  // 26.2.4/1
+  // Constructor without argument
+  // Default initialize, so that in dataflow, the variable is only written once.
+  complex() : _M_real(_Tp()), _M_imag(_Tp()) {}
+  // Constructor with ap_int.
+  // Zero initialize image part when not specified, so that `C(1) == C(1,0)`
+  complex(const _Tp &__r, const _Tp &__i = _Tp(0))
+      : _M_real(__r), _M_imag(__i) {}
+
+  // Constructor with another complex number
+  template <typename _Up>
+  complex(const complex<_Up> &__z) : _M_real(__z.real()), _M_imag(__z.imag()) {}
+
+#if __cplusplus >= 201103L
+  const _Tp& real() const { return _M_real; }
+  const _Tp& imag() const { return _M_imag; }
+#else
+  _Tp& real() { return _M_real; }
+  const _Tp& real() const { return _M_real; }
+  _Tp& imag() { return _M_imag; }
+  const _Tp& imag() const { return _M_imag; }
+#endif
+
+  void real(_Tp __val) { _M_real = __val; }
+
+  void imag(_Tp __val) { _M_imag = __val; }
+
+  // Assign this complex number with ap_int.
+  // Zero initialize image poarrt, so that `C c; c = 1; c == C(1,0);`
+  complex<_Tp> &operator=(const _Tp __t) {
+    _M_real = __t;
+    _M_imag = _Tp(0);
+    return *this;
+  }
+
+  // 26.2.5/1
+  // Add ap_int to this complex number.
+  complex<_Tp> &operator+=(const _Tp &__t) {
+    _M_real += __t;
+    return *this;
+  }
+
+  // 26.2.5/3
+  // Subtract ap_int from this complex number.
+  complex<_Tp> &operator-=(const _Tp &__t) {
+    _M_real -= __t;
+    return *this;
+  }
+
+  // 26.2.5/5
+  // Multiply this complex number by ap_int.
+  complex<_Tp> &operator*=(const _Tp &__t) {
+    _M_real *= __t;
+    _M_imag *= __t;
+    return *this;
+  }
+
+  // 26.2.5/7
+  // Divide this complex number by ap_int.
+  complex<_Tp> &operator/=(const _Tp &__t) {
+    _M_real /= __t;
+    _M_imag /= __t;
+    return *this;
+  }
+
+  // Assign complex number to this complex number.
+  template <typename _Up>
+  complex<_Tp> &operator=(const complex<_Up> &__z) {
+    _M_real = __z.real();
+    _M_imag = __z.imag();
+    return *this;
+  }
+
+  // 26.2.5/9
+  // Add complex number to this.
+  template <typename _Up>
+  complex<_Tp> &operator+=(const complex<_Up> &__z) {
+    _M_real += __z.real();
+    _M_imag += __z.imag();
+    return *this;
+  }
+
+  // 26.2.5/11
+  // Subtract complex number from this.
+  template <typename _Up>
+  complex<_Tp> &operator-=(const complex<_Up> &__z) {
+    _M_real -= __z.real();
+    _M_imag -= __z.imag();
+    return *this;
+  }
+
+  // 26.2.5/13
+  // Multiply this by complex number.
+  template <typename _Up>
+  complex<_Tp> &operator*=(const complex<_Up> &__z) {
+    const _Tp __r = _M_real * __z.real() - _M_imag * __z.imag();
+    _M_imag = _M_real * __z.imag() + _M_imag * __z.real();
+    _M_real = __r;
+    return *this;
+  }
+
+  // 26.2.5/15
+  // Divide this by complex number.
+  template <typename _Up>
+  complex<_Tp> &operator/=(const complex<_Up> &__z) {
+    complex<_Tp> cj (__z.real(), -__z.imag());
+    complex<_Tp> a = (*this) * cj;
+    complex<_Tp> b = cj * __z;
+    _M_real = a.real() / b.real();
+    _M_imag = a.imag() / b.real();
+    return *this;
+  }
+
+ private:
+  _Tp _M_real;
+  _Tp _M_imag;
+
+}; // class complex<ap_int<_AP_W> >
+
+
+/*
+   Non-member operations
+   These operations are not required by standard in 26.2.6, but libstdc++
+   defines them for
+   float, double or long double's specialization.
+*/
+// Compare complex number with ap_int.
+template <int _AP_W>
+inline bool operator==(const complex<ap_int<_AP_W> > &__x, const ap_int<_AP_W> &__y) {
+  return __x.real() == __y &&
+         __x.imag() == 0;
+}
+
+// Compare ap_int with complex number.
+template <int _AP_W>
+inline bool operator==(const ap_int<_AP_W> &__x, const complex<ap_int<_AP_W> > &__y) {
+  return __x == __y.real() &&
+         0 == __y.imag();
+}
+
+// Compare complex number with ap_int.
+template <int _AP_W>
+inline bool operator!=(const complex<ap_int<_AP_W> > &__x, const ap_int<_AP_W> &__y) {
+  return __x.real() != __y ||
+         __x.imag() != 0;
+}
+
+// Compare ap_int with complex number.
+template <int _AP_W>
+inline bool operator!=(const ap_int<_AP_W> &__x, const complex<ap_int<_AP_W> > &__y) {
+  return __x != __y.real() ||
+         0 != __y.imag();
+}
+
+}  // namespace std
+
+#endif  // ifndef __AP_INT_SPECIAL_H__
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/ap_shift_reg.h b/model_1/hls4ml_prj/firmware/ap_types/ap_shift_reg.h
new file mode 100644
index 0000000..94dba51
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/ap_shift_reg.h
@@ -0,0 +1,138 @@
+/*
+#-  (c) Copyright 2011-2019 Xilinx, Inc. All rights reserved.
+#-
+#-  This file contains confidential and proprietary information
+#-  of Xilinx, Inc. and is protected under U.S. and
+#-  international copyright and other intellectual property
+#-  laws.
+#-
+#-  DISCLAIMER
+#-  This disclaimer is not a license and does not grant any
+#-  rights to the materials distributed herewith. Except as
+#-  otherwise provided in a valid license issued to you by
+#-  Xilinx, and to the maximum extent permitted by applicable
+#-  law: (1) THESE MATERIALS ARE MADE AVAILABLE "AS IS" AND
+#-  WITH ALL FAULTS, AND XILINX HEREBY DISCLAIMS ALL WARRANTIES
+#-  AND CONDITIONS, EXPRESS, IMPLIED, OR STATUTORY, INCLUDING
+#-  BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, NON-
+#-  INFRINGEMENT, OR FITNESS FOR ANY PARTICULAR PURPOSE; and
+#-  (2) Xilinx shall not be liable (whether in contract or tort,
+#-  including negligence, or under any other theory of
+#-  liability) for any loss or damage of any kind or nature
+#-  related to, arising under or in connection with these
+#-  materials, including for any direct, or any indirect,
+#-  special, incidental, or consequential loss or damage
+#-  (including loss of data, profits, goodwill, or any type of
+#-  loss or damage suffered as a result of any action brought
+#-  by a third party) even if such damage or loss was
+#-  reasonably foreseeable or Xilinx had been advised of the
+#-  possibility of the same.
+#-
+#-  CRITICAL APPLICATIONS
+#-  Xilinx products are not designed or intended to be fail-
+#-  safe, or for use in any application requiring fail-safe
+#-  performance, such as life-support or safety devices or
+#-  systems, Class III medical devices, nuclear facilities,
+#-  applications related to the deployment of airbags, or any
+#-  other applications that could lead to death, personal
+#-  injury, or severe property or environmental damage
+#-  (individually and collectively, "Critical
+#-  Applications"). Customer assumes the sole risk and
+#-  liability of any use of Xilinx products in Critical
+#-  Applications, subject only to applicable laws and
+#-  regulations governing limitations on product liability.
+#-
+#-  THIS COPYRIGHT NOTICE AND DISCLAIMER MUST BE RETAINED AS
+#-  PART OF THIS FILE AT ALL TIMES. 
+#- ************************************************************************
+
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+*/
+
+#ifndef __SIM_AP_SHIFT_REG_H__
+#define __SIM_AP_SHIFT_REG_H__
+
+
+/*
+ * This file contains a C++ model of shift register.
+ * It defines C level simulation model.
+ */
+#ifndef __cplusplus
+#error C++ is required to include this header file
+#else
+
+#include <cassert>
+
+//////////////////////////////////////////////
+// C level simulation model for ap_shift_reg
+//////////////////////////////////////////////
+template<typename __SHIFT_T__, unsigned int __SHIFT_DEPTH__ = 32>
+class ap_shift_reg
+{
+  public:
+    /// Constructors
+    ap_shift_reg() { }
+    ap_shift_reg(const char* name) { }
+    /// Destructor
+    virtual ~ap_shift_reg() { }
+
+  private:
+    /// Make copy constructor and assignment operator private
+    ap_shift_reg(const ap_shift_reg< __SHIFT_T__, __SHIFT_DEPTH__ >& shreg)
+    {
+        for (unsigned i = 0; i < __SHIFT_DEPTH__; ++i)
+            Array[i] = shreg.Array[i];
+    }
+
+    ap_shift_reg& operator = (const ap_shift_reg< __SHIFT_T__,
+        __SHIFT_DEPTH__ >& shreg)
+    {
+        for (unsigned i = 0; i < __SHIFT_DEPTH__; ++i)
+            Array[i] = shreg.Array[i];
+        return *this;
+    }
+
+  public:
+    // Shift the queue, push to back and read from a given address.
+    __SHIFT_T__ shift(__SHIFT_T__ DataIn,
+        unsigned int Addr = __SHIFT_DEPTH__ - 1, bool Enable = true)
+    {
+        assert(Addr < __SHIFT_DEPTH__ &&
+            "Out-of-bound shift is found in ap_shift_reg.");
+        __SHIFT_T__ ret = Array[Addr];
+        if (Enable) {
+            for (unsigned int i = __SHIFT_DEPTH__ - 1; i > 0; --i)
+                Array[i] = Array[i-1];
+            Array[0] = DataIn;
+        }
+        return ret;
+    }
+
+    // Read from a given address.
+    __SHIFT_T__ read(unsigned int Addr = __SHIFT_DEPTH__ - 1) const
+    {
+        assert(Addr < __SHIFT_DEPTH__ &&
+            "Out-of-bound read is found in ap_shift_reg.");
+        return Array[Addr];
+    }
+
+  protected:
+    __SHIFT_T__ Array[__SHIFT_DEPTH__];
+};
+
+#endif //__cplusplus
+
+#endif //__SIM_AP_SHIFT_REG_H__
+
+
diff --git a/model_1/hls4ml_prj/firmware/ap_types/etc/ap_private.h b/model_1/hls4ml_prj/firmware/ap_types/etc/ap_private.h
new file mode 100644
index 0000000..0c29a0a
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/etc/ap_private.h
@@ -0,0 +1,7199 @@
+/*
+ * Copyright 2011-2019 Xilinx, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef __AP_PRIVATE_H__
+#define __AP_PRIVATE_H__
+
+// common macros and type declarations are now defined in ap_common.h, and
+// ap_private becomes part of it.
+#ifndef __AP_COMMON_H__
+#error "etc/ap_private.h cannot be included directly."
+#endif
+
+// forward declarations
+//template <int _AP_W, bool _AP_S, bool _AP_C = _AP_W <= 64>
+//class ap_private; // moved to ap_common.h
+template <int _AP_W, bool _AP_S>
+struct _private_range_ref;
+template <int _AP_W, bool _AP_S>
+struct _private_bit_ref;
+
+// TODO clean up this part.
+#ifndef LLVM_SUPPORT_MATHEXTRAS_H
+#define LLVM_SUPPORT_MATHEXTRAS_H
+
+#ifdef _MSC_VER
+#if _MSC_VER <= 1500
+typedef __int8 int8_t;
+typedef unsigned __int8 uint8_t;
+typedef __int16 int16_t;
+typedef unsigned __int16 uint16_t;
+typedef __int32 int32_t;
+typedef unsigned __int32 uint32_t;
+typedef __int64 int64_t;
+typedef unsigned __int64 uint64_t;
+#else
+#include <stdint.h>
+#endif
+#else
+#include <stdint.h>
+#endif
+
+#ifndef INLINE
+#define INLINE inline
+// Enable to debug ap_int/ap_fixed
+// #define INLINE  __attribute__((weak))
+#endif
+
+// NOTE: The following support functions use the _32/_64 extensions instead of
+// type overloading so that signed and unsigned integers can be used without
+// ambiguity.
+namespace AESL_std {
+template <class DataType>
+DataType INLINE min(DataType a, DataType b) {
+  return (a >= b) ? b : a;
+}
+
+template <class DataType>
+DataType INLINE max(DataType a, DataType b) {
+  return (a >= b) ? a : b;
+}
+} // namespace AESL_std
+
+// TODO clean up included headers.
+#include <math.h>
+#include <stdio.h>
+#include <cassert>
+#include <cstdlib>
+#include <cstring>
+#include <iomanip>
+#include <limits>
+#include <sstream>
+#include <string>
+
+namespace ap_private_ops {
+/// Hi_32 - This function returns the high 32 bits of a 64 bit value.
+static INLINE uint32_t Hi_32(uint64_t Value) {
+  return static_cast<uint32_t>(Value >> 32);
+}
+
+/// Lo_32 - This function returns the low 32 bits of a 64 bit value.
+static INLINE uint32_t Lo_32(uint64_t Value) {
+  return static_cast<uint32_t>(Value);
+}
+
+template <int _AP_W>
+INLINE bool isNegative(const ap_private<_AP_W, false>& a) {
+  return false;
+}
+
+template <int _AP_W>
+INLINE bool isNegative(const ap_private<_AP_W, true>& a) {
+  enum {
+    APINT_BITS_PER_WORD = 64,
+    _AP_N = (_AP_W + APINT_BITS_PER_WORD - 1) / APINT_BITS_PER_WORD
+  };
+  static const uint64_t sign_mask = 1ULL << ((_AP_W - 1) % APINT_BITS_PER_WORD);
+  return (sign_mask & a.get_pVal(_AP_N - 1)) != 0;
+}
+
+/// CountLeadingZeros_32 - this function performs the platform optimal form of
+/// counting the number of zeros from the most significant bit to the first one
+/// bit.  Ex. CountLeadingZeros_32(0x00F000FF) == 8.
+/// Returns 32 if the word is zero.
+static INLINE unsigned CountLeadingZeros_32(uint32_t Value) {
+  unsigned Count; // result
+#if __GNUC__ >= 4
+// PowerPC is defined for __builtin_clz(0)
+#if !defined(__ppc__) && !defined(__ppc64__)
+  if (Value == 0) return 32;
+#endif
+  Count = __builtin_clz(Value);
+#else
+  if (Value == 0) return 32;
+  Count = 0;
+  // bisecton method for count leading zeros
+  for (unsigned Shift = 32 >> 1; Shift; Shift >>= 1) {
+    uint32_t Tmp = (Value) >> (Shift);
+    if (Tmp) {
+      Value = Tmp;
+    } else {
+      Count |= Shift;
+    }
+  }
+#endif
+  return Count;
+}
+
+/// CountLeadingZeros_64 - This function performs the platform optimal form
+/// of counting the number of zeros from the most significant bit to the first
+/// one bit (64 bit edition.)
+/// Returns 64 if the word is zero.
+static INLINE unsigned CountLeadingZeros_64(uint64_t Value) {
+  unsigned Count; // result
+#if __GNUC__ >= 4
+// PowerPC is defined for __builtin_clzll(0)
+#if !defined(__ppc__) && !defined(__ppc64__)
+  if (!Value) return 64;
+#endif
+  Count = __builtin_clzll(Value);
+#else
+  if (sizeof(long) == sizeof(int64_t)) {
+    if (!Value) return 64;
+    Count = 0;
+    // bisecton method for count leading zeros
+    for (unsigned Shift = 64 >> 1; Shift; Shift >>= 1) {
+      uint64_t Tmp = (Value) >> (Shift);
+      if (Tmp) {
+        Value = Tmp;
+      } else {
+        Count |= Shift;
+      }
+    }
+  } else {
+    // get hi portion
+    uint32_t Hi = Hi_32(Value);
+
+    // if some bits in hi portion
+    if (Hi) {
+      // leading zeros in hi portion plus all bits in lo portion
+      Count = CountLeadingZeros_32(Hi);
+    } else {
+      // get lo portion
+      uint32_t Lo = Lo_32(Value);
+      // same as 32 bit value
+      Count = CountLeadingZeros_32(Lo) + 32;
+    }
+  }
+#endif
+  return Count;
+}
+
+/// CountTrailingZeros_64 - This function performs the platform optimal form
+/// of counting the number of zeros from the least significant bit to the first
+/// one bit (64 bit edition.)
+/// Returns 64 if the word is zero.
+static INLINE unsigned CountTrailingZeros_64(uint64_t Value) {
+#if __GNUC__ >= 4
+  return (Value != 0) ? __builtin_ctzll(Value) : 64;
+#else
+  static const unsigned Mod67Position[] = {
+      64, 0,  1,  39, 2,  15, 40, 23, 3,  12, 16, 59, 41, 19, 24, 54, 4,
+      64, 13, 10, 17, 62, 60, 28, 42, 30, 20, 51, 25, 44, 55, 47, 5,  32,
+      65, 38, 14, 22, 11, 58, 18, 53, 63, 9,  61, 27, 29, 50, 43, 46, 31,
+      37, 21, 57, 52, 8,  26, 49, 45, 36, 56, 7,  48, 35, 6,  34, 33, 0};
+  return Mod67Position[(uint64_t)(-(int64_t)Value & (int64_t)Value) % 67];
+#endif
+}
+
+/// CountPopulation_64 - this function counts the number of set bits in a value,
+/// (64 bit edition.)
+static INLINE unsigned CountPopulation_64(uint64_t Value) {
+#if __GNUC__ >= 4
+  return __builtin_popcountll(Value);
+#else
+  uint64_t v = Value - (((Value) >> 1) & 0x5555555555555555ULL);
+  v = (v & 0x3333333333333333ULL) + (((v) >> 2) & 0x3333333333333333ULL);
+  v = (v + ((v) >> 4)) & 0x0F0F0F0F0F0F0F0FULL;
+  return unsigned((uint64_t)(v * 0x0101010101010101ULL) >> 56);
+#endif
+}
+
+static INLINE uint32_t countLeadingOnes_64(uint64_t __V, uint32_t skip) {
+  uint32_t Count = 0;
+  if (skip) (__V) <<= (skip);
+  while (__V && (__V & (1ULL << 63))) {
+    Count++;
+    (__V) <<= 1;
+  }
+  return Count;
+}
+
+static INLINE std::string oct2Bin(char oct) {
+  switch (oct) {
+    case '\0': {
+      return "";
+    }
+    case '.': {
+      return ".";
+    }
+    case '0': {
+      return "000";
+    }
+    case '1': {
+      return "001";
+    }
+    case '2': {
+      return "010";
+    }
+    case '3': {
+      return "011";
+    }
+    case '4': {
+      return "100";
+    }
+    case '5': {
+      return "101";
+    }
+    case '6': {
+      return "110";
+    }
+    case '7': {
+      return "111";
+    }
+  }
+  assert(0 && "Invalid character in digit string");
+  return "";
+}
+
+static INLINE std::string hex2Bin(char hex) {
+  switch (hex) {
+    case '\0': {
+      return "";
+    }
+    case '.': {
+      return ".";
+    }
+    case '0': {
+      return "0000";
+    }
+    case '1': {
+      return "0001";
+    }
+    case '2': {
+      return "0010";
+    }
+    case '3': {
+      return "0011";
+    }
+    case '4': {
+      return "0100";
+    }
+    case '5': {
+      return "0101";
+    }
+    case '6': {
+      return "0110";
+    }
+    case '7': {
+      return "0111";
+    }
+    case '8': {
+      return "1000";
+    }
+    case '9': {
+      return "1001";
+    }
+    case 'A':
+    case 'a': {
+      return "1010";
+    }
+    case 'B':
+    case 'b': {
+      return "1011";
+    }
+    case 'C':
+    case 'c': {
+      return "1100";
+    }
+    case 'D':
+    case 'd': {
+      return "1101";
+    }
+    case 'E':
+    case 'e': {
+      return "1110";
+    }
+    case 'F':
+    case 'f': {
+      return "1111";
+    }
+  }
+  assert(0 && "Invalid character in digit string");
+  return "";
+}
+
+static INLINE uint32_t decode_digit(char cdigit, int radix) {
+  uint32_t digit = 0;
+  if (radix == 16) {
+#define isxdigit(c)                                            \
+  (((c) >= '0' && (c) <= '9') || ((c) >= 'a' && (c) <= 'f') || \
+   ((c) >= 'A' && (c) <= 'F'))
+#define isdigit(c) ((c) >= '0' && (c) <= '9')
+    if (!isxdigit(cdigit)) assert(0 && "Invalid hex digit in string");
+    if (isdigit(cdigit))
+      digit = cdigit - '0';
+    else if (cdigit >= 'a')
+      digit = cdigit - 'a' + 10;
+    else if (cdigit >= 'A')
+      digit = cdigit - 'A' + 10;
+    else
+      assert(0 && "huh? we shouldn't get here");
+  } else if (isdigit(cdigit)) {
+    digit = cdigit - '0';
+  } else {
+    assert(0 && "Invalid character in digit string");
+  }
+#undef isxdigit
+#undef isdigit
+  return digit;
+}
+
+// Determine the radix of "val".
+static INLINE std::string parseString(const std::string& input, unsigned char& radix) {
+  size_t len = input.length();
+  if (len == 0) {
+    if (radix == 0) radix = 10;
+    return input;
+  }
+
+  size_t startPos = 0;
+  // Trim whitespace
+  while (input[startPos] == ' ' && startPos < len) startPos++;
+  while (input[len - 1] == ' ' && startPos < len) len--;
+
+  std::string val = input.substr(startPos, len - startPos);
+  // std::cout << "val = " << val << "\n";
+  len = val.length();
+  startPos = 0;
+
+  // If the length of the string is less than 2, then radix
+  // is decimal and there is no exponent.
+  if (len < 2) {
+    if (radix == 0) radix = 10;
+    return val;
+  }
+
+  bool isNegative = false;
+  std::string ans;
+
+  // First check to see if we start with a sign indicator
+  if (val[0] == '-') {
+    ans = "-";
+    ++startPos;
+    isNegative = true;
+  } else if (val[0] == '+')
+    ++startPos;
+
+  if (len - startPos < 2) {
+    if (radix == 0) radix = 10;
+    return val;
+  }
+
+  if (val.substr(startPos, 2) == "0x" || val.substr(startPos, 2) == "0X") {
+    // If we start with "0x", then the radix is hex.
+    radix = 16;
+    startPos += 2;
+  } else if (val.substr(startPos, 2) == "0b" ||
+             val.substr(startPos, 2) == "0B") {
+    // If we start with "0b", then the radix is binary.
+    radix = 2;
+    startPos += 2;
+  } else if (val.substr(startPos, 2) == "0o" ||
+             val.substr(startPos, 2) == "0O") {
+    // If we start with "0o", then the radix is octal.
+    radix = 8;
+    startPos += 2;
+  } else if (radix == 0) {
+    radix = 10;
+  }
+
+  int exp = 0;
+  if (radix == 10) {
+    // If radix is decimal, then see if there is an
+    // exponent indicator.
+    size_t expPos = val.find('e');
+    bool has_exponent = true;
+    if (expPos == std::string::npos) expPos = val.find('E');
+    if (expPos == std::string::npos) {
+      // No exponent indicator, so the mantissa goes to the end.
+      expPos = len;
+      has_exponent = false;
+    }
+    // std::cout << "startPos = " << startPos << " " << expPos << "\n";
+
+    ans += val.substr(startPos, expPos - startPos);
+    if (has_exponent) {
+      // Parse the exponent.
+      std::istringstream iss(val.substr(expPos + 1, len - expPos - 1));
+      iss >> exp;
+    }
+  } else {
+    // Check for a binary exponent indicator.
+    size_t expPos = val.find('p');
+    bool has_exponent = true;
+    if (expPos == std::string::npos) expPos = val.find('P');
+    if (expPos == std::string::npos) {
+      // No exponent indicator, so the mantissa goes to the end.
+      expPos = len;
+      has_exponent = false;
+    }
+
+    // std::cout << "startPos = " << startPos << " " << expPos << "\n";
+
+    assert(startPos <= expPos);
+    // Convert to binary as we go.
+    for (size_t i = startPos; i < expPos; ++i) {
+      if (radix == 16) {
+        ans += hex2Bin(val[i]);
+      } else if (radix == 8) {
+        ans += oct2Bin(val[i]);
+      } else { // radix == 2
+        ans += val[i];
+      }
+    }
+    // End in binary
+    radix = 2;
+    if (has_exponent) {
+      // Parse the exponent.
+      std::istringstream iss(val.substr(expPos + 1, len - expPos - 1));
+      iss >> exp;
+    }
+  }
+  if (exp == 0) return ans;
+
+  size_t decPos = ans.find('.');
+  if (decPos == std::string::npos) decPos = ans.length();
+  if ((int)decPos + exp >= (int)ans.length()) {
+    int i = decPos;
+    for (; i < (int)ans.length() - 1; ++i) ans[i] = ans[i + 1];
+    for (; i < (int)ans.length(); ++i) ans[i] = '0';
+    for (; i < (int)decPos + exp; ++i) ans += '0';
+    return ans;
+  } else if ((int)decPos + exp < (int)isNegative) {
+    std::string dupAns = "0.";
+    if (ans[0] == '-') dupAns = "-0.";
+    for (int i = 0; i < isNegative - (int)decPos - exp; ++i) dupAns += '0';
+    for (size_t i = isNegative; i < ans.length(); ++i)
+      if (ans[i] != '.') dupAns += ans[i];
+    return dupAns;
+  }
+
+  if (exp > 0)
+    for (size_t i = decPos; i < decPos + exp; ++i) ans[i] = ans[i + 1];
+  else {
+    if (decPos == ans.length()) ans += ' ';
+    for (int i = decPos; i > (int)decPos + exp; --i) ans[i] = ans[i - 1];
+  }
+  ans[decPos + exp] = '.';
+  return ans;
+}
+
+/// sub_1 - This function subtracts a single "digit" (64-bit word), y, from
+/// the multi-digit integer array, x[], propagating the borrowed 1 value until
+/// no further borrowing is neeeded or it runs out of "digits" in x.  The result
+/// is 1 if "borrowing" exhausted the digits in x, or 0 if x was not exhausted.
+/// In other words, if y > x then this function returns 1, otherwise 0.
+/// @returns the borrow out of the subtraction
+static INLINE bool sub_1(uint64_t x[], uint32_t len, uint64_t y) {
+  for (uint32_t i = 0; i < len; ++i) {
+    uint64_t __X = x[i];
+    x[i] -= y;
+    if (y > __X)
+      y = 1; // We have to "borrow 1" from next "digit"
+    else {
+      y = 0; // No need to borrow
+      break; // Remaining digits are unchanged so exit early
+    }
+  }
+  return (y != 0);
+}
+
+/// add_1 - This function adds a single "digit" integer, y, to the multiple
+/// "digit" integer array,  x[]. x[] is modified to reflect the addition and
+/// 1 is returned if there is a carry out, otherwise 0 is returned.
+/// @returns the carry of the addition.
+static INLINE bool add_1(uint64_t dest[], uint64_t x[], uint32_t len,
+                         uint64_t y) {
+  for (uint32_t i = 0; i < len; ++i) {
+    dest[i] = y + x[i];
+    if (dest[i] < y)
+      y = 1; // Carry one to next digit.
+    else {
+      y = 0; // No need to carry so exit early
+      break;
+    }
+  }
+  return (y != 0);
+}
+
+/// add - This function adds the integer array x to the integer array Y and
+/// places the result in dest.
+/// @returns the carry out from the addition
+/// @brief General addition of 64-bit integer arrays
+static INLINE bool add(uint64_t* dest, const uint64_t* x, const uint64_t* y,
+                       uint32_t destlen, uint32_t xlen, uint32_t ylen,
+                       bool xsigned, bool ysigned) {
+  bool carry = false;
+  uint32_t len = AESL_std::min(xlen, ylen);
+  uint32_t i;
+  for (i = 0; i < len && i < destlen; ++i) {
+    uint64_t limit =
+        AESL_std::min(x[i], y[i]); // must come first in case dest == x
+    dest[i] = x[i] + y[i] + carry;
+    carry = dest[i] < limit || (carry && dest[i] == limit);
+  }
+  if (xlen > ylen) {
+    const uint64_t yext = ysigned && int64_t(y[ylen - 1]) < 0 ? -1 : 0;
+    for (i = ylen; i < xlen && i < destlen; i++) {
+      uint64_t limit = AESL_std::min(x[i], yext);
+      dest[i] = x[i] + yext + carry;
+      carry = (dest[i] < limit) || (carry && dest[i] == limit);
+    }
+  } else if (ylen > xlen) {
+    const uint64_t xext = xsigned && int64_t(x[xlen - 1]) < 0 ? -1 : 0;
+    for (i = xlen; i < ylen && i < destlen; i++) {
+      uint64_t limit = AESL_std::min(xext, y[i]);
+      dest[i] = xext + y[i] + carry;
+      carry = (dest[i] < limit) || (carry && dest[i] == limit);
+    }
+  }
+  return carry;
+}
+
+/// @returns returns the borrow out.
+/// @brief Generalized subtraction of 64-bit integer arrays.
+static INLINE bool sub(uint64_t* dest, const uint64_t* x, const uint64_t* y,
+                       uint32_t destlen, uint32_t xlen, uint32_t ylen,
+                       bool xsigned, bool ysigned) {
+  bool borrow = false;
+  uint32_t i;
+  uint32_t len = AESL_std::min(xlen, ylen);
+  for (i = 0; i < len && i < destlen; ++i) {
+    uint64_t x_tmp = borrow ? x[i] - 1 : x[i];
+    borrow = y[i] > x_tmp || (borrow && x[i] == 0);
+    dest[i] = x_tmp - y[i];
+  }
+  if (xlen > ylen) {
+    const uint64_t yext = ysigned && int64_t(y[ylen - 1]) < 0 ? -1 : 0;
+    for (i = ylen; i < xlen && i < destlen; i++) {
+      uint64_t x_tmp = borrow ? x[i] - 1 : x[i];
+      borrow = yext > x_tmp || (borrow && x[i] == 0);
+      dest[i] = x_tmp - yext;
+    }
+  } else if (ylen > xlen) {
+    const uint64_t xext = xsigned && int64_t(x[xlen - 1]) < 0 ? -1 : 0;
+    for (i = xlen; i < ylen && i < destlen; i++) {
+      uint64_t x_tmp = borrow ? xext - 1 : xext;
+      borrow = y[i] > x_tmp || (borrow && xext == 0);
+      dest[i] = x_tmp - y[i];
+    }
+  }
+  return borrow;
+}
+
+/// Subtracts the RHS ap_private from this ap_private
+/// @returns this, after subtraction
+/// @brief Subtraction assignment operator.
+
+/// Multiplies an integer array, x by a a uint64_t integer and places the result
+/// into dest.
+/// @returns the carry out of the multiplication.
+/// @brief Multiply a multi-digit ap_private by a single digit (64-bit) integer.
+static INLINE uint64_t mul_1(uint64_t dest[], const uint64_t x[], uint32_t len,
+                             uint64_t y) {
+  // Split y into high 32-bit part (hy)  and low 32-bit part (ly)
+  uint64_t ly = y & 0xffffffffULL, hy = (y) >> 32;
+  uint64_t carry = 0;
+  static const uint64_t two_power_32 = 1ULL << 32;
+  // For each digit of x.
+  for (uint32_t i = 0; i < len; ++i) {
+    // Split x into high and low words
+    uint64_t lx = x[i] & 0xffffffffULL;
+    uint64_t hx = (x[i]) >> 32;
+    // hasCarry - A flag to indicate if there is a carry to the next digit.
+    // hasCarry == 0, no carry
+    // hasCarry == 1, has carry
+    // hasCarry == 2, no carry and the calculation result == 0.
+    uint8_t hasCarry = 0;
+    dest[i] = carry + lx * ly;
+    // Determine if the add above introduces carry.
+    hasCarry = (dest[i] < carry) ? 1 : 0;
+    carry = hx * ly + ((dest[i]) >> 32) + (hasCarry ? two_power_32 : 0);
+    // The upper limit of carry can be (2^32 - 1)(2^32 - 1) +
+    // (2^32 - 1) + 2^32 = 2^64.
+    hasCarry = (!carry && hasCarry) ? 1 : (!carry ? 2 : 0);
+
+    carry += (lx * hy) & 0xffffffffULL;
+    dest[i] = ((carry) << 32) | (dest[i] & 0xffffffffULL);
+    carry = (((!carry && hasCarry != 2) || hasCarry == 1) ? two_power_32 : 0) +
+            ((carry) >> 32) + ((lx * hy) >> 32) + hx * hy;
+  }
+  return carry;
+}
+
+/// Multiplies integer array x by integer array y and stores the result into
+/// the integer array dest. Note that dest's size must be >= xlen + ylen in
+/// order to
+/// do a full precision computation. If it is not, then only the low-order words
+/// are returned.
+/// @brief Generalized multiplicate of integer arrays.
+static INLINE void mul(uint64_t dest[], const uint64_t x[], uint32_t xlen,
+                       const uint64_t y[], uint32_t ylen, uint32_t destlen) {
+  assert(xlen > 0);
+  assert(ylen > 0);
+  assert(destlen >= xlen + ylen);
+  if (xlen < destlen) dest[xlen] = mul_1(dest, x, xlen, y[0]);
+  for (uint32_t i = 1; i < ylen; ++i) {
+    uint64_t ly = y[i] & 0xffffffffULL, hy = (y[i]) >> 32;
+    uint64_t carry = 0, lx = 0, hx = 0;
+    for (uint32_t j = 0; j < xlen; ++j) {
+      lx = x[j] & 0xffffffffULL;
+      hx = (x[j]) >> 32;
+      // hasCarry - A flag to indicate if has carry.
+      // hasCarry == 0, no carry
+      // hasCarry == 1, has carry
+      // hasCarry == 2, no carry and the calculation result == 0.
+      uint8_t hasCarry = 0;
+      uint64_t resul = carry + lx * ly;
+      hasCarry = (resul < carry) ? 1 : 0;
+      carry = (hasCarry ? (1ULL << 32) : 0) + hx * ly + ((resul) >> 32);
+      hasCarry = (!carry && hasCarry) ? 1 : (!carry ? 2 : 0);
+      carry += (lx * hy) & 0xffffffffULL;
+      resul = ((carry) << 32) | (resul & 0xffffffffULL);
+      if (i + j < destlen) dest[i + j] += resul;
+      carry =
+          (((!carry && hasCarry != 2) || hasCarry == 1) ? (1ULL << 32) : 0) +
+          ((carry) >> 32) + (dest[i + j] < resul ? 1 : 0) + ((lx * hy) >> 32) +
+          hx * hy;
+    }
+    if (i + xlen < destlen) dest[i + xlen] = carry;
+  }
+}
+
+/// Implementation of Knuth's Algorithm D (Division of nonnegative integers)
+/// from "Art of Computer Programming, Volume 2", section 4.3.1, p. 272. The
+/// variables here have the same names as in the algorithm. Comments explain
+/// the algorithm and any deviation from it.
+static INLINE void KnuthDiv(uint32_t* u, uint32_t* v, uint32_t* q, uint32_t* r,
+                            uint32_t m, uint32_t n) {
+  assert(u && "Must provide dividend");
+  assert(v && "Must provide divisor");
+  assert(q && "Must provide quotient");
+  assert(u != v && u != q && v != q && "Must us different memory");
+  assert(n > 1 && "n must be > 1");
+
+  // Knuth uses the value b as the base of the number system. In our case b
+  // is 2^31 so we just set it to -1u.
+  uint64_t b = uint64_t(1) << 32;
+
+  // DEBUG(cerr << "KnuthDiv: m=" << m << " n=" << n << '\n');
+  // DEBUG(cerr << "KnuthDiv: original:");
+  // DEBUG(for (int i = m+n; i >=0; i--) cerr << " " << std::setbase(16) <<
+  // u[i]);
+  // DEBUG(cerr << " by");
+  // DEBUG(for (int i = n; i >0; i--) cerr << " " << std::setbase(16) <<
+  // v[i-1]);
+  // DEBUG(cerr << '\n');
+  // D1. [Normalize.] Set d = b / (v[n-1] + 1) and multiply all the digits of
+  // u and v by d. Note that we have taken Knuth's advice here to use a power
+  // of 2 value for d such that d * v[n-1] >= b/2 (b is the base). A power of
+  // 2 allows us to shift instead of multiply and it is easy to determine the
+  // shift amount from the leading zeros.  We are basically normalizing the u
+  // and v so that its high bits are shifted to the top of v's range without
+  // overflow. Note that this can require an extra word in u so that u must
+  // be of length m+n+1.
+  uint32_t shift = CountLeadingZeros_32(v[n - 1]);
+  uint32_t v_carry = 0;
+  uint32_t u_carry = 0;
+  if (shift) {
+    for (uint32_t i = 0; i < m + n; ++i) {
+      uint32_t u_tmp = (u[i]) >> (32 - shift);
+      u[i] = ((u[i]) << (shift)) | u_carry;
+      u_carry = u_tmp;
+    }
+    for (uint32_t i = 0; i < n; ++i) {
+      uint32_t v_tmp = (v[i]) >> (32 - shift);
+      v[i] = ((v[i]) << (shift)) | v_carry;
+      v_carry = v_tmp;
+    }
+  }
+  u[m + n] = u_carry;
+  // DEBUG(cerr << "KnuthDiv:   normal:");
+  // DEBUG(for (int i = m+n; i >=0; i--) cerr << " " << std::setbase(16) <<
+  // u[i]);
+  // DEBUG(cerr << " by");
+  // DEBUG(for (int i = n; i >0; i--) cerr << " " << std::setbase(16) <<
+  // v[i-1]);
+  // DEBUG(cerr << '\n');
+
+  // D2. [Initialize j.]  Set j to m. This is the loop counter over the places.
+  int j = m;
+  do {
+    // DEBUG(cerr << "KnuthDiv: quotient digit #" << j << '\n');
+    // D3. [Calculate q'.].
+    //     Set qp = (u[j+n]*b + u[j+n-1]) / v[n-1]. (qp=qprime=q')
+    //     Set rp = (u[j+n]*b + u[j+n-1]) % v[n-1]. (rp=rprime=r')
+    // Now test if qp == b or qp*v[n-2] > b*rp + u[j+n-2]; if so, decrease
+    // qp by 1, inrease rp by v[n-1], and repeat this test if rp < b. The test
+    // on v[n-2] determines at high speed most of the cases in which the trial
+    // value qp is one too large, and it eliminates all cases where qp is two
+    // too large.
+    uint64_t dividend = ((uint64_t(u[j + n]) << 32) + u[j + n - 1]);
+    // DEBUG(cerr << "KnuthDiv: dividend == " << dividend << '\n');
+    uint64_t qp = dividend / v[n - 1];
+    uint64_t rp = dividend % v[n - 1];
+    if (qp == b || qp * v[n - 2] > b * rp + u[j + n - 2]) {
+      qp--;
+      rp += v[n - 1];
+      if (rp < b && (qp == b || qp * v[n - 2] > b * rp + u[j + n - 2])) qp--;
+    }
+    // DEBUG(cerr << "KnuthDiv: qp == " << qp << ", rp == " << rp << '\n');
+
+    // D4. [Multiply and subtract.] Replace (u[j+n]u[j+n-1]...u[j]) with
+    // (u[j+n]u[j+n-1]..u[j]) - qp * (v[n-1]...v[1]v[0]). This computation
+    // consists of a simple multiplication by a one-place number, combined with
+    // a subtraction.
+    bool isNeg = false;
+    for (uint32_t i = 0; i < n; ++i) {
+      uint64_t u_tmp = uint64_t(u[j + i]) | ((uint64_t(u[j + i + 1])) << 32);
+      uint64_t subtrahend = uint64_t(qp) * uint64_t(v[i]);
+      bool borrow = subtrahend > u_tmp;
+      /*DEBUG(cerr << "KnuthDiv: u_tmp == " << u_tmp
+        << ", subtrahend == " << subtrahend
+        << ", borrow = " << borrow << '\n');*/
+
+      uint64_t result = u_tmp - subtrahend;
+      uint32_t k = j + i;
+      u[k++] = (uint32_t)(result & (b - 1)); // subtract low word
+      u[k++] = (uint32_t)((result) >> 32);   // subtract high word
+      while (borrow && k <= m + n) {         // deal with borrow to the left
+        borrow = u[k] == 0;
+        u[k]--;
+        k++;
+      }
+      isNeg |= borrow;
+      /*DEBUG(cerr << "KnuthDiv: u[j+i] == " << u[j+i] << ",  u[j+i+1] == " <<
+        u[j+i+1] << '\n');*/
+    }
+    /*DEBUG(cerr << "KnuthDiv: after subtraction:");
+      DEBUG(for (int i = m+n; i >=0; i--) cerr << " " << u[i]);
+      DEBUG(cerr << '\n');*/
+    // The digits (u[j+n]...u[j]) should be kept positive; if the result of
+    // this step is actually negative, (u[j+n]...u[j]) should be left as the
+    // true value plus b**(n+1), namely as the b's complement of
+    // the true value, and a "borrow" to the left should be remembered.
+    //
+    if (isNeg) {
+      bool carry = true; // true because b's complement is "complement + 1"
+      for (uint32_t i = 0; i <= m + n; ++i) {
+        u[i] = ~u[i] + carry; // b's complement
+        carry = carry && u[i] == 0;
+      }
+    }
+    /*DEBUG(cerr << "KnuthDiv: after complement:");
+      DEBUG(for (int i = m+n; i >=0; i--) cerr << " " << u[i]);
+      DEBUG(cerr << '\n');*/
+
+    // D5. [Test remainder.] Set q[j] = qp. If the result of step D4 was
+    // negative, go to step D6; otherwise go on to step D7.
+    q[j] = (uint32_t)qp;
+    if (isNeg) {
+      // D6. [Add back]. The probability that this step is necessary is very
+      // small, on the order of only 2/b. Make sure that test data accounts for
+      // this possibility. Decrease q[j] by 1
+      q[j]--;
+      // and add (0v[n-1]...v[1]v[0]) to (u[j+n]u[j+n-1]...u[j+1]u[j]).
+      // A carry will occur to the left of u[j+n], and it should be ignored
+      // since it cancels with the borrow that occurred in D4.
+      bool carry = false;
+      for (uint32_t i = 0; i < n; i++) {
+        uint32_t limit = AESL_std::min(u[j + i], v[i]);
+        u[j + i] += v[i] + carry;
+        carry = u[j + i] < limit || (carry && u[j + i] == limit);
+      }
+      u[j + n] += carry;
+    }
+    /*DEBUG(cerr << "KnuthDiv: after correction:");
+      DEBUG(for (int i = m+n; i >=0; i--) cerr <<" " << u[i]);
+      DEBUG(cerr << "\nKnuthDiv: digit result = " << q[j] << '\n');*/
+
+    // D7. [Loop on j.]  Decrease j by one. Now if j >= 0, go back to D3.
+  } while (--j >= 0);
+
+  /*DEBUG(cerr << "KnuthDiv: quotient:");
+    DEBUG(for (int i = m; i >=0; i--) cerr <<" " << q[i]);
+    DEBUG(cerr << '\n');*/
+
+  // D8. [Unnormalize]. Now q[...] is the desired quotient, and the desired
+  // remainder may be obtained by dividing u[...] by d. If r is non-null we
+  // compute the remainder (urem uses this).
+  if (r) {
+    // The value d is expressed by the "shift" value above since we avoided
+    // multiplication by d by using a shift left. So, all we have to do is
+    // shift right here. In order to mak
+    if (shift) {
+      uint32_t carry = 0;
+      // DEBUG(cerr << "KnuthDiv: remainder:");
+      for (int i = n - 1; i >= 0; i--) {
+        r[i] = ((u[i]) >> (shift)) | carry;
+        carry = (u[i]) << (32 - shift);
+        // DEBUG(cerr << " " << r[i]);
+      }
+    } else {
+      for (int i = n - 1; i >= 0; i--) {
+        r[i] = u[i];
+        // DEBUG(cerr << " " << r[i]);
+      }
+    }
+    // DEBUG(cerr << '\n');
+  }
+  // DEBUG(cerr << std::setbase(10) << '\n');
+}
+
+template <int _AP_W, bool _AP_S>
+void divide(const ap_private<_AP_W, _AP_S>& LHS, uint32_t lhsWords,
+            const ap_private<_AP_W, _AP_S>& RHS, uint32_t rhsWords,
+            ap_private<_AP_W, _AP_S>* Quotient,
+            ap_private<_AP_W, _AP_S>* Remainder) {
+  assert(lhsWords >= rhsWords && "Fractional result");
+  enum { APINT_BITS_PER_WORD = 64 };
+  // First, compose the values into an array of 32-bit words instead of
+  // 64-bit words. This is a necessity of both the "short division" algorithm
+  // and the the Knuth "classical algorithm" which requires there to be native
+  // operations for +, -, and * on an m bit value with an m*2 bit result. We
+  // can't use 64-bit operands here because we don't have native results of
+  // 128-bits. Furthremore, casting the 64-bit values to 32-bit values won't
+  // work on large-endian machines.
+  uint64_t mask = ~0ull >> (sizeof(uint32_t) * 8);
+  uint32_t n = rhsWords * 2;
+  uint32_t m = (lhsWords * 2) - n;
+
+  // Allocate space for the temporary values we need either on the stack, if
+  // it will fit, or on the heap if it won't.
+  uint32_t SPACE[128];
+  uint32_t* __U = 0;
+  uint32_t* __V = 0;
+  uint32_t* __Q = 0;
+  uint32_t* __R = 0;
+  if ((Remainder ? 4 : 3) * n + 2 * m + 1 <= 128) {
+    __U = &SPACE[0];
+    __V = &SPACE[m + n + 1];
+    __Q = &SPACE[(m + n + 1) + n];
+    if (Remainder) __R = &SPACE[(m + n + 1) + n + (m + n)];
+  } else {
+    __U = new uint32_t[m + n + 1];
+    __V = new uint32_t[n];
+    __Q = new uint32_t[m + n];
+    if (Remainder) __R = new uint32_t[n];
+  }
+
+  // Initialize the dividend
+  memset(__U, 0, (m + n + 1) * sizeof(uint32_t));
+  for (unsigned i = 0; i < lhsWords; ++i) {
+    uint64_t tmp = LHS.get_pVal(i);
+    __U[i * 2] = (uint32_t)(tmp & mask);
+    __U[i * 2 + 1] = (tmp) >> (sizeof(uint32_t) * 8);
+  }
+  __U[m + n] = 0; // this extra word is for "spill" in the Knuth algorithm.
+
+  // Initialize the divisor
+  memset(__V, 0, (n) * sizeof(uint32_t));
+  for (unsigned i = 0; i < rhsWords; ++i) {
+    uint64_t tmp = RHS.get_pVal(i);
+    __V[i * 2] = (uint32_t)(tmp & mask);
+    __V[i * 2 + 1] = (tmp) >> (sizeof(uint32_t) * 8);
+  }
+
+  // initialize the quotient and remainder
+  memset(__Q, 0, (m + n) * sizeof(uint32_t));
+  if (Remainder) memset(__R, 0, n * sizeof(uint32_t));
+
+  // Now, adjust m and n for the Knuth division. n is the number of words in
+  // the divisor. m is the number of words by which the dividend exceeds the
+  // divisor (i.e. m+n is the length of the dividend). These sizes must not
+  // contain any zero words or the Knuth algorithm fails.
+  for (unsigned i = n; i > 0 && __V[i - 1] == 0; i--) {
+    n--;
+    m++;
+  }
+  for (unsigned i = m + n; i > 0 && __U[i - 1] == 0; i--) m--;
+
+  // If we're left with only a single word for the divisor, Knuth doesn't work
+  // so we implement the short division algorithm here. This is much simpler
+  // and faster because we are certain that we can divide a 64-bit quantity
+  // by a 32-bit quantity at hardware speed and short division is simply a
+  // series of such operations. This is just like doing short division but we
+  // are using base 2^32 instead of base 10.
+  assert(n != 0 && "Divide by zero?");
+  if (n == 1) {
+    uint32_t divisor = __V[0];
+    uint32_t remainder = 0;
+    for (int i = m + n - 1; i >= 0; i--) {
+      uint64_t partial_dividend = (uint64_t(remainder)) << 32 | __U[i];
+      if (partial_dividend == 0) {
+        __Q[i] = 0;
+        remainder = 0;
+      } else if (partial_dividend < divisor) {
+        __Q[i] = 0;
+        remainder = (uint32_t)partial_dividend;
+      } else if (partial_dividend == divisor) {
+        __Q[i] = 1;
+        remainder = 0;
+      } else {
+        __Q[i] = (uint32_t)(partial_dividend / divisor);
+        remainder = (uint32_t)(partial_dividend - (__Q[i] * divisor));
+      }
+    }
+    if (__R) __R[0] = remainder;
+  } else {
+    // Now we're ready to invoke the Knuth classical divide algorithm. In this
+    // case n > 1.
+    KnuthDiv(__U, __V, __Q, __R, m, n);
+  }
+
+  // If the caller wants the quotient
+  if (Quotient) {
+    // Set up the Quotient value's memory.
+    if (Quotient->BitWidth != LHS.BitWidth) {
+      if (Quotient->isSingleWord()) Quotient->set_VAL(0);
+    } else
+      Quotient->clear();
+
+    // The quotient is in Q. Reconstitute the quotient into Quotient's low
+    // order words.
+    if (lhsWords == 1) {
+      uint64_t tmp =
+          uint64_t(__Q[0]) | ((uint64_t(__Q[1])) << (APINT_BITS_PER_WORD / 2));
+      Quotient->set_VAL(tmp);
+    } else {
+      assert(!Quotient->isSingleWord() &&
+             "Quotient ap_private not large enough");
+      for (unsigned i = 0; i < lhsWords; ++i)
+        Quotient->set_pVal(
+            i, uint64_t(__Q[i * 2]) |
+                   ((uint64_t(__Q[i * 2 + 1])) << (APINT_BITS_PER_WORD / 2)));
+    }
+    Quotient->clearUnusedBits();
+  }
+
+  // If the caller wants the remainder
+  if (Remainder) {
+    // Set up the Remainder value's memory.
+    if (Remainder->BitWidth != RHS.BitWidth) {
+      if (Remainder->isSingleWord()) Remainder->set_VAL(0);
+    } else
+      Remainder->clear();
+
+    // The remainder is in R. Reconstitute the remainder into Remainder's low
+    // order words.
+    if (rhsWords == 1) {
+      uint64_t tmp =
+          uint64_t(__R[0]) | ((uint64_t(__R[1])) << (APINT_BITS_PER_WORD / 2));
+      Remainder->set_VAL(tmp);
+    } else {
+      assert(!Remainder->isSingleWord() &&
+             "Remainder ap_private not large enough");
+      for (unsigned i = 0; i < rhsWords; ++i)
+        Remainder->set_pVal(
+            i, uint64_t(__R[i * 2]) |
+                   ((uint64_t(__R[i * 2 + 1])) << (APINT_BITS_PER_WORD / 2)));
+    }
+    Remainder->clearUnusedBits();
+  }
+
+  // Clean up the memory we allocated.
+  if (__U != &SPACE[0]) {
+    delete[] __U;
+    delete[] __V;
+    delete[] __Q;
+    delete[] __R;
+  }
+}
+
+template <int _AP_W, bool _AP_S>
+void divide(const ap_private<_AP_W, _AP_S>& LHS, uint32_t lhsWords,
+            uint64_t RHS, ap_private<_AP_W, _AP_S>* Quotient,
+            ap_private<_AP_W, _AP_S>* Remainder) {
+  uint32_t rhsWords = 1;
+  assert(lhsWords >= rhsWords && "Fractional result");
+  enum { APINT_BITS_PER_WORD = 64 };
+  // First, compose the values into an array of 32-bit words instead of
+  // 64-bit words. This is a necessity of both the "short division" algorithm
+  // and the the Knuth "classical algorithm" which requires there to be native
+  // operations for +, -, and * on an m bit value with an m*2 bit result. We
+  // can't use 64-bit operands here because we don't have native results of
+  // 128-bits. Furthremore, casting the 64-bit values to 32-bit values won't
+  // work on large-endian machines.
+  uint64_t mask = ~0ull >> (sizeof(uint32_t) * 8);
+  uint32_t n = 2;
+  uint32_t m = (lhsWords * 2) - n;
+
+  // Allocate space for the temporary values we need either on the stack, if
+  // it will fit, or on the heap if it won't.
+  uint32_t SPACE[128];
+  uint32_t* __U = 0;
+  uint32_t* __V = 0;
+  uint32_t* __Q = 0;
+  uint32_t* __R = 0;
+  if ((Remainder ? 4 : 3) * n + 2 * m + 1 <= 128) {
+    __U = &SPACE[0];
+    __V = &SPACE[m + n + 1];
+    __Q = &SPACE[(m + n + 1) + n];
+    if (Remainder) __R = &SPACE[(m + n + 1) + n + (m + n)];
+  } else {
+    __U = new uint32_t[m + n + 1];
+    __V = new uint32_t[n];
+    __Q = new uint32_t[m + n];
+    if (Remainder) __R = new uint32_t[n];
+  }
+
+  // Initialize the dividend
+  memset(__U, 0, (m + n + 1) * sizeof(uint32_t));
+  for (unsigned i = 0; i < lhsWords; ++i) {
+    uint64_t tmp = LHS.get_pVal(i);
+    __U[i * 2] = tmp & mask;
+    __U[i * 2 + 1] = (tmp) >> (sizeof(uint32_t) * 8);
+  }
+  __U[m + n] = 0; // this extra word is for "spill" in the Knuth algorithm.
+
+  // Initialize the divisor
+  memset(__V, 0, (n) * sizeof(uint32_t));
+  __V[0] = RHS & mask;
+  __V[1] = (RHS) >> (sizeof(uint32_t) * 8);
+
+  // initialize the quotient and remainder
+  memset(__Q, 0, (m + n) * sizeof(uint32_t));
+  if (Remainder) memset(__R, 0, n * sizeof(uint32_t));
+
+  // Now, adjust m and n for the Knuth division. n is the number of words in
+  // the divisor. m is the number of words by which the dividend exceeds the
+  // divisor (i.e. m+n is the length of the dividend). These sizes must not
+  // contain any zero words or the Knuth algorithm fails.
+  for (unsigned i = n; i > 0 && __V[i - 1] == 0; i--) {
+    n--;
+    m++;
+  }
+  for (unsigned i = m + n; i > 0 && __U[i - 1] == 0; i--) m--;
+
+  // If we're left with only a single word for the divisor, Knuth doesn't work
+  // so we implement the short division algorithm here. This is much simpler
+  // and faster because we are certain that we can divide a 64-bit quantity
+  // by a 32-bit quantity at hardware speed and short division is simply a
+  // series of such operations. This is just like doing short division but we
+  // are using base 2^32 instead of base 10.
+  assert(n != 0 && "Divide by zero?");
+  if (n == 1) {
+    uint32_t divisor = __V[0];
+    uint32_t remainder = 0;
+    for (int i = m + n - 1; i >= 0; i--) {
+      uint64_t partial_dividend = (uint64_t(remainder)) << 32 | __U[i];
+      if (partial_dividend == 0) {
+        __Q[i] = 0;
+        remainder = 0;
+      } else if (partial_dividend < divisor) {
+        __Q[i] = 0;
+        remainder = partial_dividend;
+      } else if (partial_dividend == divisor) {
+        __Q[i] = 1;
+        remainder = 0;
+      } else {
+        __Q[i] = partial_dividend / divisor;
+        remainder = partial_dividend - (__Q[i] * divisor);
+      }
+    }
+    if (__R) __R[0] = remainder;
+  } else {
+    // Now we're ready to invoke the Knuth classical divide algorithm. In this
+    // case n > 1.
+    KnuthDiv(__U, __V, __Q, __R, m, n);
+  }
+
+  // If the caller wants the quotient
+  if (Quotient) {
+    // Set up the Quotient value's memory.
+    if (Quotient->BitWidth != LHS.BitWidth) {
+      if (Quotient->isSingleWord()) Quotient->set_VAL(0);
+    } else
+      Quotient->clear();
+
+    // The quotient is in Q. Reconstitute the quotient into Quotient's low
+    // order words.
+    if (lhsWords == 1) {
+      uint64_t tmp =
+          uint64_t(__Q[0]) | ((uint64_t(__Q[1])) << (APINT_BITS_PER_WORD / 2));
+      Quotient->set_VAL(tmp);
+    } else {
+      assert(!Quotient->isSingleWord() &&
+             "Quotient ap_private not large enough");
+      for (unsigned i = 0; i < lhsWords; ++i)
+        Quotient->set_pVal(
+            i, uint64_t(__Q[i * 2]) |
+                   ((uint64_t(__Q[i * 2 + 1])) << (APINT_BITS_PER_WORD / 2)));
+    }
+    Quotient->clearUnusedBits();
+  }
+
+  // If the caller wants the remainder
+  if (Remainder) {
+    // Set up the Remainder value's memory.
+    if (Remainder->BitWidth != 64 /* RHS.BitWidth */) {
+      if (Remainder->isSingleWord()) Remainder->set_VAL(0);
+    } else
+      Remainder->clear();
+
+    // The remainder is in __R. Reconstitute the remainder into Remainder's low
+    // order words.
+    if (rhsWords == 1) {
+      uint64_t tmp =
+          uint64_t(__R[0]) | ((uint64_t(__R[1])) << (APINT_BITS_PER_WORD / 2));
+      Remainder->set_VAL(tmp);
+    } else {
+      assert(!Remainder->isSingleWord() &&
+             "Remainder ap_private not large enough");
+      for (unsigned i = 0; i < rhsWords; ++i)
+        Remainder->set_pVal(
+            i, uint64_t(__R[i * 2]) |
+                   ((uint64_t(__R[i * 2 + 1])) << (APINT_BITS_PER_WORD / 2)));
+    }
+    Remainder->clearUnusedBits();
+  }
+
+  // Clean up the memory we allocated.
+  if (__U != &SPACE[0]) {
+    delete[] __U;
+    delete[] __V;
+    delete[] __Q;
+    delete[] __R;
+  }
+}
+
+/// @brief Logical right-shift function.
+template <int _AP_W, bool _AP_S, bool _AP_C>
+INLINE ap_private<_AP_W, _AP_S, _AP_C> lshr(
+    const ap_private<_AP_W, _AP_S, _AP_C>& LHS, uint32_t shiftAmt) {
+  return LHS.lshr(shiftAmt);
+}
+
+/// Left-shift the ap_private by shiftAmt.
+/// @brief Left-shift function.
+template <int _AP_W, bool _AP_S, bool _AP_C>
+INLINE ap_private<_AP_W, _AP_S, _AP_C> shl(
+    const ap_private<_AP_W, _AP_S, _AP_C>& LHS, uint32_t shiftAmt) {
+  return LHS.shl(shiftAmt);
+}
+
+} // namespace ap_private_ops
+
+#endif // LLVM_SUPPORT_MATHEXTRAS_H
+
+/// This enumeration just provides for internal constants used in this
+/// translation unit.
+enum {
+  MIN_INT_BITS = 1, ///< Minimum number of bits that can be specified
+  ///< Note that this must remain synchronized with IntegerType::MIN_INT_BITS
+  MAX_INT_BITS = (1 << 23) - 1 ///< Maximum number of bits that can be specified
+  ///< Note that this must remain synchronized with IntegerType::MAX_INT_BITS
+};
+
+//===----------------------------------------------------------------------===//
+//                              ap_private Class
+//===----------------------------------------------------------------------===//
+
+/// ap_private - This class represents arbitrary precision constant integral
+/// values.
+/// It is a functional replacement for common case unsigned integer type like
+/// "unsigned", "unsigned long" or "uint64_t", but also allows non-byte-width
+/// integer sizes and large integer value types such as 3-bits, 15-bits, or more
+/// than 64-bits of precision. ap_private provides a variety of arithmetic
+/// operators
+/// and methods to manipulate integer values of any bit-width. It supports both
+/// the typical integer arithmetic and comparison operations as well as bitwise
+/// manipulation.
+///
+/// The class has several invariants worth noting:
+///   * All bit, byte, and word positions are zero-based.
+///   * Once the bit width is set, it doesn't change except by the Truncate,
+///     SignExtend, or ZeroExtend operations.
+///   * All binary operators must be on ap_private instances of the same bit
+///   width.
+///     Attempting to use these operators on instances with different bit
+///     widths will yield an assertion.
+///   * The value is stored canonically as an unsigned value. For operations
+///     where it makes a difference, there are both signed and unsigned variants
+///     of the operation. For example, sdiv and udiv. However, because the bit
+///     widths must be the same, operations such as Mul and Add produce the same
+///     results regardless of whether the values are interpreted as signed or
+///     not.
+///   * In general, the class tries to follow the style of computation that LLVM
+///     uses in its IR. This simplifies its use for LLVM.
+///
+/// @brief Class for arbitrary precision integers.
+
+#if defined(_MSC_VER)
+#if _MSC_VER < 1400 && !defined(for)
+#define for if (0); else for
+#endif
+typedef unsigned __int64 ap_ulong;
+typedef signed __int64 ap_slong;
+#else
+typedef unsigned long long ap_ulong;
+typedef signed long long ap_slong;
+#endif
+template <int _AP_N8, bool _AP_S>
+struct valtype;
+
+template <int _AP_N8>
+struct valtype<_AP_N8, false> {
+  typedef uint64_t Type;
+};
+
+template <int _AP_N8>
+struct valtype<_AP_N8, true> {
+  typedef int64_t Type;
+};
+
+template <>
+struct valtype<1, false> {
+  typedef unsigned char Type;
+};
+template <>
+struct valtype<2, false> {
+  typedef unsigned short Type;
+};
+template <>
+struct valtype<3, false> {
+  typedef unsigned int Type;
+};
+template <>
+struct valtype<4, false> {
+  typedef unsigned int Type;
+};
+template <>
+struct valtype<1, true> {
+  typedef signed char Type;
+};
+template <>
+struct valtype<2, true> {
+  typedef short Type;
+};
+template <>
+struct valtype<3, true> {
+  typedef int Type;
+};
+template <>
+struct valtype<4, true> {
+  typedef int Type;
+};
+
+template <bool enable>
+struct ap_private_enable_if {};
+template <>
+struct ap_private_enable_if<true> {
+  static const bool isValid = true;
+};
+
+// When bitwidth < 64
+template <int _AP_W, bool _AP_S>
+class ap_private<_AP_W, _AP_S, true> {
+  // SFINAE pattern.  Only consider this class when _AP_W <= 64
+  const static bool valid = ap_private_enable_if<_AP_W <= 64>::isValid;
+
+#ifdef _MSC_VER
+#pragma warning(disable : 4521 4522)
+#endif
+ public:
+  typedef typename valtype<(_AP_W + 7) / 8, _AP_S>::Type ValType;
+  typedef ap_private<_AP_W, _AP_S> Type;
+  template <int _AP_W2, bool _AP_S2>
+  struct RType {
+    enum {
+      mult_w = _AP_W + _AP_W2,
+      mult_s = _AP_S || _AP_S2,
+      plus_w =
+          AP_MAX(_AP_W + (_AP_S2 && !_AP_S), _AP_W2 + (_AP_S && !_AP_S2)) + 1,
+      plus_s = _AP_S || _AP_S2,
+      minus_w =
+          AP_MAX(_AP_W + (_AP_S2 && !_AP_S), _AP_W2 + (_AP_S && !_AP_S2)) + 1,
+      minus_s = true,
+      div_w = _AP_W + _AP_S2,
+      div_s = _AP_S || _AP_S2,
+      mod_w = AP_MIN(_AP_W, _AP_W2 + (!_AP_S2 && _AP_S)),
+      mod_s = _AP_S,
+      logic_w = AP_MAX(_AP_W + (_AP_S2 && !_AP_S), _AP_W2 + (_AP_S && !_AP_S2)),
+      logic_s = _AP_S || _AP_S2
+    };
+    typedef ap_private<mult_w, mult_s> mult;
+    typedef ap_private<plus_w, plus_s> plus;
+    typedef ap_private<minus_w, minus_s> minus;
+    typedef ap_private<logic_w, logic_s> logic;
+    typedef ap_private<div_w, div_s> div;
+    typedef ap_private<mod_w, mod_s> mod;
+    typedef ap_private<_AP_W, _AP_S> arg1;
+    typedef bool reduce;
+  };
+  enum { APINT_BITS_PER_WORD = sizeof(uint64_t) * 8 };
+  enum {
+    excess_bits = (_AP_W % APINT_BITS_PER_WORD)
+                      ? APINT_BITS_PER_WORD - (_AP_W % APINT_BITS_PER_WORD)
+                      : 0
+  };
+  static const uint64_t mask = ((uint64_t)~0ULL >> (excess_bits));
+  static const uint64_t not_mask = ~mask;
+  static const uint64_t sign_bit_mask = 1ULL << (APINT_BITS_PER_WORD - 1);
+  template <int _AP_W1>
+  struct sign_ext_mask {
+    static const uint64_t mask = ~0ULL << _AP_W1;
+  };
+  static const int width = _AP_W;
+
+  enum {
+    BitWidth = _AP_W,
+    _AP_N = 1,
+  };
+  ValType VAL; ///< Used to store the <= 64 bits integer value.
+#ifdef AP_CANARY
+  ValType CANARY;
+  void check_canary() { assert(CANARY == (ValType)0xDEADBEEFDEADBEEF); }
+  void set_canary() { CANARY = (ValType)0xDEADBEEFDEADBEEF; }
+#else
+  void check_canary() {}
+  void set_canary() {}
+#endif
+
+  INLINE ValType& get_VAL(void) { return VAL; }
+  INLINE ValType get_VAL(void) const { return VAL; }
+  INLINE ValType get_VAL(void) const volatile { return VAL; }
+  INLINE void set_VAL(uint64_t value) { VAL = (ValType)value; }
+  INLINE ValType& get_pVal(int i) { return VAL; }
+  INLINE ValType get_pVal(int i) const { return VAL; }
+  INLINE const uint64_t* get_pVal() const {
+    assert(0 && "invalid usage");
+    return 0;
+  }
+  INLINE ValType get_pVal(int i) const volatile { return VAL; }
+  INLINE uint64_t* get_pVal() const volatile {
+    assert(0 && "invalid usage");
+    return 0;
+  }
+  INLINE void set_pVal(int i, uint64_t value) { VAL = (ValType)value; }
+
+  INLINE uint32_t getBitWidth() const { return BitWidth; }
+
+  template <int _AP_W1, bool _AP_S1>
+  ap_private<_AP_W, _AP_S>& operator=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    VAL = (ValType)(RHS.get_VAL());
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  ap_private<_AP_W, _AP_S>& operator=(
+      const volatile ap_private<_AP_W1, _AP_S1>& RHS) {
+    VAL = (ValType)(RHS.get_VAL()); // TODO check here about ap_private<W,S,false>
+    clearUnusedBits();
+    return *this;
+  }
+
+  void operator=(const ap_private& RHS) volatile {
+    // Don't do anything for X = X
+    VAL = RHS.get_VAL(); // No need to check because no harm done by copying.
+    clearUnusedBits();
+  }
+
+  ap_private& operator=(const ap_private& RHS) {
+    // Don't do anything for X = X
+    VAL = RHS.get_VAL(); // No need to check because no harm done by copying.
+    clearUnusedBits();
+    return *this;
+  }
+
+  void operator=(const volatile ap_private& RHS) volatile {
+    // Don't do anything for X = X
+    VAL = RHS.get_VAL(); // No need to check because no harm done by copying.
+    clearUnusedBits();
+  }
+
+  ap_private& operator=(const volatile ap_private& RHS) {
+    // Don't do anything for X = X
+    VAL = RHS.get_VAL(); // No need to check because no harm done by copying.
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private& operator=(const _private_range_ref<_AP_W2, _AP_S2>& op2) {
+    *this = ap_private<_AP_W2, false>(op2);
+    return *this;
+  }
+
+#define ASSIGN_OP_FROM_INT(C_TYPE)               \
+  INLINE ap_private& operator=(const C_TYPE v) { \
+    set_canary();                                \
+    this->VAL = (ValType)v;                      \
+    clearUnusedBits();                           \
+    check_canary();                              \
+    return *this;                                \
+  }
+
+ASSIGN_OP_FROM_INT(bool)
+ASSIGN_OP_FROM_INT(char)
+ASSIGN_OP_FROM_INT(signed char)
+ASSIGN_OP_FROM_INT(unsigned char)
+ASSIGN_OP_FROM_INT(short)
+ASSIGN_OP_FROM_INT(unsigned short)
+ASSIGN_OP_FROM_INT(int)
+ASSIGN_OP_FROM_INT(unsigned int)
+ASSIGN_OP_FROM_INT(long)
+ASSIGN_OP_FROM_INT(unsigned long)
+ASSIGN_OP_FROM_INT(ap_slong)
+ASSIGN_OP_FROM_INT(ap_ulong)
+#if 0
+ASSIGN_OP_FROM_INT(half)
+ASSIGN_OP_FROM_INT(float)
+ASSIGN_OP_FROM_INT(double)
+#endif
+#undef ASSIGN_OP_FROM_INT
+
+  // XXX This is a must to prevent pointer being converted to bool.
+  INLINE ap_private& operator=(const char* s) {
+    ap_private tmp(s); // XXX direct-initialization, as ctor is explicit.
+    operator=(tmp);
+    return *this;
+  }
+
+ private:
+  explicit INLINE ap_private(uint64_t* val) : VAL(val[0]) {
+    set_canary();
+    clearUnusedBits();
+    check_canary();
+  }
+
+  INLINE bool isSingleWord() const { return true; }
+
+ public:
+  INLINE void fromString(const char* strStart, uint32_t slen, uint8_t radix) {
+    bool isNeg = strStart[0] == '-';
+    if (isNeg) {
+      strStart++;
+      slen--;
+    }
+
+    if (strStart[0] == '0' && (strStart[1] == 'b' || strStart[1] == 'B')) {
+      //if(radix == 0) radix = 2;
+      _AP_WARNING(radix != 2, "%s seems to have base %d, but %d given.", strStart, 2, radix);
+      strStart += 2;
+      slen -=2;
+    } else if (strStart[0] == '0' && (strStart[1] == 'o' || strStart[1] == 'O')) {
+      //if (radix == 0) radix = 8;
+      _AP_WARNING(radix != 8, "%s seems to have base %d, but %d given.", strStart, 8, radix);
+      strStart += 2;
+      slen -=2;
+    } else if (strStart[0] == '0' && (strStart[1] == 'x' || strStart[1] == 'X')) {
+      //if (radix == 0) radix = 16;
+      _AP_WARNING(radix != 16, "%s seems to have base %d, but %d given.", strStart, 16, radix);
+      strStart += 2;
+      slen -=2;
+    } else if (strStart[0] == '0' && (strStart[1] == 'd' || strStart[1] == 'D')) {
+      //if (radix == 0) radix = 10;
+      _AP_WARNING(radix != 10, "%s seems to have base %d, but %d given.", strStart, 10, radix);
+      strStart += 2;
+      slen -=2;
+    } else if (radix == 0) {
+      //radix = 2; // XXX default value
+    }
+
+    // Check our assumptions here
+    assert((radix == 10 || radix == 8 || radix == 16 || radix == 2) &&
+           "Radix should be 2, 8, 10, or 16!");
+    assert(strStart && "String is null?");
+
+    // Clear bits.
+    uint64_t tmpVAL = VAL = 0;
+
+    switch (radix) {
+      case 2:
+        //        sscanf(strStart,"%b",&VAL);
+        // tmpVAL = *strStart =='1' ? ~0ULL : 0;
+        for (; *strStart; ++strStart) {
+          assert((*strStart == '0' || *strStart == '1') &&
+                 ("Wrong binary number"));
+          tmpVAL <<= 1;
+          tmpVAL |= (*strStart - '0');
+        }
+        break;
+      case 8:
+#ifdef _MSC_VER
+        sscanf_s(strStart, "%llo", &tmpVAL, slen + 1);
+#else
+#if defined(__x86_64__) && !defined(__MINGW32__) && !defined(__WIN32__)
+        sscanf(strStart, "%lo", &tmpVAL);
+#else
+        sscanf(strStart, "%llo", &tmpVAL);
+#endif //__x86_64__
+#endif //_MSC_VER
+        break;
+      case 10:
+#ifdef _MSC_VER
+        sscanf_s(strStart, "%llu", &tmpVAL, slen + 1);
+#else
+#if defined(__x86_64__) && !defined(__MINGW32__) && !defined(__WIN32__)
+        sscanf(strStart, "%lu", &tmpVAL);
+#else
+        sscanf(strStart, "%llu", &tmpVAL);
+#endif //__x86_64__
+#endif //_MSC_VER
+        break;
+      case 16:
+#ifdef _MSC_VER
+        sscanf_s(strStart, "%llx", &tmpVAL, slen + 1);
+#else
+#if defined(__x86_64__) && !defined(__MINGW32__) && !defined(__WIN32__)
+        sscanf(strStart, "%lx", &tmpVAL);
+#else
+        sscanf(strStart, "%llx", &tmpVAL);
+#endif //__x86_64__
+#endif //_MSC_VER
+        break;
+      default:
+        assert(true && "Unknown radix");
+        // error
+    }
+    VAL = isNeg ? (ValType)(-tmpVAL) : (ValType)(tmpVAL);
+
+    clearUnusedBits();
+  }
+
+ private:
+  INLINE ap_private(const std::string& val, uint8_t radix = 2) : VAL(0) {
+    assert(!val.empty() && "String empty?");
+    set_canary();
+    fromString(val.c_str(), val.size(), radix);
+    check_canary();
+  }
+
+  INLINE ap_private(const char strStart[], uint32_t slen, uint8_t radix)
+      : VAL(0) {
+    set_canary();
+    fromString(strStart, slen, radix);
+    check_canary();
+  }
+
+  INLINE ap_private(uint32_t numWords, const uint64_t bigVal[])
+      : VAL(bigVal[0]) {
+    set_canary();
+    clearUnusedBits();
+    check_canary();
+  }
+
+ public:
+  INLINE ap_private() {
+    set_canary();
+    clearUnusedBits();
+    check_canary();
+  }
+
+#define CTOR(TYPE)                              \
+  INLINE ap_private(TYPE v) : VAL((ValType)v) { \
+    set_canary();                               \
+    clearUnusedBits();                          \
+    check_canary();                             \
+  }
+  CTOR(bool)
+  CTOR(char)
+  CTOR(signed char)
+  CTOR(unsigned char)
+  CTOR(short)
+  CTOR(unsigned short)
+  CTOR(int)
+  CTOR(unsigned int)
+  CTOR(long)
+  CTOR(unsigned long)
+  CTOR(ap_slong)
+  CTOR(ap_ulong)
+#if 0
+  CTOR(half)
+  CTOR(float)
+  CTOR(double)
+#endif
+#undef CTOR
+
+  template <int _AP_W1, bool _AP_S1, bool _AP_OPT>
+  INLINE ap_private(const ap_private<_AP_W1, _AP_S1, _AP_OPT>& that)
+      : VAL((ValType)that.get_VAL()) {
+    set_canary();
+    clearUnusedBits();
+    check_canary();
+  }
+
+  template <int _AP_W1, bool _AP_S1, bool _AP_OPT>
+  INLINE ap_private(const volatile ap_private<_AP_W1, _AP_S1, _AP_OPT>& that)
+      : VAL((ValType)that.get_VAL()) {
+    set_canary();
+    clearUnusedBits();
+    check_canary();
+  }
+
+  explicit INLINE ap_private(const char* val) {
+    set_canary();
+    unsigned char radix = 10;
+    std::string str = ap_private_ops::parseString(val, radix); // will set radix.
+    std::string::size_type pos = str.find('.');
+    // trunc all fraction part
+    if (pos != std::string::npos) str = str.substr(pos);
+
+    ap_private<_AP_W, _AP_S> ap_private_val(str, radix);
+    operator=(ap_private_val);
+    check_canary();
+  }
+
+  INLINE ap_private(const char* val, signed char rd) {
+    set_canary();
+    unsigned char radix = rd;
+    std::string str = ap_private_ops::parseString(val, radix); // will set radix.
+    std::string::size_type pos = str.find('.');
+    // trunc all fraction part
+    if (pos != std::string::npos) str = str.substr(pos);
+
+    ap_private<_AP_W, _AP_S> ap_private_val(str, radix);
+    operator=(ap_private_val);
+    check_canary();
+  }
+
+  INLINE ~ap_private() { check_canary(); }
+
+  INLINE bool isNegative() const {
+    static const uint64_t sign_mask = 1ULL << (_AP_W - 1);
+    return _AP_S && (sign_mask & VAL);
+  }
+
+  INLINE bool isPositive() const { return !isNegative(); }
+
+  INLINE bool isStrictlyPositive() const { return !isNegative() && VAL != 0; }
+
+  INLINE bool isAllOnesValue() const { return (mask & VAL) == mask; }
+
+  INLINE bool operator==(const ap_private<_AP_W, _AP_S>& RHS) const {
+    return VAL == RHS.get_VAL();
+  }
+  INLINE bool operator==(const ap_private<_AP_W, !_AP_S>& RHS) const {
+    return (uint64_t)VAL == (uint64_t)RHS.get_VAL();
+  }
+
+  INLINE bool operator==(uint64_t Val) const { return ((uint64_t)VAL == Val); }
+  INLINE bool operator!=(uint64_t Val) const { return ((uint64_t)VAL != Val); }
+  INLINE bool operator!=(const ap_private<_AP_W, _AP_S>& RHS) const {
+    return VAL != RHS.get_VAL();
+  }
+  INLINE bool operator!=(const ap_private<_AP_W, !_AP_S>& RHS) const {
+    return (uint64_t)VAL != (uint64_t)RHS.get_VAL();
+  }
+
+  /// postfix increment.
+  const ap_private operator++(int) {
+    ap_private orig(*this);
+    VAL++;
+    clearUnusedBits();
+    return orig;
+  }
+
+  /// prefix increment.
+  const ap_private operator++() {
+    ++VAL;
+    clearUnusedBits();
+    return *this;
+  }
+
+  /// postfix decrement.
+  const ap_private operator--(int) {
+    ap_private orig(*this);
+    --VAL;
+    clearUnusedBits();
+    return orig;
+  }
+
+  /// prefix decrement.
+  const ap_private operator--() {
+    --VAL;
+    clearUnusedBits();
+    return *this;
+  }
+
+  /// one's complement.
+  INLINE ap_private<_AP_W + !_AP_S, true> operator~() const {
+    ap_private<_AP_W + !_AP_S, true> Result(*this);
+    Result.flip();
+    return Result;
+  }
+
+  /// two's complement.
+  INLINE typename RType<1, false>::minus operator-() const {
+    return ap_private<1, false>(0) - (*this);
+  }
+
+  /// logic negation.
+  INLINE bool operator!() const { return !VAL; }
+
+  INLINE std::string toString(uint8_t radix, bool wantSigned) const;
+  INLINE std::string toStringUnsigned(uint8_t radix = 10) const {
+    return toString(radix, false);
+  }
+  INLINE std::string toStringSigned(uint8_t radix = 10) const {
+    return toString(radix, true);
+  }
+  INLINE void clear() { VAL = 0; }
+  INLINE ap_private& clear(uint32_t bitPosition) {
+    VAL &= ~(1ULL << (bitPosition));
+    clearUnusedBits();
+    return *this;
+  }
+
+  INLINE ap_private ashr(uint32_t shiftAmt) const {
+    if (_AP_S)
+      return ap_private((shiftAmt == BitWidth) ? 0
+                                               : ((int64_t)VAL) >> (shiftAmt));
+    else
+      return ap_private((shiftAmt == BitWidth) ? 0
+                                               : ((uint64_t)VAL) >> (shiftAmt));
+  }
+
+  INLINE ap_private lshr(uint32_t shiftAmt) const {
+    return ap_private((shiftAmt == BitWidth)
+                          ? ap_private(0)
+                          : ap_private((VAL & mask) >> (shiftAmt)));
+  }
+
+  INLINE ap_private shl(uint32_t shiftAmt) const
+// just for clang compiler
+#if defined(__clang__) && !defined(__CLANG_3_1__)
+      __attribute__((no_sanitize("undefined")))
+#endif
+  {
+    if (shiftAmt > BitWidth) {
+      if (!isNegative())
+        return ap_private(0);
+      else
+        return ap_private(-1);
+    }
+    if (shiftAmt == BitWidth)
+      return ap_private(0);
+    else
+      return ap_private((VAL) << (shiftAmt));
+    // return ap_private((shiftAmt == BitWidth) ? ap_private(0ULL) :
+    // ap_private(VAL << shiftAmt));
+  }
+
+  INLINE int64_t getSExtValue() const { return VAL; }
+
+  // XXX XXX this function is used in CBE
+  INLINE uint64_t getZExtValue() const { return VAL & mask; }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private(const _private_range_ref<_AP_W2, _AP_S2>& ref) {
+    set_canary();
+    *this = ref.get();
+    check_canary();
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private(const _private_bit_ref<_AP_W2, _AP_S2>& ref) {
+    set_canary();
+    *this = ((uint64_t)(bool)ref);
+    check_canary();
+  }
+
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_private(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& ref) {
+//    set_canary();
+//    *this = ref.get();
+//    check_canary();
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_private(
+//      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+//    set_canary();
+//    *this = ((val.operator ap_private<_AP_W2, false>()));
+//    check_canary();
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_private(
+//      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+//    set_canary();
+//    *this = (uint64_t)(bool)val;
+//    check_canary();
+//  }
+
+  INLINE void write(const ap_private<_AP_W, _AP_S>& op2) volatile {
+    *this = (op2);
+  }
+
+  // Explicit conversions to C interger types
+  //-----------------------------------------------------------
+  INLINE operator ValType() const { return get_VAL(); }
+
+  INLINE int to_uchar() const { return (unsigned char)get_VAL(); }
+
+  INLINE int to_char() const { return (signed char)get_VAL(); }
+
+  INLINE int to_ushort() const { return (unsigned short)get_VAL(); }
+
+  INLINE int to_short() const { return (short)get_VAL(); }
+
+  INLINE int to_int() const {
+    //      ap_private<64 /* _AP_W */, _AP_S> res(V);
+    return (int)get_VAL();
+  }
+
+  INLINE unsigned to_uint() const { return (unsigned)get_VAL(); }
+
+  INLINE long to_long() const { return (long)get_VAL(); }
+
+  INLINE unsigned long to_ulong() const { return (unsigned long)get_VAL(); }
+
+  INLINE ap_slong to_int64() const { return (ap_slong)get_VAL(); }
+
+  INLINE ap_ulong to_uint64() const { return (ap_ulong)get_VAL(); }
+
+  INLINE double to_double() const {
+    if (isNegative())
+      return roundToDouble(true);
+    else
+      return roundToDouble(false);
+  }
+
+  INLINE unsigned length() const { return _AP_W; }
+
+  INLINE bool isMinValue() const { return VAL == 0; }
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator&=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    VAL = (ValType)(((uint64_t)VAL) & RHS.get_VAL());
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator|=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    VAL = (ValType)(((uint64_t)VAL) | RHS.get_VAL());
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator^=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    VAL = (ValType)(((uint64_t)VAL) ^ RHS.get_VAL());
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator*=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    VAL = (ValType)(((uint64_t)VAL) * RHS.get_VAL());
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator+=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    VAL = (ValType)(((uint64_t)VAL) + RHS.get_VAL());
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator-=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    VAL = (ValType)(((uint64_t)VAL) - RHS.get_VAL());
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE typename RType<_AP_W1, _AP_S1>::logic operator&(
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    if (RType<_AP_W1, _AP_S1>::logic_w <= 64) {
+      typename RType<_AP_W1, _AP_S1>::logic Ret(((uint64_t)VAL) &
+                                                RHS.get_VAL());
+      return Ret;
+    } else {
+      typename RType<_AP_W1, _AP_S1>::logic Ret = *this;
+      return Ret & RHS;
+    }
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE typename RType<_AP_W1, _AP_S1>::logic operator^(
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    if (RType<_AP_W1, _AP_S1>::logic_w <= 64) {
+      typename RType<_AP_W1, _AP_S1>::logic Ret(((uint64_t)VAL) ^
+                                                RHS.get_VAL());
+      return Ret;
+    } else {
+      typename RType<_AP_W1, _AP_S1>::logic Ret = *this;
+      return Ret ^ RHS;
+    }
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE typename RType<_AP_W1, _AP_S1>::logic operator|(
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    if (RType<_AP_W1, _AP_S1>::logic_w <= 64) {
+      typename RType<_AP_W1, _AP_S1>::logic Ret(((uint64_t)VAL) |
+                                                RHS.get_VAL());
+      return Ret;
+    } else {
+      typename RType<_AP_W1, _AP_S1>::logic Ret = *this;
+      return Ret | RHS;
+    }
+  }
+
+  INLINE ap_private And(const ap_private& RHS) const {
+    return ap_private(VAL & RHS.get_VAL());
+  }
+
+  INLINE ap_private Or(const ap_private& RHS) const {
+    return ap_private(VAL | RHS.get_VAL());
+  }
+
+  INLINE ap_private Xor(const ap_private& RHS) const {
+    return ap_private(VAL ^ RHS.get_VAL());
+  }
+#if 1
+  template <int _AP_W1, bool _AP_S1>
+  INLINE typename RType<_AP_W1, _AP_S1>::mult operator*(
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    if (RType<_AP_W1, _AP_S1>::mult_w <= 64) {
+      typename RType<_AP_W1, _AP_S1>::mult Result(((uint64_t)VAL) *
+                                                  RHS.get_VAL());
+      return Result;
+    } else {
+      typename RType<_AP_W1, _AP_S1>::mult Result(*this);
+      Result *= RHS;
+      return Result;
+    }
+  }
+#endif
+  INLINE ap_private Mul(const ap_private& RHS) const {
+    return ap_private(VAL * RHS.get_VAL());
+  }
+
+  INLINE ap_private Add(const ap_private& RHS) const {
+    return ap_private(VAL + RHS.get_VAL());
+  }
+
+  INLINE ap_private Sub(const ap_private& RHS) const {
+    return ap_private(VAL - RHS.get_VAL());
+  }
+
+  INLINE ap_private& operator&=(uint64_t RHS) {
+    VAL &= (ValType)RHS;
+    clearUnusedBits();
+    return *this;
+  }
+  INLINE ap_private& operator|=(uint64_t RHS) {
+    VAL |= (ValType)RHS;
+    clearUnusedBits();
+    return *this;
+  }
+  INLINE ap_private& operator^=(uint64_t RHS) {
+    VAL ^= (ValType)RHS;
+    clearUnusedBits();
+    return *this;
+  }
+  INLINE ap_private& operator*=(uint64_t RHS) {
+    VAL *= (ValType)RHS;
+    clearUnusedBits();
+    return *this;
+  }
+  INLINE ap_private& operator+=(uint64_t RHS) {
+    VAL += (ValType)RHS;
+    clearUnusedBits();
+    return *this;
+  }
+  INLINE ap_private& operator-=(uint64_t RHS) {
+    VAL -= (ValType)RHS;
+    clearUnusedBits();
+    return *this;
+  }
+
+  INLINE bool isMinSignedValue() const {
+    static const uint64_t min_mask = ~(~0ULL << (_AP_W - 1));
+    return BitWidth == 1 ? VAL == 1
+                         : (ap_private_ops::isNegative<_AP_W>(*this) &&
+                            ((min_mask & VAL) == 0));
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE typename RType<_AP_W1, _AP_S1>::plus operator+(
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    if (RType<_AP_W1, _AP_S1>::plus_w <= 64)
+      return typename RType<_AP_W1, _AP_S1>::plus(
+          RType<_AP_W1, _AP_S1>::plus_s
+              ? int64_t(((uint64_t)VAL) + RHS.get_VAL())
+              : uint64_t(((uint64_t)VAL) + RHS.get_VAL()));
+    typename RType<_AP_W1, _AP_S1>::plus Result = RHS;
+    Result += VAL;
+    return Result;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE typename RType<_AP_W1, _AP_S1>::minus operator-(
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    if (RType<_AP_W1, _AP_S1>::minus_w <= 64)
+      return typename RType<_AP_W1, _AP_S1>::minus(
+          int64_t(((uint64_t)VAL) - RHS.get_VAL()));
+    typename RType<_AP_W1, _AP_S1>::minus Result = *this;
+    Result -= RHS;
+    return Result;
+  }
+
+  INLINE uint32_t countPopulation() const {
+    return ap_private_ops::CountPopulation_64(VAL);
+  }
+  INLINE uint32_t countLeadingZeros() const {
+    int remainder = BitWidth % 64;
+    int excessBits = (64 - remainder) % 64;
+    uint32_t Count = ap_private_ops::CountLeadingZeros_64(VAL);
+    if (Count) Count -= excessBits;
+    return AESL_std::min(Count, (uint32_t)_AP_W);
+  }
+
+  /// HiBits - This function returns the high "numBits" bits of this ap_private.
+  INLINE ap_private<_AP_W, _AP_S> getHiBits(uint32_t numBits) const {
+    ap_private<_AP_W, _AP_S> ret(*this);
+    ret = (ret) >> (BitWidth - numBits);
+    return ret;
+  }
+
+  /// LoBits - This function returns the low "numBits" bits of this ap_private.
+  INLINE ap_private<_AP_W, _AP_S> getLoBits(uint32_t numBits) const {
+    ap_private<_AP_W, _AP_S> ret(((uint64_t)VAL) << (BitWidth - numBits));
+    ret = (ret) >> (BitWidth - numBits);
+    return ret;
+    // return ap_private(numBits, (VAL << (BitWidth - numBits))>> (BitWidth -
+    // numBits));
+  }
+
+  INLINE ap_private<_AP_W, _AP_S>& set(uint32_t bitPosition) {
+    VAL |= (1ULL << (bitPosition));
+    clearUnusedBits();
+    return *this; // clearUnusedBits();
+  }
+
+  INLINE void set() {
+    VAL = (ValType)~0ULL;
+    clearUnusedBits();
+  }
+
+  template <int _AP_W3>
+  INLINE void set(const ap_private<_AP_W3, false>& val) {
+    operator=(ap_private<_AP_W3, _AP_S>(val));
+  }
+
+  INLINE void set(const ap_private& val) { operator=(val); }
+
+  INLINE void clearUnusedBits(void) volatile
+// just for clang compiler
+#if defined(__clang__) && !defined(__CLANG_3_1__)
+      __attribute__((no_sanitize("undefined")))
+#endif
+  {
+    enum { excess_bits = (_AP_W % 64) ? 64 - _AP_W % 64 : 0 };
+    VAL = (ValType)(
+        _AP_S
+            ? ((((int64_t)VAL) << (excess_bits)) >> (excess_bits))
+            : (excess_bits ? (((uint64_t)VAL) << (excess_bits)) >> (excess_bits)
+                           : (uint64_t)VAL));
+  }
+
+  INLINE void clearUnusedBitsToZero(void) {
+    enum { excess_bits = (_AP_W % 64) ? 64 - _AP_W % 64 : 0 };
+    static uint64_t mask = ~0ULL >> (excess_bits);
+    VAL &= mask;
+  }
+
+  INLINE ap_private udiv(const ap_private& RHS) const {
+    return ap_private((uint64_t)VAL / RHS.get_VAL());
+  }
+
+  /// Signed divide this ap_private by ap_private RHS.
+  /// @brief Signed division function for ap_private.
+  INLINE ap_private sdiv(const ap_private& RHS) const {
+    if (isNegative())
+      if (RHS.isNegative())
+        return ((uint64_t)(0 - (*this))) / (uint64_t)(0 - RHS);
+      else
+        return 0 - ((uint64_t)(0 - (*this)) / (uint64_t)(RHS));
+    else if (RHS.isNegative())
+      return 0 - (this->udiv((ap_private)(0 - RHS)));
+    return this->udiv(RHS);
+  }
+
+  template <bool _AP_S2>
+  INLINE ap_private urem(const ap_private<_AP_W, _AP_S2>& RHS) const {
+    assert(RHS.get_VAL() != 0 && "Divide by 0");
+    return ap_private(((uint64_t)VAL) % ((uint64_t)RHS.get_VAL()));
+  }
+
+  /// Signed remainder operation on ap_private.
+  /// @brief Function for signed remainder operation.
+  template <bool _AP_S2>
+  INLINE ap_private srem(const ap_private<_AP_W, _AP_S2>& RHS) const {
+    if (isNegative()) {
+      ap_private lhs = 0 - (*this);
+      if (RHS.isNegative()) {
+        ap_private rhs = 0 - RHS;
+        return 0 - (lhs.urem(rhs));
+      } else
+        return 0 - (lhs.urem(RHS));
+    } else if (RHS.isNegative()) {
+      ap_private rhs = 0 - RHS;
+      return this->urem(rhs);
+    }
+    return this->urem(RHS);
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool eq(const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    return (*this) == RHS;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool ne(const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    return !((*this) == RHS);
+  }
+
+  /// Regards both *this and RHS as unsigned quantities and compares them for
+  /// the validity of the less-than relationship.
+  /// @returns true if *this < RHS when both are considered unsigned.
+  /// @brief Unsigned less than comparison
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool ult(const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    if (_AP_W1 <= 64) {
+      uint64_t lhsZext = ((uint64_t(VAL)) << (64 - _AP_W)) >> (64 - _AP_W);
+      uint64_t rhsZext =
+          ((uint64_t(RHS.get_VAL())) << (64 - _AP_W1)) >> (64 - _AP_W1);
+      return lhsZext < rhsZext;
+    } else
+      return RHS.uge(*this);
+  }
+
+  /// Regards both *this and RHS as signed quantities and compares them for
+  /// validity of the less-than relationship.
+  /// @returns true if *this < RHS when both are considered signed.
+  /// @brief Signed less than comparison
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool slt(const ap_private<_AP_W1, _AP_S1>& RHS) const
+// just for clang compiler
+#if defined(__clang__) && !defined(__CLANG_3_1__)
+      __attribute__((no_sanitize("undefined")))
+#endif
+  {
+    if (_AP_W1 <= 64) {
+      int64_t lhsSext = ((int64_t(VAL)) << (64 - _AP_W)) >> (64 - _AP_W);
+      int64_t rhsSext =
+          ((int64_t(RHS.get_VAL())) << (64 - _AP_W1)) >> (64 - _AP_W1);
+      return lhsSext < rhsSext;
+    } else
+      return RHS.sge(*this);
+  }
+
+  /// Regards both *this and RHS as unsigned quantities and compares them for
+  /// validity of the less-or-equal relationship.
+  /// @returns true if *this <= RHS when both are considered unsigned.
+  /// @brief Unsigned less or equal comparison
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool ule(const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    return ult(RHS) || eq(RHS);
+  }
+
+  /// Regards both *this and RHS as signed quantities and compares them for
+  /// validity of the less-or-equal relationship.
+  /// @returns true if *this <= RHS when both are considered signed.
+  /// @brief Signed less or equal comparison
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool sle(const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    return slt(RHS) || eq(RHS);
+  }
+
+  /// Regards both *this and RHS as unsigned quantities and compares them for
+  /// the validity of the greater-than relationship.
+  /// @returns true if *this > RHS when both are considered unsigned.
+  /// @brief Unsigned greather than comparison
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool ugt(const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    return !ult(RHS) && !eq(RHS);
+  }
+
+  /// Regards both *this and RHS as signed quantities and compares them for
+  /// the validity of the greater-than relationship.
+  /// @returns true if *this > RHS when both are considered signed.
+  /// @brief Signed greather than comparison
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool sgt(const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    return !slt(RHS) && !eq(RHS);
+  }
+
+  /// Regards both *this and RHS as unsigned quantities and compares them for
+  /// validity of the greater-or-equal relationship.
+  /// @returns true if *this >= RHS when both are considered unsigned.
+  /// @brief Unsigned greater or equal comparison
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool uge(const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    return !ult(RHS);
+  }
+
+  /// Regards both *this and RHS as signed quantities and compares them for
+  /// validity of the greater-or-equal relationship.
+  /// @returns true if *this >= RHS when both are considered signed.
+  /// @brief Signed greather or equal comparison
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool sge(const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    return !slt(RHS);
+  }
+
+  INLINE ap_private abs() const {
+    if (isNegative()) return -(*this);
+    return *this;
+  }
+
+  INLINE ap_private<_AP_W, false> get() const {
+    ap_private<_AP_W, false> ret(*this);
+    return ret;
+  }
+
+  INLINE static uint32_t getBitsNeeded(const char* str, uint32_t slen,
+                                       uint8_t radix) {
+    return _AP_W;
+  }
+
+  INLINE uint32_t getActiveBits() const {
+    uint32_t bits = _AP_W - countLeadingZeros();
+    return bits ? bits : 1;
+  }
+
+  INLINE double roundToDouble(bool isSigned = false) const {
+    return isSigned ? double((int64_t)VAL) : double((uint64_t)VAL);
+  }
+
+  /*Reverse the contents of ap_private instance. I.e. LSB becomes MSB and vise
+   * versa*/
+  INLINE ap_private& reverse() {
+    for (int i = 0; i < _AP_W / 2; ++i) {
+      bool tmp = operator[](i);
+      if (operator[](_AP_W - 1 - i))
+        set(i);
+      else
+        clear(i);
+      if (tmp)
+        set(_AP_W - 1 - i);
+      else
+        clear(_AP_W - 1 - i);
+    }
+    clearUnusedBits();
+    return *this;
+  }
+
+  /*Return true if the value of ap_private instance is zero*/
+  INLINE bool iszero() const { return isMinValue(); }
+
+  INLINE bool to_bool() const { return !iszero(); }
+
+  /* x < 0 */
+  INLINE bool sign() const {
+    if (isNegative()) return true;
+    return false;
+  }
+
+  /* x[i] = !x[i] */
+  INLINE void invert(int i) {
+    assert(i >= 0 && "Attempting to read bit with negative index");
+    assert(i < _AP_W && "Attempting to read bit beyond MSB");
+    flip(i);
+  }
+
+  /* x[i] */
+  INLINE bool test(int i) const {
+    assert(i >= 0 && "Attempting to read bit with negative index");
+    assert(i < _AP_W && "Attempting to read bit beyond MSB");
+    return operator[](i);
+  }
+
+  // This is used for sc_lv and sc_bv, which is implemented by sc_uint
+  // Rotate an ap_private object n places to the left
+  INLINE void lrotate(int n) {
+    assert(n >= 0 && "Attempting to shift negative index");
+    assert(n < _AP_W && "Shift value larger than bit width");
+    operator=(shl(n) | lshr(_AP_W - n));
+  }
+
+  // This is used for sc_lv and sc_bv, which is implemented by sc_uint
+  // Rotate an ap_private object n places to the right
+  INLINE void rrotate(int n) {
+    assert(n >= 0 && "Attempting to shift negative index");
+    assert(n < _AP_W && "Shift value larger than bit width");
+    operator=(lshr(n) | shl(_AP_W - n));
+  }
+
+  // Set the ith bit into v
+  INLINE void set(int i, bool v) {
+    assert(i >= 0 && "Attempting to write bit with negative index");
+    assert(i < _AP_W && "Attempting to write bit beyond MSB");
+    v ? set(i) : clear(i);
+  }
+
+  // Set the ith bit into v
+  INLINE void set_bit(int i, bool v) {
+    assert(i >= 0 && "Attempting to write bit with negative index");
+    assert(i < _AP_W && "Attempting to write bit beyond MSB");
+    v ? set(i) : clear(i);
+  }
+
+  // Get the value of ith bit
+  INLINE bool get_bit(int i) const {
+    assert(i >= 0 && "Attempting to read bit with negative index");
+    assert(i < _AP_W && "Attempting to read bit beyond MSB");
+    return (((1ULL << i) & VAL) != 0);
+  }
+
+  /// Toggle all bits.
+  INLINE ap_private& flip() {
+    VAL = (ValType)((~0ULL ^ VAL) & mask);
+    clearUnusedBits();
+    return *this;
+  }
+
+  /// Toggles a given bit to its opposite value.
+  INLINE ap_private& flip(uint32_t bitPosition) {
+    assert(bitPosition < BitWidth && "Out of the bit-width range!");
+    set_bit(bitPosition, !get_bit(bitPosition));
+    return *this;
+  }
+
+  // complements every bit
+  INLINE void b_not() { flip(); }
+
+// Binary Arithmetic
+//-----------------------------------------------------------
+#define OP_BIN_AP(Sym, Rty, Fun)                           \
+  template <int _AP_W2, bool _AP_S2>                       \
+  INLINE typename RType<_AP_W2, _AP_S2>::Rty operator Sym( \
+      const ap_private<_AP_W2, _AP_S2>& op) const {        \
+    typename RType<_AP_W2, _AP_S2>::Rty lhs(*this);        \
+    typename RType<_AP_W2, _AP_S2>::Rty rhs(op);           \
+    return lhs.Fun(rhs);                                   \
+  }
+
+/// Bitwise and, or, xor
+// OP_BIN_AP(&,logic, And)
+// OP_BIN_AP(|,logic, Or)
+// OP_BIN_AP(^,logic, Xor)
+#undef OP_BIN_AP
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE typename RType<_AP_W2, _AP_S2>::div operator/(
+      const ap_private<_AP_W2, _AP_S2>& op) const {
+    ap_private<AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2)),
+               (_AP_W > _AP_W2 ? _AP_S
+                               : (_AP_W2 > _AP_W ? _AP_S2 : _AP_S || _AP_S2))>
+        lhs = *this;
+    ap_private<AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2)),
+               (_AP_W > _AP_W2 ? _AP_S
+                               : (_AP_W2 > _AP_W ? _AP_S2 : _AP_S || _AP_S2))>
+        rhs = op;
+    return typename RType<_AP_W2, _AP_S2>::div(
+        (_AP_S || _AP_S2) ? lhs.sdiv(rhs) : lhs.udiv(rhs));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE typename RType<_AP_W2, _AP_S2>::mod operator%(
+      const ap_private<_AP_W2, _AP_S2>& op) const {
+    ap_private<AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2)),
+               (_AP_W > _AP_W2 ? _AP_S
+                               : (_AP_W2 > _AP_W ? _AP_S2 : _AP_S || _AP_S2))>
+        lhs = *this;
+    ap_private<AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2)),
+               (_AP_W > _AP_W2 ? _AP_S
+                               : (_AP_W2 > _AP_W ? _AP_S2 : _AP_S || _AP_S2))>
+        rhs = op;
+    typename RType<_AP_W2, _AP_S2>::mod res =
+        typename RType<_AP_W2, _AP_S2>::mod(_AP_S ? lhs.srem(rhs)
+                                                  : lhs.urem(rhs));
+    return res;
+  }
+
+#define OP_ASSIGN_AP_2(Sym)                         \
+  template <int _AP_W2, bool _AP_S2>                \
+  INLINE ap_private<_AP_W, _AP_S>& operator Sym##=( \
+      const ap_private<_AP_W2, _AP_S2>& op) {       \
+    *this = operator Sym(op);                       \
+    return *this;                                   \
+  }
+
+  OP_ASSIGN_AP_2(/)
+  OP_ASSIGN_AP_2(%)
+#undef OP_ASSIGN_AP_2
+
+/// Bitwise assign: and, or, xor
+//-------------------------------------------------------------
+//    OP_ASSIGN_AP(&)
+//    OP_ASSIGN_AP(^)
+//    OP_ASSIGN_AP(|)
+
+#define OP_LEFT_SHIFT_CTYPE(TYPE, SIGNED)             \
+  INLINE ap_private operator<<(const TYPE op) const { \
+    if (op >= _AP_W) return ap_private(0);            \
+    if (SIGNED && op < 0) return *this >> (0 - op);   \
+    return shl(op);                                   \
+  }
+
+  // OP_LEFT_SHIFT_CTYPE(bool, false)
+  OP_LEFT_SHIFT_CTYPE(char, CHAR_IS_SIGNED)
+  OP_LEFT_SHIFT_CTYPE(signed char, true)
+  OP_LEFT_SHIFT_CTYPE(unsigned char, false)
+  OP_LEFT_SHIFT_CTYPE(short, true)
+  OP_LEFT_SHIFT_CTYPE(unsigned short, false)
+  OP_LEFT_SHIFT_CTYPE(int, true)
+  OP_LEFT_SHIFT_CTYPE(unsigned int, false)
+  OP_LEFT_SHIFT_CTYPE(long, true)
+  OP_LEFT_SHIFT_CTYPE(unsigned long, false)
+  OP_LEFT_SHIFT_CTYPE(long long, true)
+  OP_LEFT_SHIFT_CTYPE(unsigned long long, false)
+#if 0
+  OP_LEFT_SHIFT_CTYPE(half, false)
+  OP_LEFT_SHIFT_CTYPE(float, false)
+  OP_LEFT_SHIFT_CTYPE(double, false)
+#endif
+
+#undef OP_LEFT_SHIFT_CTYPE
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private operator<<(const ap_private<_AP_W2, _AP_S2>& op2) const {
+    if (_AP_S2 == false) {
+      uint32_t sh = op2.to_uint();
+      return *this << sh;
+    } else {
+      int sh = op2.to_int();
+      return *this << sh;
+    }
+  }
+
+#define OP_RIGHT_SHIFT_CTYPE(TYPE, SIGNED)            \
+  INLINE ap_private operator>>(const TYPE op) const { \
+    if (op >= _AP_W) {                                \
+      if (isNegative())                               \
+        return ap_private(-1);                        \
+      else                                            \
+        return ap_private(0);                         \
+    }                                                 \
+    if ((SIGNED) && op < 0) return *this << (0 - op); \
+    if (_AP_S)                                        \
+      return ashr(op);                                \
+    else                                              \
+      return lshr(op);                                \
+  }
+
+  // OP_RIGHT_SHIFT_CTYPE(bool, false)
+  OP_RIGHT_SHIFT_CTYPE(char, CHAR_IS_SIGNED)
+  OP_RIGHT_SHIFT_CTYPE(signed char, true)
+  OP_RIGHT_SHIFT_CTYPE(unsigned char, false)
+  OP_RIGHT_SHIFT_CTYPE(short, true)
+  OP_RIGHT_SHIFT_CTYPE(unsigned short, false)
+  OP_RIGHT_SHIFT_CTYPE(int, true)
+  OP_RIGHT_SHIFT_CTYPE(unsigned int, false)
+  OP_RIGHT_SHIFT_CTYPE(long, true)
+  OP_RIGHT_SHIFT_CTYPE(unsigned long, false)
+  OP_RIGHT_SHIFT_CTYPE(unsigned long long, false)
+  OP_RIGHT_SHIFT_CTYPE(long long, true)
+#if 0
+  OP_RIGHT_SHIFT_CTYPE(half, false)
+  OP_RIGHT_SHIFT_CTYPE(float, false)
+  OP_RIGHT_SHIFT_CTYPE(double, false)
+#endif
+
+#undef OP_RIGHT_SHIFT_CTYPE
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private operator>>(const ap_private<_AP_W2, _AP_S2>& op2) const {
+    if (_AP_S2 == false) {
+      uint32_t sh = op2.to_uint();
+      return *this >> sh;
+    } else {
+      int sh = op2.to_int();
+      return *this >> sh;
+    }
+  }
+
+  /// Shift assign
+  //-----------------------------------------------------------------
+
+  //INLINE const ap_private& operator<<=(uint32_t shiftAmt) {
+  //  VAL <<= shiftAmt;
+  //  clearUnusedBits();
+  //  return *this;
+  //}
+
+#define OP_ASSIGN_AP(Sym)                                                    \
+  template <int _AP_W2, bool _AP_S2>                                         \
+  INLINE ap_private& operator Sym##=(int op) {                               \
+    *this = operator Sym(op);                                                \
+    clearUnusedBits();                                                       \
+    return *this;                                                            \
+  }                                                                          \
+  INLINE ap_private& operator Sym##=(unsigned int op) {                      \
+    *this = operator Sym(op);                                                \
+    clearUnusedBits();                                                       \
+    return *this;                                                            \
+  }                                                                          \
+  template <int _AP_W2, bool _AP_S2>                                         \
+  INLINE ap_private& operator Sym##=(const ap_private<_AP_W2, _AP_S2>& op) { \
+    *this = operator Sym(op);                                                \
+    clearUnusedBits();                                                       \
+    return *this;                                                            \
+  }
+
+  OP_ASSIGN_AP(>>)
+  OP_ASSIGN_AP(<<)
+#undef OP_ASSIGN_AP
+
+  /// Comparisons
+  //-----------------------------------------------------------------
+  template <int _AP_W1, bool _AP_S1>
+  INLINE bool operator==(const ap_private<_AP_W1, _AP_S1>& op) const {
+    enum { _AP_MAX_W = AP_MAX(AP_MAX(_AP_W, _AP_W1), 32) };
+    ap_private<_AP_MAX_W, false> lhs(*this);
+    ap_private<_AP_MAX_W, false> rhs(op);
+    if (_AP_MAX_W <= 64) {
+      return (uint64_t)lhs.get_VAL() == (uint64_t)rhs.get_VAL();
+    } else
+      return lhs == rhs;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator!=(const ap_private<_AP_W2, _AP_S2>& op) const {
+    return !(*this == op);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>(const ap_private<_AP_W2, _AP_S2>& op) const {
+    enum {
+      _AP_MAX_W = AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2))
+    };
+    ap_private<_AP_MAX_W, _AP_S> lhs(*this);
+    ap_private<_AP_MAX_W, _AP_S2> rhs(op);
+    // this will follow gcc rule for comparison
+    // between different bitwidth and signness
+    if (_AP_S == _AP_S2)
+      return _AP_S ? lhs.sgt(rhs) : lhs.ugt(rhs);
+    else if (_AP_W < 32 && _AP_W2 < 32)
+      // different signness but both bitwidth is less than 32
+      return lhs.sgt(rhs);
+    else
+        // different signness but bigger bitwidth
+        // is greater or equal to 32
+        if (_AP_S)
+      if (_AP_W2 >= _AP_W)
+        return lhs.ugt(rhs);
+      else
+        return lhs.sgt(rhs);
+    else if (_AP_W >= _AP_W2)
+      return lhs.ugt(rhs);
+    else
+      return lhs.sgt(rhs);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<=(const ap_private<_AP_W2, _AP_S2>& op) const {
+    return !(*this > op);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<(const ap_private<_AP_W2, _AP_S2>& op) const {
+    enum {
+      _AP_MAX_W = AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2))
+    };
+    ap_private<_AP_MAX_W, _AP_S> lhs(*this);
+    ap_private<_AP_MAX_W, _AP_S2> rhs(op);
+    if (_AP_S == _AP_S2)
+      return _AP_S ? lhs.slt(rhs) : lhs.ult(rhs);
+    else if (_AP_W < 32 && _AP_W2 < 32)
+      return lhs.slt(rhs);
+    else if (_AP_S)
+      if (_AP_W2 >= _AP_W)
+        return lhs.ult(rhs);
+      else
+        return lhs.slt(rhs);
+    else if (_AP_W >= _AP_W2)
+      return lhs.ult(rhs);
+    else
+      return lhs.slt(rhs);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>=(const ap_private<_AP_W2, _AP_S2>& op) const {
+    return !(*this < op);
+  }
+
+  /// Bit and Part Select
+  //--------------------------------------------------------------
+  // FIXME now _private_range_ref refs to _AP_ROOT_TYPE(struct ssdm_int).
+  INLINE _private_range_ref<_AP_W, _AP_S> operator()(int Hi, int Lo) {
+    return _private_range_ref<_AP_W, _AP_S>(this, Hi, Lo);
+  }
+
+  INLINE _private_range_ref<_AP_W, _AP_S> operator()(int Hi, int Lo) const {
+    return _private_range_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>*>(this), Hi, Lo);
+  }
+
+  INLINE _private_range_ref<_AP_W, _AP_S> range(int Hi, int Lo) const {
+    return _private_range_ref<_AP_W, _AP_S>(
+        (const_cast<ap_private<_AP_W, _AP_S>*>(this)), Hi, Lo);
+  }
+
+  INLINE _private_range_ref<_AP_W, _AP_S> range(int Hi, int Lo) {
+    return _private_range_ref<_AP_W, _AP_S>(this, Hi, Lo);
+  }
+
+  INLINE _private_bit_ref<_AP_W, _AP_S> operator[](int index) {
+    return _private_bit_ref<_AP_W, _AP_S>(*this, index);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_bit_ref<_AP_W, _AP_S> operator[](
+      const ap_private<_AP_W2, _AP_S2>& index) {
+    return _private_bit_ref<_AP_W, _AP_S>(*this, index.to_int());
+  }
+
+  INLINE const _private_bit_ref<_AP_W, _AP_S> operator[](int index) const {
+    return _private_bit_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>&>(*this), index);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE const _private_bit_ref<_AP_W, _AP_S> operator[](
+      const ap_private<_AP_W2, _AP_S2>& index) const {
+    return _private_bit_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>&>(*this), index.to_int());
+  }
+
+  INLINE _private_bit_ref<_AP_W, _AP_S> bit(int index) {
+    return _private_bit_ref<_AP_W, _AP_S>(*this, index);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_bit_ref<_AP_W, _AP_S> bit(const ap_private<_AP_W2, _AP_S2>& index) {
+    return _private_bit_ref<_AP_W, _AP_S>(*this, index.to_int());
+  }
+
+  INLINE const _private_bit_ref<_AP_W, _AP_S> bit(int index) const {
+    return _private_bit_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>&>(*this), index);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE const _private_bit_ref<_AP_W, _AP_S> bit(
+      const ap_private<_AP_W2, _AP_S2>& index) const {
+    return _private_bit_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>&>(*this), index.to_int());
+  }
+
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                       ap_private<_AP_W2, _AP_S2> >
+//  concat(const ap_private<_AP_W2, _AP_S2>& a2) const {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<ap_private<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                       ap_private<_AP_W2, _AP_S2> >
+//  concat(ap_private<_AP_W2, _AP_S2>& a2) {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private, _AP_W2, ap_private<_AP_W2, _AP_S2> >
+//  operator,(const ap_private<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<ap_private<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private, _AP_W2, ap_private<_AP_W2, _AP_S2> >
+//  operator,(const ap_private<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(
+//        *this, const_cast<ap_private<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private, _AP_W2, ap_private<_AP_W2, _AP_S2> >
+//  operator,(ap_private<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this), a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private, _AP_W2, ap_private<_AP_W2, _AP_S2> >
+//  operator,(ap_private<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                       _private_range_ref<_AP_W2, _AP_S2> >
+//  operator,(const _private_range_ref<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                         _private_range_ref<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<_private_range_ref<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                       _private_range_ref<_AP_W2, _AP_S2> >
+//  operator,(_private_range_ref<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                         _private_range_ref<_AP_W2, _AP_S2> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, 1,
+//                       _private_bit_ref<_AP_W2, _AP_S2> >
+//  operator,(const _private_bit_ref<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, 1,
+//                         _private_bit_ref<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<_private_bit_ref<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, 1,
+//                       _private_bit_ref<_AP_W2, _AP_S2> >
+//  operator,(_private_bit_ref<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, 1,
+//                         _private_bit_ref<_AP_W2, _AP_S2> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2 + _AP_W3,
+//                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+//  operator,(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2 + _AP_W3,
+//                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>&>(a2));
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2 + _AP_W3,
+//                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+//  operator,(ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2 + _AP_W3,
+//                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(*this,
+//                                                                         a2);
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_concat_ref<
+//      _AP_W, ap_private, _AP_W2,
+//      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//  operator,(const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>
+//                &a2) const {
+//    return ap_concat_ref<
+//        _AP_W, ap_private, _AP_W2,
+//        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<
+//            af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_concat_ref<
+//      _AP_W, ap_private, _AP_W2,
+//      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//  operator,(af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &a2) {
+//    return ap_concat_ref<
+//        _AP_W, ap_private, _AP_W2,
+//        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(*this,
+//                                                                       a2);
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE
+//      ap_concat_ref<_AP_W, ap_private, 1,
+//                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//      operator,(const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>
+//                    &a2) const {
+//    return ap_concat_ref<
+//        _AP_W, ap_private, 1,
+//        af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(
+//            a2));
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE
+//      ap_concat_ref<_AP_W, ap_private, 1,
+//                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//      operator,(
+//          af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &a2) {
+//    return ap_concat_ref<
+//        _AP_W, ap_private, 1,
+//        af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_private<AP_MAX(_AP_W2 + _AP_W3, _AP_W), _AP_S> operator&(
+//      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& a2) {
+//    return *this & a2.get();
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_private<AP_MAX(_AP_W2 + _AP_W3, _AP_W), _AP_S> operator|(
+//      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& a2) {
+//    return *this | a2.get();
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_private<AP_MAX(_AP_W2 + _AP_W3, _AP_W), _AP_S> operator^(
+//      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& a2) {
+//    return *this ^ a2.get();
+//  }
+
+  // Reduce operation
+  //-----------------------------------------------------------
+  INLINE bool and_reduce() const { return (VAL & mask) == mask; }
+
+  INLINE bool nand_reduce() const { return (VAL & mask) != mask; }
+
+  INLINE bool or_reduce() const { return (bool)VAL; }
+
+  INLINE bool nor_reduce() const { return VAL == 0; }
+
+  INLINE bool xor_reduce() const {
+    unsigned int i = countPopulation();
+    return (i % 2) ? true : false;
+  }
+
+  INLINE bool xnor_reduce() const {
+    unsigned int i = countPopulation();
+    return (i % 2) ? false : true;
+  }
+
+  INLINE std::string to_string(uint8_t radix = 2, bool sign = false) const {
+    return toString(radix, radix == 10 ? _AP_S : sign);
+  }
+}; // End of class ap_private <_AP_W, _AP_S, true>
+
+template <int _AP_W, bool _AP_S>
+std::string ap_private<_AP_W, _AP_S, true>::toString(uint8_t radix,
+                                                     bool wantSigned) const {
+  assert((radix == 10 || radix == 8 || radix == 16 || radix == 2) &&
+         "Radix should be 2, 8, 10, or 16!");
+  static const char* digits[] = {"0", "1", "2", "3", "4", "5", "6", "7",
+                                 "8", "9", "a", "b", "c", "d", "e", "f"};
+  std::string result;
+  if (radix != 10) {
+    // For the 2, 8 and 16 bit cases, we can just shift instead of divide
+    // because the number of bits per digit (1,3 and 4 respectively) divides
+    // equaly. We just shift until there value is zero.
+
+    // First, check for a zero value and just short circuit the logic below.
+    if (*this == (uint64_t)(0)) {
+      // Always generate a radix indicator because fixed-point
+      // formats require it.
+      switch (radix) {
+        case 2:
+          result = "0b0";
+          break;
+        case 8:
+          result = "0o0";
+          break;
+        case 16:
+          result = "0x0";
+          break;
+        default:
+          assert("invalid radix" && 0);
+      }
+    } else {
+      ap_private<_AP_W, false, true> tmp(*this);
+      size_t insert_at = 0;
+      bool leading_zero = true;
+      if (wantSigned && isNegative()) {
+        // They want to print the signed version and it is a negative value
+        // Flip the bits and add one to turn it into the equivalent positive
+        // value and put a '-' in the result.
+        tmp.flip();
+        tmp++;
+        result = "-";
+        insert_at = 1;
+        leading_zero = false;
+      }
+      switch (radix) {
+        case 2:
+          result += "0b";
+          break;
+        case 8:
+          result += "0o";
+          break;
+        case 16:
+          result += "0x";
+          break;
+        default:
+          assert("invalid radix" && 0);
+      }
+      insert_at += 2;
+
+      // Just shift tmp right for each digit width until it becomes zero
+      uint32_t shift = (radix == 16 ? 4 : (radix == 8 ? 3 : 1));
+      uint64_t mask = radix - 1;
+      ap_private<_AP_W, false, true> zero(0);
+      unsigned bits = 0;
+      bool msb = false;
+      while (tmp.ne(zero)) {
+        unsigned digit = (unsigned)(tmp.get_VAL() & mask);
+        result.insert(insert_at, digits[digit]);
+        tmp = tmp.lshr(shift);
+        bits++;
+        msb = (digit >> (shift - 1)) == 1;
+      }
+      bits *= shift;
+      if (bits < _AP_W && leading_zero && msb)
+        result.insert(insert_at, digits[0]);
+    }
+    return result;
+  }
+
+  ap_private<_AP_W, false, true> tmp(*this);
+  ap_private<6, false, true> divisor(radix);
+  ap_private<_AP_W, _AP_S, true> zero(0);
+  size_t insert_at = 0;
+  if (wantSigned && isNegative()) {
+    // They want to print the signed version and it is a negative value
+    // Flip the bits and add one to turn it into the equivalent positive
+    // value and put a '-' in the result.
+    tmp.flip();
+    tmp++;
+    result = "-";
+    insert_at = 1;
+  }
+  if (tmp == ap_private<_AP_W, false, true>(0ULL))
+    result = "0";
+  else
+    while (tmp.ne(zero)) {
+      ap_private<_AP_W, false, true> APdigit = tmp % divisor;
+      ap_private<_AP_W, false, true> tmp2 = tmp / divisor;
+      uint32_t digit = (uint32_t)(APdigit.getZExtValue());
+      assert(digit < radix && "divide failed");
+      result.insert(insert_at, digits[digit]);
+      tmp = tmp2;
+    }
+  return result;
+
+} // End of ap_private<_AP_W, _AP_S, true>::toString()
+
+// bitwidth > 64
+template <int _AP_W, bool _AP_S>
+class ap_private<_AP_W, _AP_S, false> {
+  // SFINAE pattern.  Only consider this class when _AP_W > 64
+  const static bool valid = ap_private_enable_if<(_AP_W > 64)>::isValid;
+
+#ifdef _MSC_VER
+#pragma warning(disable : 4521 4522)
+#endif
+ public:
+  enum { BitWidth = _AP_W, _AP_N = (_AP_W + 63) / 64 };
+  static const int width = _AP_W;
+
+ private:
+  /// This constructor is used only internally for speed of construction of
+  /// temporaries. It is unsafe for general use so it is not public.
+
+  /* Constructors */
+  /// Note that numWords can be smaller or larger than the corresponding bit
+  /// width but any extraneous bits will be dropped.
+  /// @param numWords the number of words in bigVal
+  /// @param bigVal a sequence of words to form the initial value of the
+  /// ap_private
+  /// @brief Construct an ap_private, initialized as bigVal[].
+  INLINE ap_private(uint32_t numWords, const uint64_t bigVal[]) {
+    set_canary();
+    assert(bigVal && "Null pointer detected!");
+    {
+      // Get memory, cleared to 0
+      memset(pVal, 0, _AP_N * sizeof(uint64_t));
+
+      // Calculate the number of words to copy
+      uint32_t words = AESL_std::min<uint32_t>(numWords, _AP_N);
+      // Copy the words from bigVal to pVal
+      memcpy(pVal, bigVal, words * APINT_WORD_SIZE);
+      if (words >= _AP_W) clearUnusedBits();
+      // Make sure unused high bits are cleared
+    }
+    check_canary();
+  }
+
+  /// This constructor interprets Val as a string in the given radix. The
+  /// interpretation stops when the first charater that is not suitable for the
+  /// radix is encountered. Acceptable radix values are 2, 8, 10 and 16. It is
+  /// an error for the value implied by the string to require more bits than
+  /// numBits.
+  /// @param val the string to be interpreted
+  /// @param radix the radix of Val to use for the intepretation
+  /// @brief Construct an ap_private from a string representation.
+  INLINE ap_private(const std::string& val, uint8_t radix = 2) {
+    set_canary();
+    assert(!val.empty() && "The input string is empty.");
+    const char* c_str = val.c_str();
+    fromString(c_str, val.size(), radix);
+    check_canary();
+  }
+
+  /// This constructor interprets the slen characters starting at StrStart as
+  /// a string in the given radix. The interpretation stops when the first
+  /// character that is not suitable for the radix is encountered. Acceptable
+  /// radix values are 2, 8, 10 and 16. It is an error for the value implied by
+  /// the string to require more bits than numBits.
+  /// @param strStart the start of the string to be interpreted
+  /// @param slen the maximum number of characters to interpret
+  /// @param radix the radix to use for the conversion
+  /// @brief Construct an ap_private from a string representation.
+  /// This method does not consider whether it is negative or not.
+  INLINE ap_private(const char strStart[], uint32_t slen, uint8_t radix) {
+    set_canary();
+    fromString(strStart, slen, radix);
+    check_canary();
+  }
+
+  INLINE void report() {
+    _AP_ERROR(_AP_W > MAX_MODE(AP_INT_MAX_W) * 1024,
+              "ap_%sint<%d>: Bitwidth exceeds the "
+              "default max value %d. Please use macro "
+              "AP_INT_MAX_W to set a larger max value.",
+              _AP_S ? "" : "u", _AP_W, MAX_MODE(AP_INT_MAX_W) * 1024);
+  }
+  /// This union is used to store the integer value. When the
+  /// integer bit-width <= 64, it uses VAL, otherwise it uses pVal.
+
+  /// This enum is used to hold the constants we needed for ap_private.
+  // uint64_t VAL;    ///< Used to store the <= 64 bits integer value.
+  uint64_t pVal[_AP_N]; ///< Used to store the >64 bits integer value.
+#ifdef AP_CANARY
+  uint64_t CANARY;
+  INLINE void check_canary() { assert(CANARY == (uint64_t)0xDEADBEEFDEADBEEF); }
+  INLINE void set_canary() { CANARY = (uint64_t)0xDEADBEEFDEADBEEF; }
+#else
+  INLINE void check_canary() {}
+  INLINE void set_canary() {}
+#endif
+
+ public:
+  typedef typename valtype<8, _AP_S>::Type ValType;
+  typedef ap_private<_AP_W, _AP_S> Type;
+  // FIXME remove friend type?
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  friend struct ap_fixed_base;
+  /// return type of variety of operations
+  //----------------------------------------------------------
+  template <int _AP_W2, bool _AP_S2>
+  struct RType {
+    enum {
+      mult_w = _AP_W + _AP_W2,
+      mult_s = _AP_S || _AP_S2,
+      plus_w =
+          AP_MAX(_AP_W + (_AP_S2 && !_AP_S), _AP_W2 + (_AP_S && !_AP_S2)) + 1,
+      plus_s = _AP_S || _AP_S2,
+      minus_w =
+          AP_MAX(_AP_W + (_AP_S2 && !_AP_S), _AP_W2 + (_AP_S && !_AP_S2)) + 1,
+      minus_s = true,
+      div_w = _AP_W + _AP_S2,
+      div_s = _AP_S || _AP_S2,
+      mod_w = AP_MIN(_AP_W, _AP_W2 + (!_AP_S2 && _AP_S)),
+      mod_s = _AP_S,
+      logic_w = AP_MAX(_AP_W + (_AP_S2 && !_AP_S), _AP_W2 + (_AP_S && !_AP_S2)),
+      logic_s = _AP_S || _AP_S2
+    };
+    typedef ap_private<mult_w, mult_s> mult;
+    typedef ap_private<plus_w, plus_s> plus;
+    typedef ap_private<minus_w, minus_s> minus;
+    typedef ap_private<logic_w, logic_s> logic;
+    typedef ap_private<div_w, div_s> div;
+    typedef ap_private<mod_w, mod_s> mod;
+    typedef ap_private<_AP_W, _AP_S> arg1;
+    typedef bool reduce;
+  };
+
+  INLINE uint64_t& get_VAL(void) { return pVal[0]; }
+  INLINE uint64_t get_VAL(void) const { return pVal[0]; }
+  INLINE uint64_t get_VAL(void) const volatile { return pVal[0]; }
+  INLINE void set_VAL(uint64_t value) { pVal[0] = value; }
+  INLINE uint64_t& get_pVal(int index) { return pVal[index]; }
+  INLINE uint64_t* get_pVal() { return pVal; }
+  INLINE const uint64_t* get_pVal() const { return pVal; }
+  INLINE uint64_t get_pVal(int index) const { return pVal[index]; }
+  INLINE uint64_t* get_pVal() const volatile { return pVal; }
+  INLINE uint64_t get_pVal(int index) const volatile { return pVal[index]; }
+  INLINE void set_pVal(int i, uint64_t value) { pVal[i] = value; }
+
+  /// This enum is used to hold the constants we needed for ap_private.
+  enum {
+    APINT_BITS_PER_WORD = sizeof(uint64_t) * 8, ///< Bits in a word
+    APINT_WORD_SIZE = sizeof(uint64_t)          ///< Byte size of a word
+  };
+
+  enum {
+    excess_bits = (_AP_W % APINT_BITS_PER_WORD)
+                      ? APINT_BITS_PER_WORD - (_AP_W % APINT_BITS_PER_WORD)
+                      : 0
+  };
+  static const uint64_t mask = ((uint64_t)~0ULL >> (excess_bits));
+
+ public:
+  // NOTE changed to explicit to be consistent with ap_private<W,S,true>
+  explicit INLINE ap_private(const char* val) {
+    set_canary();
+    unsigned char radix = 10;
+    std::string str = ap_private_ops::parseString(val, radix); // determine radix.
+    std::string::size_type pos = str.find('.');
+    if (pos != std::string::npos) str = str.substr(pos);
+    ap_private ap_private_val(str, radix);
+    operator=(ap_private_val);
+    report();
+    check_canary();
+  }
+
+  INLINE ap_private(const char* val, unsigned char rd) {
+    set_canary();
+    unsigned char radix = rd;
+    std::string str = ap_private_ops::parseString(val, radix); // determine radix.
+    std::string::size_type pos = str.find('.');
+    if (pos != std::string::npos) str = str.substr(pos);
+    ap_private ap_private_val(str, radix);
+    operator=(ap_private_val);
+    report();
+
+    report();
+    check_canary();
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private(const _private_range_ref<_AP_W2, _AP_S2>& ref) {
+    set_canary();
+    *this = ref.get();
+    report();
+    check_canary();
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private(const _private_bit_ref<_AP_W2, _AP_S2>& ref) {
+    set_canary();
+    *this = ((uint64_t)(bool)ref);
+    report();
+    check_canary();
+  }
+
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_private(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& ref) {
+//    set_canary();
+//    *this = ref.get();
+//    report();
+//    check_canary();
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_private(
+//      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+//    set_canary();
+//    *this = ((val.operator ap_private<_AP_W2, false>()));
+//    report();
+//    check_canary();
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_private(
+//      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+//    set_canary();
+//    *this = (uint64_t)(bool)val;
+//    report();
+//    check_canary();
+//  }
+
+  /// Simply makes *this a copy of that.
+  /// @brief Copy Constructor.
+  INLINE ap_private(const ap_private& that) {
+      set_canary();
+      memcpy(pVal, that.get_pVal(), _AP_N * APINT_WORD_SIZE);
+      clearUnusedBits();
+      check_canary();
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private(const ap_private<_AP_W1, _AP_S1, false>& that) {
+    set_canary();
+    operator=(that);
+    check_canary();
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private(const volatile ap_private<_AP_W1, _AP_S1, false>& that) {
+    set_canary();
+    operator=(const_cast<const ap_private<_AP_W1, _AP_S1, false>&>(that));
+    check_canary();
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private(const ap_private<_AP_W1, _AP_S1, true>& that) {
+    set_canary();
+    static const uint64_t that_sign_ext_mask =
+        (_AP_W1 == APINT_BITS_PER_WORD)
+            ? 0
+            : ~0ULL >> (_AP_W1 % APINT_BITS_PER_WORD)
+                           << (_AP_W1 % APINT_BITS_PER_WORD);
+    if (that.isNegative()) {
+      pVal[0] = that.get_VAL() | that_sign_ext_mask;
+      memset(pVal + 1, ~0, sizeof(uint64_t) * (_AP_N - 1));
+    } else {
+      pVal[0] = that.get_VAL();
+      memset(pVal + 1, 0, sizeof(uint64_t) * (_AP_N - 1));
+    }
+    clearUnusedBits();
+    check_canary();
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private(const volatile ap_private<_AP_W1, _AP_S1, true>& that) {
+    set_canary();
+    operator=(const_cast<const ap_private<_AP_W1, _AP_S1, true>&>(that));
+    check_canary();
+  }
+
+  /// @brief Destructor.
+  // virtual ~ap_private() {}
+  INLINE ~ap_private() { check_canary(); }
+
+  /// @name Constructors
+  /// @{
+
+  /// Default constructor that creates an uninitialized ap_private.  This is
+  /// useful
+  ///  for object deserialization (pair this with the static method Read).
+  INLINE ap_private() {
+    set_canary();
+    clearUnusedBits();
+    check_canary();
+  }
+
+  INLINE ap_private(uint64_t* val, uint32_t bits = _AP_W) { assert(0); }
+  INLINE ap_private(const uint64_t* const val, uint32_t bits) { assert(0); }
+
+/// If isSigned is true then val is treated as if it were a signed value
+/// (i.e. as an int64_t) and the appropriate sign extension to the bit width
+/// will be done. Otherwise, no sign extension occurs (high order bits beyond
+/// the range of val are zero filled).
+/// @param numBits the bit width of the constructed ap_private
+/// @param val the initial value of the ap_private
+/// @param isSigned how to treat signedness of val
+/// @brief Create a new ap_private of numBits width, initialized as val.
+#define CTOR(TYPE, SIGNED)                                  \
+  INLINE ap_private(TYPE val, bool isSigned = SIGNED) {     \
+    set_canary();                                           \
+    pVal[0] = (ValType)val;                                 \
+    if (isSigned && int64_t(pVal[0]) < 0) {                 \
+      memset(pVal + 1, ~0, sizeof(uint64_t) * (_AP_N - 1)); \
+    } else {                                                \
+      memset(pVal + 1, 0, sizeof(uint64_t) * (_AP_N - 1));  \
+    }                                                       \
+    clearUnusedBits();                                      \
+    check_canary();                                         \
+  }
+
+  CTOR(bool, false)
+  CTOR(char, CHAR_IS_SIGNED)
+  CTOR(signed char, true)
+  CTOR(unsigned char, false)
+  CTOR(short, true)
+  CTOR(unsigned short, false)
+  CTOR(int, true)
+  CTOR(unsigned int, false)
+  CTOR(long, true)
+  CTOR(unsigned long, false)
+  CTOR(ap_slong, true)
+  CTOR(ap_ulong, false)
+#if 0
+  CTOR(half, false)
+  CTOR(float, false)
+  CTOR(double, false)
+#endif
+#undef CTOR
+
+  /// @returns true if the number of bits <= 64, false otherwise.
+  /// @brief Determine if this ap_private just has one word to store value.
+  INLINE bool isSingleWord() const { return false; }
+
+  /// @returns the word position for the specified bit position.
+  /// @brief Determine which word a bit is in.
+  static INLINE uint32_t whichWord(uint32_t bitPosition) {
+    //    return bitPosition / APINT_BITS_PER_WORD;
+    return (bitPosition) >> 6;
+  }
+
+  /// @returns the bit position in a word for the specified bit position
+  /// in the ap_private.
+  /// @brief Determine which bit in a word a bit is in.
+  static INLINE uint32_t whichBit(uint32_t bitPosition) {
+    //    return bitPosition % APINT_BITS_PER_WORD;
+    return bitPosition & 0x3f;
+  }
+
+  /// bit at a specific bit position. This is used to mask the bit in the
+  /// corresponding word.
+  /// @returns a uint64_t with only bit at "whichBit(bitPosition)" set
+  /// @brief Get a single bit mask.
+  static INLINE uint64_t maskBit(uint32_t bitPosition) {
+    return 1ULL << (whichBit(bitPosition));
+  }
+
+  /// @returns the corresponding word for the specified bit position.
+  /// @brief Get the word corresponding to a bit position
+  INLINE uint64_t getWord(uint32_t bitPosition) const {
+    return pVal[whichWord(bitPosition)];
+  }
+
+  /// This method is used internally to clear the to "N" bits in the high order
+  /// word that are not used by the ap_private. This is needed after the most
+  /// significant word is assigned a value to ensure that those bits are
+  /// zero'd out.
+  /// @brief Clear unused high order bits
+  INLINE void clearUnusedBits(void) volatile
+// just for clang compiler
+#if defined(__clang__) && !defined(__CLANG_3_1__)
+      __attribute__((no_sanitize("undefined")))
+#endif
+  {
+    pVal[_AP_N - 1] =
+        _AP_S ? ((((int64_t)pVal[_AP_N - 1]) << (excess_bits)) >> excess_bits)
+              : (excess_bits
+                     ? ((pVal[_AP_N - 1]) << (excess_bits)) >> (excess_bits)
+                     : pVal[_AP_N - 1]);
+  }
+
+  INLINE void clearUnusedBitsToZero(void) { pVal[_AP_N - 1] &= mask; }
+
+  INLINE void clearUnusedBitsToOne(void) { pVal[_AP_N - 1] |= mask; }
+
+  /// This is used by the constructors that take string arguments.
+  /// @brief Convert a char array into an ap_private
+  INLINE void fromString(const char* str, uint32_t slen, uint8_t radix) {
+    enum { numbits = _AP_W };
+    bool isNeg = str[0] == '-';
+    if (isNeg) {
+      str++;
+      slen--;
+    }
+
+    if (str[0] == '0' && (str[1] == 'b' || str[1] == 'B')) {
+      //if(radix == 0) radix = 2;
+      _AP_WARNING(radix != 2, "%s seems to have base %d, but %d given.", str, 2, radix);
+      str += 2;
+      slen -=2;
+    } else if (str[0] == '0' && (str[1] == 'o' || str[1] == 'O')) {
+      //if (radix == 0) radix = 8;
+      _AP_WARNING(radix != 8, "%s seems to have base %d, but %d given.", str, 8, radix);
+      str += 2;
+      slen -=2;
+    } else if (str[0] == '0' && (str[1] == 'x' || str[1] == 'X')) {
+      //if (radix == 0) radix = 16;
+      _AP_WARNING(radix != 16, "%s seems to have base %d, but %d given.", str, 16, radix);
+      str += 2;
+      slen -=2;
+    } else if (str[0] == '0' && (str[1] == 'd' || str[1] == 'D')) {
+      //if (radix == 0) radix = 10;
+      _AP_WARNING(radix != 10, "%s seems to have base %d, but %d given.", str, 10, radix);
+      str += 2;
+      slen -=2;
+    } else if (radix == 0) {
+      //radix = 2; // XXX default value
+    }
+
+    // Check our assumptions here
+    assert((radix == 10 || radix == 8 || radix == 16 || radix == 2) &&
+           "Radix should be 2, 8, 10, or 16!");
+    assert(str && "String is null?");
+
+    // skip any leading zero
+    while (*str == '0' && *(str + 1) != '\0') {
+      str++;
+      slen--;
+    }
+    assert((slen <= numbits || radix != 2) && "Insufficient bit width");
+    assert(((slen - 1) * 3 <= numbits || radix != 8) &&
+           "Insufficient bit width");
+    assert(((slen - 1) * 4 <= numbits || radix != 16) &&
+           "Insufficient bit width");
+    assert((((slen - 1) * 64) / 22 <= numbits || radix != 10) &&
+           "Insufficient bit width");
+
+    // clear bits
+    memset(pVal, 0, _AP_N * sizeof(uint64_t));
+
+    // Figure out if we can shift instead of multiply
+    uint32_t shift = (radix == 16 ? 4 : radix == 8 ? 3 : radix == 2 ? 1 : 0);
+
+    // Set up an ap_private for the digit to add outside the loop so we don't
+    // constantly construct/destruct it.
+    uint64_t bigVal[_AP_N];
+    memset(bigVal, 0, _AP_N * sizeof(uint64_t));
+    ap_private<_AP_W, _AP_S> apdigit(getBitWidth(), bigVal);
+    ap_private<_AP_W, _AP_S> apradix(radix);
+
+    // Enter digit traversal loop
+    for (unsigned i = 0; i < slen; i++) {
+      // Get a digit
+      uint32_t digit = 0;
+      char cdigit = str[i];
+      if (radix == 16) {
+#define isxdigit(c)                                            \
+  (((c) >= '0' && (c) <= '9') || ((c) >= 'a' && (c) <= 'f') || \
+   ((c) >= 'A' && (c) <= 'F'))
+#define isdigit(c) ((c) >= '0' && (c) <= '9')
+        if (!isxdigit(cdigit)) assert(0 && "Invalid hex digit in string");
+        if (isdigit(cdigit))
+          digit = cdigit - '0';
+        else if (cdigit >= 'a')
+          digit = cdigit - 'a' + 10;
+        else if (cdigit >= 'A')
+          digit = cdigit - 'A' + 10;
+        else
+          assert(0 && "huh? we shouldn't get here");
+      } else if (isdigit(cdigit)) {
+        digit = cdigit - '0';
+      } else if (cdigit != '\0') {
+        assert(0 && "Invalid character in digit string");
+      }
+#undef isxdigit
+#undef isdigit
+      // Shift or multiply the value by the radix
+      if (shift)
+        *this <<= shift;
+      else
+        *this *= apradix;
+
+      // Add in the digit we just interpreted
+      apdigit.set_VAL(digit);
+      *this += apdigit;
+    }
+    // If its negative, put it in two's complement form
+    if (isNeg) {
+      (*this)--;
+      this->flip();
+    }
+    clearUnusedBits();
+  }
+
+  INLINE ap_private read() volatile { return *this; }
+
+  INLINE void write(const ap_private& op2) volatile { *this = (op2); }
+
+  INLINE operator ValType() const { return get_VAL(); }
+
+  INLINE int to_uchar() const { return (unsigned char)get_VAL(); }
+
+  INLINE int to_char() const { return (signed char)get_VAL(); }
+
+  INLINE int to_ushort() const { return (unsigned short)get_VAL(); }
+
+  INLINE int to_short() const { return (short)get_VAL(); }
+
+  INLINE int to_int() const { return (int)get_VAL(); }
+
+  INLINE unsigned to_uint() const { return (unsigned)get_VAL(); }
+
+  INLINE long to_long() const { return (long)get_VAL(); }
+
+  INLINE unsigned long to_ulong() const { return (unsigned long)get_VAL(); }
+
+  INLINE ap_slong to_int64() const { return (ap_slong)get_VAL(); }
+
+  INLINE ap_ulong to_uint64() const { return (ap_ulong)get_VAL(); }
+
+  INLINE double to_double() const {
+    if (isNegative())
+      return roundToDouble(true);
+    else
+      return roundToDouble(false);
+  }
+
+  INLINE unsigned length() const { return _AP_W; }
+
+  /*Reverse the contents of ap_private instance. I.e. LSB becomes MSB and vise
+   * versa*/
+  INLINE ap_private& reverse() {
+    for (int i = 0; i < _AP_W / 2; ++i) {
+      bool tmp = operator[](i);
+      if (operator[](_AP_W - 1 - i))
+        set(i);
+      else
+        clear(i);
+      if (tmp)
+        set(_AP_W - 1 - i);
+      else
+        clear(_AP_W - 1 - i);
+    }
+    clearUnusedBits();
+    return *this;
+  }
+
+  /*Return true if the value of ap_private instance is zero*/
+  INLINE bool iszero() const { return isMinValue(); }
+
+  INLINE bool to_bool() const { return !iszero(); }
+
+  /* x < 0 */
+  INLINE bool sign() const {
+    if (isNegative()) return true;
+    return false;
+  }
+
+  /* x[i] = !x[i] */
+  INLINE void invert(int i) {
+    assert(i >= 0 && "Attempting to read bit with negative index");
+    assert(i < _AP_W && "Attempting to read bit beyond MSB");
+    flip(i);
+  }
+
+  /* x[i] */
+  INLINE bool test(int i) const {
+    assert(i >= 0 && "Attempting to read bit with negative index");
+    assert(i < _AP_W && "Attempting to read bit beyond MSB");
+    return operator[](i);
+  }
+
+  // Set the ith bit into v
+  INLINE void set(int i, bool v) {
+    assert(i >= 0 && "Attempting to write bit with negative index");
+    assert(i < _AP_W && "Attempting to write bit beyond MSB");
+    v ? set(i) : clear(i);
+  }
+
+  // Set the ith bit into v
+  INLINE void set_bit(int i, bool v) {
+    assert(i >= 0 && "Attempting to write bit with negative index");
+    assert(i < _AP_W && "Attempting to write bit beyond MSB");
+    v ? set(i) : clear(i);
+  }
+
+  // FIXME different argument for different action?
+  INLINE ap_private& set(uint32_t bitPosition) {
+    pVal[whichWord(bitPosition)] |= maskBit(bitPosition);
+    clearUnusedBits();
+    return *this;
+  }
+
+  INLINE void set() {
+    for (int i = 0; i < _AP_N; ++i) pVal[i] = ~0ULL;
+    clearUnusedBits();
+  }
+
+  // Get the value of ith bit
+  INLINE bool get(int i) const {
+    assert(i >= 0 && "Attempting to read bit with negative index");
+    assert(i < _AP_W && "Attempting to read bit beyond MSB");
+    return ((maskBit(i) & (pVal[whichWord(i)])) != 0);
+  }
+
+  // Get the value of ith bit
+  INLINE bool get_bit(int i) const {
+    assert(i >= 0 && "Attempting to read bit with negative index");
+    assert(i < _AP_W && "Attempting to read bit beyond MSB");
+    return ((maskBit(i) & (pVal[whichWord(i)])) != 0);
+  }
+
+  // This is used for sc_lv and sc_bv, which is implemented by sc_uint
+  // Rotate an ap_private object n places to the left
+  INLINE void lrotate(int n) {
+    assert(n >= 0 && "Attempting to shift negative index");
+    assert(n < _AP_W && "Shift value larger than bit width");
+    operator=(shl(n) | lshr(_AP_W - n));
+  }
+
+  // This is used for sc_lv and sc_bv, which is implemented by sc_uint
+  // Rotate an ap_private object n places to the right
+  INLINE void rrotate(int n) {
+    assert(n >= 0 && "Attempting to shift negative index");
+    assert(n < _AP_W && "Shift value larger than bit width");
+    operator=(lshr(n) | shl(_AP_W - n));
+  }
+
+  /// Set the given bit to 0 whose position is given as "bitPosition".
+  /// @brief Set a given bit to 0.
+  INLINE ap_private& clear(uint32_t bitPosition) {
+    pVal[whichWord(bitPosition)] &= ~maskBit(bitPosition);
+    clearUnusedBits();
+    return *this;
+  }
+
+  /// @brief Set every bit to 0.
+  INLINE void clear() { memset(pVal, 0, _AP_N * APINT_WORD_SIZE); }
+
+  /// @brief Toggle every bit to its opposite value.
+  ap_private& flip() {
+    for (int i = 0; i < _AP_N; ++i) pVal[i] ^= ~0ULL;
+    clearUnusedBits();
+    return *this;
+  }
+
+  /// @brief Toggles a given bit to its opposite value.
+  INLINE ap_private& flip(uint32_t bitPosition) {
+    assert(bitPosition < BitWidth && "Out of the bit-width range!");
+    set_bit(bitPosition, !get_bit(bitPosition));
+    return *this;
+  }
+
+  // complements every bit
+  INLINE void b_not() { flip(); }
+
+  INLINE ap_private getLoBits(uint32_t numBits) const {
+    return ap_private_ops::lshr(ap_private_ops::shl(*this, _AP_W - numBits),
+                                _AP_W - numBits);
+  }
+
+  INLINE ap_private getHiBits(uint32_t numBits) const {
+    return ap_private_ops::lshr(*this, _AP_W - numBits);
+  }
+
+  // Binary Arithmetic
+  //-----------------------------------------------------------
+
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_private<AP_MAX(_AP_W2 + _AP_W3, _AP_W), _AP_S> operator&(
+//      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& a2) {
+//    return *this & a2.get();
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_private<AP_MAX(_AP_W2 + _AP_W3, _AP_W), _AP_S> operator|(
+//      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& a2) {
+//    return *this | a2.get();
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_private<AP_MAX(_AP_W2 + _AP_W3, _AP_W), _AP_S> operator^(
+//      const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>& a2) {
+//    return *this ^ a2.get();
+//  }
+
+/// Arithmetic assign
+//-------------------------------------------------------------
+
+#define OP_BIN_LOGIC_ASSIGN_AP(Sym)                                            \
+  template <int _AP_W1, bool _AP_S1>                                           \
+  INLINE ap_private& operator Sym(const ap_private<_AP_W1, _AP_S1>& RHS) {     \
+    const int _AP_N1 = ap_private<_AP_W1, _AP_S1>::_AP_N;                      \
+    uint32_t numWords = AESL_std::min((int)_AP_N, _AP_N1);                     \
+    uint32_t i;                                                                \
+    if (_AP_W != _AP_W1)                                                       \
+      fprintf(stderr,                                                          \
+              "Warning! Bitsize mismach for ap_[u]int " #Sym " ap_[u]int.\n"); \
+    for (i = 0; i < numWords; ++i) pVal[i] Sym RHS.get_pVal(i);                \
+    if (_AP_N1 < _AP_N) {                                                      \
+      uint64_t ext = RHS.isNegative() ? ~0ULL : 0;                             \
+      for (; i < _AP_N; i++) pVal[i] Sym ext;                                  \
+    }                                                                          \
+    clearUnusedBits();                                                         \
+    return *this;                                                              \
+  }
+
+  OP_BIN_LOGIC_ASSIGN_AP(&=);
+  OP_BIN_LOGIC_ASSIGN_AP(|=);
+  OP_BIN_LOGIC_ASSIGN_AP(^=);
+#undef OP_BIN_LOGIC_ASSIGN_AP
+
+  /// Adds the RHS APint to this ap_private.
+  /// @returns this, after addition of RHS.
+  /// @brief Addition assignment operator.
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator+=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    const int _AP_N1 = ap_private<_AP_W1, _AP_S1>::_AP_N;
+    uint64_t RHSpVal[_AP_N1];
+    for (int i = 0; i < _AP_N1; ++i) RHSpVal[i] = RHS.get_pVal(i);
+    ap_private_ops::add(pVal, pVal, RHSpVal, _AP_N, _AP_N, _AP_N1, _AP_S,
+                        _AP_S1);
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator-=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    const int _AP_N1 = ap_private<_AP_W1, _AP_S1>::_AP_N;
+    uint64_t RHSpVal[_AP_N1];
+    for (int i = 0; i < _AP_N1; ++i) RHSpVal[i] = RHS.get_pVal(i);
+    ap_private_ops::sub(pVal, pVal, RHSpVal, _AP_N, _AP_N, _AP_N1, _AP_S,
+                        _AP_S1);
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator*=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    // Get some bit facts about LHS and check for zero
+    uint32_t lhsBits = getActiveBits();
+    uint32_t lhsWords = !lhsBits ? 0 : whichWord(lhsBits - 1) + 1;
+    if (!lhsWords) {
+      // 0 * X ===> 0
+      return *this;
+    }
+
+    ap_private dupRHS = RHS;
+    // Get some bit facts about RHS and check for zero
+    uint32_t rhsBits = dupRHS.getActiveBits();
+    uint32_t rhsWords = !rhsBits ? 0 : whichWord(rhsBits - 1) + 1;
+    if (!rhsWords) {
+      // X * 0 ===> 0
+      clear();
+      return *this;
+    }
+
+    // Allocate space for the result
+    uint32_t destWords = rhsWords + lhsWords;
+    uint64_t* dest = (uint64_t*)malloc(destWords * sizeof(uint64_t));
+
+    // Perform the long multiply
+    ap_private_ops::mul(dest, pVal, lhsWords, dupRHS.get_pVal(), rhsWords,
+                        destWords);
+
+    // Copy result back into *this
+    clear();
+    uint32_t wordsToCopy = destWords >= _AP_N ? _AP_N : destWords;
+
+    memcpy(pVal, dest, wordsToCopy * APINT_WORD_SIZE);
+
+    uint64_t ext = (isNegative() ^ RHS.isNegative()) ? ~0ULL : 0ULL;
+    for (int i = wordsToCopy; i < _AP_N; i++) pVal[i] = ext;
+    clearUnusedBits();
+    // delete dest array and return
+    free(dest);
+    return *this;
+  }
+
+#define OP_ASSIGN_AP(Sym)                                                    \
+  template <int _AP_W2, bool _AP_S2>                                         \
+  INLINE ap_private& operator Sym##=(const ap_private<_AP_W2, _AP_S2>& op) { \
+    *this = operator Sym(op);                                                \
+    return *this;                                                            \
+  }
+
+  OP_ASSIGN_AP(/)
+  OP_ASSIGN_AP(%)
+#undef OP_ASSIGN_AP
+
+#define OP_BIN_LOGIC_AP(Sym)                                                  \
+  template <int _AP_W1, bool _AP_S1>                                          \
+  INLINE typename RType<_AP_W1, _AP_S1>::logic operator Sym(                  \
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {                          \
+    enum {                                                                    \
+      numWords = (RType<_AP_W1, _AP_S1>::logic_w + APINT_BITS_PER_WORD - 1) / \
+                 APINT_BITS_PER_WORD                                          \
+    };                                                                        \
+    typename RType<_AP_W1, _AP_S1>::logic Result;                             \
+    uint32_t i;                                                               \
+    const int _AP_N1 = ap_private<_AP_W1, _AP_S1>::_AP_N;                     \
+    uint32_t min_N = std::min((int)_AP_N, _AP_N1);                            \
+    uint32_t max_N = std::max((int)_AP_N, _AP_N1);                            \
+    for (i = 0; i < min_N; ++i)                                               \
+      Result.set_pVal(i, pVal[i] Sym RHS.get_pVal(i));                        \
+    if (numWords > i) {                                                       \
+      uint64_t ext = ((_AP_N < _AP_N1 && isNegative()) ||                     \
+                      (_AP_N1 < _AP_N && RHS.isNegative()))                   \
+                         ? ~0ULL                                              \
+                         : 0;                                                 \
+      if (_AP_N > _AP_N1)                                                     \
+        for (; i < max_N; i++) Result.set_pVal(i, pVal[i] Sym ext);           \
+      else                                                                    \
+        for (; i < max_N; i++) Result.set_pVal(i, RHS.get_pVal(i) Sym ext);   \
+      if (numWords > i) {                                                     \
+        uint64_t ext2 = ((_AP_N > _AP_N1 && isNegative()) ||                  \
+                         (_AP_N1 > _AP_N && RHS.isNegative()))                \
+                            ? ~0ULL                                           \
+                            : 0;                                              \
+        Result.set_pVal(i, ext Sym ext2);                                     \
+      }                                                                       \
+    }                                                                         \
+    Result.clearUnusedBits();                                                 \
+    return Result;                                                            \
+  }
+
+  OP_BIN_LOGIC_AP(|);
+  OP_BIN_LOGIC_AP(&);
+  OP_BIN_LOGIC_AP(^);
+
+#undef OP_BIN_LOGIC_AP
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE typename RType<_AP_W1, _AP_S1>::plus operator+(
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    typename RType<_AP_W1, _AP_S1>::plus Result, lhs(*this), rhs(RHS);
+    const int Result_AP_N = (RType<_AP_W1, _AP_S1>::plus_w + 63) / 64;
+    ap_private_ops::add(Result.get_pVal(), lhs.get_pVal(), rhs.get_pVal(),
+                        Result_AP_N, Result_AP_N, Result_AP_N, _AP_S, _AP_S1);
+    Result.clearUnusedBits();
+    return Result;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE typename RType<_AP_W1, _AP_S1>::minus operator-(
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    typename RType<_AP_W1, _AP_S1>::minus Result, lhs(*this), rhs(RHS);
+    const int Result_AP_N = (RType<_AP_W1, _AP_S1>::minus_w + 63) / 64;
+    ap_private_ops::sub(Result.get_pVal(), lhs.get_pVal(), rhs.get_pVal(),
+                        Result_AP_N, Result_AP_N, Result_AP_N, _AP_S, _AP_S1);
+    Result.clearUnusedBits();
+    return Result;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE typename RType<_AP_W1, _AP_S1>::mult operator*(
+      const ap_private<_AP_W1, _AP_S1>& RHS) const {
+    typename RType<_AP_W1, _AP_S1>::mult temp = *this;
+    temp *= RHS;
+    return temp;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE typename RType<_AP_W2, _AP_S2>::div operator/(
+      const ap_private<_AP_W2, _AP_S2>& op) const {
+    ap_private<AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2)),
+               (_AP_W > _AP_W2 ? _AP_S
+                               : (_AP_W2 > _AP_W ? _AP_S2 : _AP_S || _AP_S2))>
+        lhs = *this;
+    ap_private<AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2)),
+               (_AP_W > _AP_W2 ? _AP_S
+                               : (_AP_W2 > _AP_W ? _AP_S2 : _AP_S || _AP_S2))>
+        rhs = op;
+    return typename RType<_AP_W2, _AP_S2>::div(
+        (_AP_S || _AP_S2) ? lhs.sdiv(rhs) : lhs.udiv(rhs));
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE typename RType<_AP_W2, _AP_S2>::mod operator%(
+      const ap_private<_AP_W2, _AP_S2>& op) const {
+    ap_private<AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2)),
+               (_AP_W > _AP_W2 ? _AP_S
+                               : (_AP_W2 > _AP_W ? _AP_S2 : _AP_S || _AP_S2))>
+        lhs = *this;
+    ap_private<AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2)),
+               (_AP_W > _AP_W2 ? _AP_S
+                               : (_AP_W2 > _AP_W ? _AP_S2 : _AP_S || _AP_S2))>
+        rhs = op;
+    typename RType<_AP_W2, _AP_S2>::mod res =
+        typename RType<_AP_W2, _AP_S2>::mod(_AP_S ? lhs.srem(rhs)
+                                                  : lhs.urem(rhs));
+    return res;
+  }
+
+#define OP_LEFT_SHIFT_CTYPE(TYPE, SIGNED)             \
+  INLINE ap_private operator<<(const TYPE op) const { \
+    if (op >= _AP_W) return ap_private(0);            \
+    if (SIGNED && op < 0) return *this >> (0 - op);   \
+    return shl(op);                                   \
+  }
+
+  OP_LEFT_SHIFT_CTYPE(int, true)
+  // OP_LEFT_SHIFT_CTYPE(bool, false)
+  OP_LEFT_SHIFT_CTYPE(signed char, true)
+  OP_LEFT_SHIFT_CTYPE(unsigned char, false)
+  OP_LEFT_SHIFT_CTYPE(short, true)
+  OP_LEFT_SHIFT_CTYPE(unsigned short, false)
+  OP_LEFT_SHIFT_CTYPE(unsigned int, false)
+  OP_LEFT_SHIFT_CTYPE(long, true)
+  OP_LEFT_SHIFT_CTYPE(unsigned long, false)
+  OP_LEFT_SHIFT_CTYPE(unsigned long long, false)
+  OP_LEFT_SHIFT_CTYPE(long long, true)
+#if 0
+  OP_LEFT_SHIFT_CTYPE(half, false)
+  OP_LEFT_SHIFT_CTYPE(float, false)
+  OP_LEFT_SHIFT_CTYPE(double, false)
+#endif
+#undef OP_LEFT_SHIFT_CTYPE
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private operator<<(const ap_private<_AP_W2, _AP_S2>& op2) const {
+    if (_AP_S2 == false) {
+      uint32_t sh = op2.to_uint();
+      return *this << sh;
+    } else {
+      int sh = op2.to_int();
+      return *this << sh;
+    }
+  }
+
+#define OP_RIGHT_SHIFT_CTYPE(TYPE, SIGNED)            \
+  INLINE ap_private operator>>(const TYPE op) const { \
+    if (op >= _AP_W) {                                \
+      if (isNegative())                               \
+        return ap_private(-1);                        \
+      else                                            \
+        return ap_private(0);                         \
+    }                                                 \
+    if ((SIGNED) && op < 0) return *this << (0 - op); \
+    if (_AP_S)                                        \
+      return ashr(op);                                \
+    else                                              \
+      return lshr(op);                                \
+  }
+
+  // OP_RIGHT_SHIFT_CTYPE(bool, false)
+  OP_RIGHT_SHIFT_CTYPE(char, CHAR_IS_SIGNED)
+  OP_RIGHT_SHIFT_CTYPE(signed char, true)
+  OP_RIGHT_SHIFT_CTYPE(unsigned char, false)
+  OP_RIGHT_SHIFT_CTYPE(short, true)
+  OP_RIGHT_SHIFT_CTYPE(unsigned short, false)
+  OP_RIGHT_SHIFT_CTYPE(int, true)
+  OP_RIGHT_SHIFT_CTYPE(unsigned int, false)
+  OP_RIGHT_SHIFT_CTYPE(long, true)
+  OP_RIGHT_SHIFT_CTYPE(unsigned long, false)
+  OP_RIGHT_SHIFT_CTYPE(unsigned long long, false)
+  OP_RIGHT_SHIFT_CTYPE(long long, true)
+#if 0
+  OP_RIGHT_SHIFT_CTYPE(half, false)
+  OP_RIGHT_SHIFT_CTYPE(float, false)
+  OP_RIGHT_SHIFT_CTYPE(double, false)
+#endif
+#undef OP_RIGHT_SHIFT_CTYPE
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private operator>>(const ap_private<_AP_W2, _AP_S2>& op2) const {
+    if (_AP_S2 == false) {
+      uint32_t sh = op2.to_uint();
+      return *this >> sh;
+    } else {
+      int sh = op2.to_int();
+      return *this >> sh;
+    }
+  }
+
+  /// Shift assign
+  //------------------------------------------------------------------
+  // TODO call clearUnusedBits ?
+#define OP_ASSIGN_AP(Sym)                                                    \
+  template <int _AP_W2, bool _AP_S2>                                         \
+  INLINE ap_private& operator Sym##=(int op) {                               \
+    *this = operator Sym(op);                                                \
+    return *this;                                                            \
+  }                                                                          \
+  INLINE ap_private& operator Sym##=(unsigned int op) {                      \
+    *this = operator Sym(op);                                                \
+    return *this;                                                            \
+  }                                                                          \
+  template <int _AP_W2, bool _AP_S2>                                         \
+  INLINE ap_private& operator Sym##=(const ap_private<_AP_W2, _AP_S2>& op) { \
+    *this = operator Sym(op);                                                \
+    return *this;                                                            \
+  }
+  OP_ASSIGN_AP(>>)
+  OP_ASSIGN_AP(<<)
+#undef OP_ASSIGN_AP
+
+  /// Comparisons
+  //-----------------------------------------------------------------
+  INLINE bool operator==(const ap_private& RHS) const {
+    // Get some facts about the number of bits used in the two operands.
+    uint32_t n1 = getActiveBits();
+    uint32_t n2 = RHS.getActiveBits();
+
+    // If the number of bits isn't the same, they aren't equal
+    if (n1 != n2) return false;
+
+    // If the number of bits fits in a word, we only need to compare the low
+    // word.
+    if (n1 <= APINT_BITS_PER_WORD) return pVal[0] == RHS.get_pVal(0);
+
+    // Otherwise, compare everything
+    for (int i = whichWord(n1 - 1); i >= 0; --i)
+      if (pVal[i] != RHS.get_pVal(i)) return false;
+    return true;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator==(const ap_private<_AP_W2, _AP_S2>& op) const {
+    enum {
+      _AP_MAX_W = AP_MAX(_AP_W, _AP_W2),
+    };
+    ap_private<_AP_MAX_W, false> lhs(*this);
+    ap_private<_AP_MAX_W, false> rhs(op);
+    return lhs == rhs;
+  }
+
+  INLINE bool operator==(uint64_t Val) const {
+    uint32_t n = getActiveBits();
+    if (n <= APINT_BITS_PER_WORD)
+      return pVal[0] == Val;
+    else
+      return false;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator!=(const ap_private<_AP_W2, _AP_S2>& op) const {
+    return !(*this == op);
+  }
+
+  template <bool _AP_S1>
+  INLINE bool operator!=(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return !((*this) == RHS);
+  }
+
+  INLINE bool operator!=(uint64_t Val) const { return !((*this) == Val); }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<=(const ap_private<_AP_W2, _AP_S2>& op) const {
+    return !(*this > op);
+  }
+
+  INLINE bool operator<(const ap_private& op) const {
+    return _AP_S ? slt(op) : ult(op);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<(const ap_private<_AP_W2, _AP_S2>& op) const {
+    enum {
+      _AP_MAX_W = AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2))
+    };
+    ap_private<_AP_MAX_W, _AP_S> lhs(*this);
+    ap_private<_AP_MAX_W, _AP_S2> rhs(op);
+    if (_AP_S == _AP_S2)
+      return _AP_S ? lhs.slt(rhs) : lhs.ult(rhs);
+    else if (_AP_S)
+      if (_AP_W2 >= _AP_W)
+        return lhs.ult(rhs);
+      else
+        return lhs.slt(rhs);
+    else if (_AP_W >= _AP_W2)
+      return lhs.ult(rhs);
+    else
+      return lhs.slt(rhs);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>=(const ap_private<_AP_W2, _AP_S2>& op) const {
+    return !(*this < op);
+  }
+
+  INLINE bool operator>(const ap_private& op) const {
+    return _AP_S ? sgt(op) : ugt(op);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>(const ap_private<_AP_W2, _AP_S2>& op) const {
+    enum {
+      _AP_MAX_W = AP_MAX(_AP_W + (_AP_S || _AP_S2), _AP_W2 + (_AP_S || _AP_S2))
+    };
+    ap_private<_AP_MAX_W, _AP_S> lhs(*this);
+    ap_private<_AP_MAX_W, _AP_S2> rhs(op);
+    if (_AP_S == _AP_S2)
+      return _AP_S ? lhs.sgt(rhs) : lhs.ugt(rhs);
+    else if (_AP_S)
+      if (_AP_W2 >= _AP_W)
+        return lhs.ugt(rhs);
+      else
+        return lhs.sgt(rhs);
+    else if (_AP_W >= _AP_W2)
+      return lhs.ugt(rhs);
+    else
+      return lhs.sgt(rhs);
+  }
+
+  /// Bit and Part Select
+  //--------------------------------------------------------------
+  INLINE _private_range_ref<_AP_W, _AP_S> operator()(int Hi, int Lo) {
+    return _private_range_ref<_AP_W, _AP_S>(this, Hi, Lo);
+  }
+
+  INLINE _private_range_ref<_AP_W, _AP_S> operator()(int Hi, int Lo) const {
+    return _private_range_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>*>(this), Hi, Lo);
+  }
+
+  INLINE _private_range_ref<_AP_W, _AP_S> range(int Hi, int Lo) const {
+    return _private_range_ref<_AP_W, _AP_S>(
+        (const_cast<ap_private<_AP_W, _AP_S>*>(this)), Hi, Lo);
+  }
+
+  INLINE _private_range_ref<_AP_W, _AP_S> range(int Hi, int Lo) {
+    return _private_range_ref<_AP_W, _AP_S>(this, Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE _private_range_ref<_AP_W, _AP_S> range(
+      const ap_private<_AP_W2, _AP_S2>& HiIdx,
+      const ap_private<_AP_W3, _AP_S3>& LoIdx) {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return _private_range_ref<_AP_W, _AP_S>(this, Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE _private_range_ref<_AP_W, _AP_S> operator()(
+      const ap_private<_AP_W2, _AP_S2>& HiIdx,
+      const ap_private<_AP_W3, _AP_S3>& LoIdx) {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return _private_range_ref<_AP_W, _AP_S>(this, Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE _private_range_ref<_AP_W, _AP_S> range(
+      const ap_private<_AP_W2, _AP_S2>& HiIdx,
+      const ap_private<_AP_W3, _AP_S3>& LoIdx) const {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return _private_range_ref<_AP_W, _AP_S>(const_cast<ap_private*>(this), Hi, Lo);
+  }
+
+  template <int _AP_W2, bool _AP_S2, int _AP_W3, bool _AP_S3>
+  INLINE _private_range_ref<_AP_W, _AP_S> operator()(
+      const ap_private<_AP_W2, _AP_S2>& HiIdx,
+      const ap_private<_AP_W3, _AP_S3>& LoIdx) const {
+    int Hi = HiIdx.to_int();
+    int Lo = LoIdx.to_int();
+    return this->range(Hi, Lo);
+  }
+
+  INLINE _private_bit_ref<_AP_W, _AP_S> operator[](int index) {
+    return _private_bit_ref<_AP_W, _AP_S>(*this, index);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_bit_ref<_AP_W, _AP_S> operator[](
+      const ap_private<_AP_W2, _AP_S2>& index) {
+    return _private_bit_ref<_AP_W, _AP_S>(*this, index.to_int());
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE const _private_bit_ref<_AP_W, _AP_S> operator[](
+      const ap_private<_AP_W2, _AP_S2>& index) const {
+    return _private_bit_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>&>(*this), index.to_int());
+  }
+
+  INLINE const _private_bit_ref<_AP_W, _AP_S> operator[](int index) const {
+    return _private_bit_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>&>(*this), index);
+  }
+
+  INLINE _private_bit_ref<_AP_W, _AP_S> bit(int index) {
+    return _private_bit_ref<_AP_W, _AP_S>(*this, index);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_bit_ref<_AP_W, _AP_S> bit(const ap_private<_AP_W2, _AP_S2>& index) {
+    return _private_bit_ref<_AP_W, _AP_S>(*this, index.to_int());
+  }
+
+  INLINE const _private_bit_ref<_AP_W, _AP_S> bit(int index) const {
+    return _private_bit_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>&>(*this), index);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE const _private_bit_ref<_AP_W, _AP_S> bit(
+      const ap_private<_AP_W2, _AP_S2>& index) const {
+    return _private_bit_ref<_AP_W, _AP_S>(
+        const_cast<ap_private<_AP_W, _AP_S>&>(*this), index.to_int());
+  }
+
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                       ap_private<_AP_W2, _AP_S2> >
+//  concat(ap_private<_AP_W2, _AP_S2>& a2) {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                       ap_private<_AP_W2, _AP_S2> >
+//  concat(const ap_private<_AP_W2, _AP_S2>& a2) const {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<ap_private<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private, _AP_W2, ap_private<_AP_W2, _AP_S2> >
+//  operator,(ap_private<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private, _AP_W2, ap_private<_AP_W2, _AP_S2> >
+//  operator,(ap_private<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this), a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private, _AP_W2, ap_private<_AP_W2, _AP_S2> >
+//  operator,(const ap_private<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(
+//        *this, const_cast<ap_private<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private, _AP_W2, ap_private<_AP_W2, _AP_S2> >
+//  operator,(const ap_private<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<ap_private<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                       _private_range_ref<_AP_W2, _AP_S2> >
+//  operator,(const _private_range_ref<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                         _private_range_ref<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<_private_range_ref<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                       _private_range_ref<_AP_W2, _AP_S2> >
+//  operator,(_private_range_ref<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2,
+//                         _private_range_ref<_AP_W2, _AP_S2> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, 1,
+//                       _private_bit_ref<_AP_W2, _AP_S2> >
+//  operator,(const _private_bit_ref<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, 1,
+//                         _private_bit_ref<_AP_W2, _AP_S2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<_private_bit_ref<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, 1,
+//                       _private_bit_ref<_AP_W2, _AP_S2> >
+//  operator,(_private_bit_ref<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, 1,
+//                         _private_bit_ref<_AP_W2, _AP_S2> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2 + _AP_W3,
+//                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+//  operator,(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) const {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2 + _AP_W3,
+//                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>&>(a2));
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2 + _AP_W3,
+//                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+//  operator,(ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) {
+//    return ap_concat_ref<_AP_W, ap_private<_AP_W, _AP_S>, _AP_W2 + _AP_W3,
+//                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(*this,
+//                                                                         a2);
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_concat_ref<
+//      _AP_W, ap_private, _AP_W2,
+//      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//  operator,(const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>
+//                &a2) const {
+//    return ap_concat_ref<
+//        _AP_W, ap_private, _AP_W2,
+//        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<
+//            af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_concat_ref<
+//      _AP_W, ap_private, _AP_W2,
+//      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//  operator,(af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &a2) {
+//    return ap_concat_ref<
+//        _AP_W, ap_private, _AP_W2,
+//        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(*this,
+//                                                                       a2);
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE
+//      ap_concat_ref<_AP_W, ap_private, 1,
+//                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//      operator,(const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>
+//                    &a2) const {
+//    return ap_concat_ref<
+//        _AP_W, ap_private, 1,
+//        af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+//        const_cast<ap_private<_AP_W, _AP_S>&>(*this),
+//        const_cast<af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(
+//            a2));
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE
+//      ap_concat_ref<_AP_W, ap_private, 1,
+//                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//      operator,(
+//          af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &a2) {
+//    return ap_concat_ref<
+//        _AP_W, ap_private, 1,
+//        af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(*this, a2);
+//  }
+
+  INLINE ap_private<_AP_W, false> get() const {
+    ap_private<_AP_W, false> ret(*this);
+    return ret;
+  }
+
+  template <int _AP_W3>
+  INLINE void set(const ap_private<_AP_W3, false>& val) {
+    operator=(ap_private<_AP_W3, _AP_S>(val));
+  }
+
+  ///
+  /// @name Value Tests
+  ///
+  /// This tests the high bit of this ap_private to determine if it is set.
+  /// @returns true if this ap_private is negative, false otherwise
+  /// @brief Determine sign of this ap_private.
+  INLINE bool isNegative() const {
+    // just for get rid of warnings
+    enum { shift = (_AP_W - APINT_BITS_PER_WORD * (_AP_N - 1) - 1) };
+    static const uint64_t mask = 1ULL << (shift);
+    return _AP_S && (pVal[_AP_N - 1] & mask);
+  }
+
+  /// This tests the high bit of the ap_private to determine if it is unset.
+  /// @brief Determine if this ap_private Value is positive (not negative).
+  INLINE bool isPositive() const { return !isNegative(); }
+
+  /// This tests if the value of this ap_private is strictly positive (> 0).
+  /// @returns true if this ap_private is Positive and not zero.
+  /// @brief Determine if this ap_private Value is strictly positive.
+  INLINE bool isStrictlyPositive() const {
+    return isPositive() && (*this) != 0;
+  }
+
+  /// This checks to see if the value has all bits of the ap_private are set or
+  /// not.
+  /// @brief Determine if all bits are set
+  INLINE bool isAllOnesValue() const { return countPopulation() == _AP_W; }
+
+  /// This checks to see if the value of this ap_private is the maximum unsigned
+  /// value for the ap_private's bit width.
+  /// @brief Determine if this is the largest unsigned value.
+  INLINE bool isMaxValue() const { return countPopulation() == _AP_W; }
+
+  /// This checks to see if the value of this ap_private is the maximum signed
+  /// value for the ap_private's bit width.
+  /// @brief Determine if this is the largest signed value.
+  INLINE bool isMaxSignedValue() const {
+    return !isNegative() && countPopulation() == _AP_W - 1;
+  }
+
+  /// This checks to see if the value of this ap_private is the minimum unsigned
+  /// value for the ap_private's bit width.
+  /// @brief Determine if this is the smallest unsigned value.
+  INLINE bool isMinValue() const { return countPopulation() == 0; }
+
+  /// This checks to see if the value of this ap_private is the minimum signed
+  /// value for the ap_private's bit width.
+  /// @brief Determine if this is the smallest signed value.
+  INLINE bool isMinSignedValue() const {
+    return isNegative() && countPopulation() == 1;
+  }
+
+  /// This function returns a pointer to the internal storage of the ap_private.
+  /// This is useful for writing out the ap_private in binary form without any
+  /// conversions.
+  INLINE const uint64_t* getRawData() const { return &pVal[0]; }
+
+  // Square Root - this method computes and returns the square root of "this".
+  // Three mechanisms are used for computation. For small values (<= 5 bits),
+  // a table lookup is done. This gets some performance for common cases. For
+  // values using less than 52 bits, the value is converted to double and then
+  // the libc sqrt function is called. The result is rounded and then converted
+  // back to a uint64_t which is then used to construct the result. Finally,
+  // the Babylonian method for computing square roots is used.
+  INLINE ap_private sqrt() const {
+    // Determine the magnitude of the value.
+    uint32_t magnitude = getActiveBits();
+
+    // Use a fast table for some small values. This also gets rid of some
+    // rounding errors in libc sqrt for small values.
+    if (magnitude <= 5) {
+      static const uint8_t results[32] = {
+          /*     0 */ 0,
+          /*  1- 2 */ 1, 1,
+          /*  3- 6 */ 2, 2, 2, 2,
+          /*  7-12 */ 3, 3, 3, 3, 3, 3,
+          /* 13-20 */ 4, 4, 4, 4, 4, 4, 4, 4,
+          /* 21-30 */ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+          /*    31 */ 6};
+      return ap_private<_AP_W, _AP_S>(/*BitWidth,*/ results[get_VAL()]);
+    }
+
+    // If the magnitude of the value fits in less than 52 bits (the precision of
+    // an IEEE double precision floating point value), then we can use the
+    // libc sqrt function which will probably use a hardware sqrt computation.
+    // This should be faster than the algorithm below.
+    if (magnitude < 52) {
+#ifdef _MSC_VER
+      // Amazingly, VC++ doesn't have round().
+      return ap_private<_AP_W, _AP_S>(/*BitWidth,*/
+                                      uint64_t(::sqrt(double(get_VAL()))) +
+                                      0.5);
+#else
+      return ap_private<_AP_W, _AP_S>(/*BitWidth,*/
+                                      uint64_t(
+                                          ::round(::sqrt(double(get_VAL())))));
+#endif
+    }
+
+    // Okay, all the short cuts are exhausted. We must compute it. The following
+    // is a classical Babylonian method for computing the square root. This code
+    // was adapted to APINt from a wikipedia article on such computations.
+    // See http://www.wikipedia.org/ and go to the page named
+    // Calculate_an_integer_square_root.
+    uint32_t nbits = BitWidth, i = 4;
+    ap_private<_AP_W, _AP_S> testy(16);
+    ap_private<_AP_W, _AP_S> x_old(/*BitWidth,*/ 1);
+    ap_private<_AP_W, _AP_S> x_new(0);
+    ap_private<_AP_W, _AP_S> two(/*BitWidth,*/ 2);
+
+    // Select a good starting value using binary logarithms.
+    for (;; i += 2, testy = testy.shl(2))
+      if (i >= nbits || this->ule(testy)) {
+        x_old = x_old.shl(i / 2);
+        break;
+      }
+
+    // Use the Babylonian method to arrive at the integer square root:
+    for (;;) {
+      x_new = (this->udiv(x_old) + x_old).udiv(two);
+      if (x_old.ule(x_new)) break;
+      x_old = x_new;
+    }
+
+    // Make sure we return the closest approximation
+    // NOTE: The rounding calculation below is correct. It will produce an
+    // off-by-one discrepancy with results from pari/gp. That discrepancy has
+    // been
+    // determined to be a rounding issue with pari/gp as it begins to use a
+    // floating point representation after 192 bits. There are no discrepancies
+    // between this algorithm and pari/gp for bit widths < 192 bits.
+    ap_private<_AP_W, _AP_S> square(x_old * x_old);
+    ap_private<_AP_W, _AP_S> nextSquare((x_old + 1) * (x_old + 1));
+    if (this->ult(square))
+      return x_old;
+    else if (this->ule(nextSquare)) {
+      ap_private<_AP_W, _AP_S> midpoint((nextSquare - square).udiv(two));
+      ap_private<_AP_W, _AP_S> offset(*this - square);
+      if (offset.ult(midpoint))
+        return x_old;
+      else
+        return x_old + 1;
+    } else
+      assert(0 && "Error in ap_private<_AP_W, _AP_S>::sqrt computation");
+    return x_old + 1;
+  }
+
+  ///
+  /// @Assignment Operators
+  ///
+  /// @returns *this after assignment of RHS.
+  /// @brief Copy assignment operator.
+  INLINE ap_private& operator=(const ap_private& RHS) {
+    if (this != &RHS) memcpy(pVal, RHS.get_pVal(), _AP_N * APINT_WORD_SIZE);
+    clearUnusedBits();
+    return *this;
+  }
+  INLINE ap_private& operator=(const volatile ap_private& RHS) {
+    if (this != &RHS)
+      for (int i = 0; i < _AP_N; ++i) pVal[i] = RHS.get_pVal(i);
+    clearUnusedBits();
+    return *this;
+  }
+  INLINE void operator=(const ap_private& RHS) volatile {
+    if (this != &RHS)
+      for (int i = 0; i < _AP_N; ++i) pVal[i] = RHS.get_pVal(i);
+    clearUnusedBits();
+  }
+  INLINE void operator=(const volatile ap_private& RHS) volatile {
+    if (this != &RHS)
+      for (int i = 0; i < _AP_N; ++i) pVal[i] = RHS.get_pVal(i);
+    clearUnusedBits();
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator=(const ap_private<_AP_W1, _AP_S1>& RHS) {
+    if (_AP_S1)
+      cpSextOrTrunc(RHS);
+    else
+      cpZextOrTrunc(RHS);
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE ap_private& operator=(const volatile ap_private<_AP_W1, _AP_S1>& RHS) {
+    if (_AP_S1)
+      cpSextOrTrunc(RHS);
+    else
+      cpZextOrTrunc(RHS);
+    clearUnusedBits();
+    return *this;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE ap_private& operator=(const _private_range_ref<_AP_W2, _AP_S2>& op2) {
+    *this = ap_private<_AP_W2, false>(op2);
+    return *this;
+  }
+
+#if 0
+    template<int _AP_W1, bool _AP_S1>
+    INLINE ap_private& operator=(const ap_private<_AP_W1, _AP_S1, true>& RHS) {
+        static const uint64_t that_sign_ext_mask = (_AP_W1==APINT_BITS_PER_WORD)?0:~0ULL>>(_AP_W1%APINT_BITS_PER_WORD)<<(_AP_W1%APINT_BITS_PER_WORD);
+        if (RHS.isNegative()) {
+            pVal[0] = RHS.get_VAL() | that_sign_ext_mask;
+            memset(pVal+1,~0, APINT_WORD_SIZE*(_AP_N-1));
+        } else {
+            pVal[0] = RHS.get_VAL();
+            memset(pVal+1, 0, APINT_WORD_SIZE*(_AP_N-1));
+        }
+        clearUnusedBits();
+        return *this;
+    }
+
+    template<int _AP_W1, bool _AP_S1>
+    INLINE ap_private& operator=(const volatile ap_private<_AP_W1, _AP_S1, true>& RHS) {
+        static const uint64_t that_sign_ext_mask = (_AP_W1==APINT_BITS_PER_WORD)?0:~0ULL>>(_AP_W1%APINT_BITS_PER_WORD)<<(_AP_W1%APINT_BITS_PER_WORD);
+        if (RHS.isNegative()) {
+            pVal[0] = RHS.get_VAL() | that_sign_ext_mask;
+            memset(pVal+1,~0, APINT_WORD_SIZE*(_AP_N-1));
+        } else {
+            pVal[0] = RHS.get_VAL();
+            memset(pVal+1, 0, APINT_WORD_SIZE*(_AP_N-1));
+        }
+        clearUnusedBits();
+        return *this;
+    }
+#endif
+
+/// from all c types.
+#define ASSIGN_OP_FROM_INT(C_TYPE, _AP_W2, _AP_S2) \
+  INLINE ap_private& operator=(const C_TYPE rhs) { \
+    ap_private<(_AP_W2), (_AP_S2)> tmp = rhs;      \
+    operator=(tmp);                                \
+    return *this;                                  \
+  }
+
+  ASSIGN_OP_FROM_INT(bool, 1, false)
+  ASSIGN_OP_FROM_INT(char, 8, CHAR_IS_SIGNED)
+  ASSIGN_OP_FROM_INT(signed char, 8, true)
+  ASSIGN_OP_FROM_INT(unsigned char, 8, false)
+  ASSIGN_OP_FROM_INT(short, sizeof(short) * 8, true)
+  ASSIGN_OP_FROM_INT(unsigned short, sizeof(unsigned short) * 8, false)
+  ASSIGN_OP_FROM_INT(int, sizeof(int) * 8, true)
+  ASSIGN_OP_FROM_INT(unsigned int, sizeof(unsigned int) * 8, false)
+  ASSIGN_OP_FROM_INT(long, sizeof(long) * 8, true)
+  ASSIGN_OP_FROM_INT(unsigned long, sizeof(unsigned long) * 8, false)
+  ASSIGN_OP_FROM_INT(ap_slong, sizeof(ap_slong) * 8, true)
+  ASSIGN_OP_FROM_INT(ap_ulong, sizeof(ap_ulong) * 8, false)
+#undef ASSIGN_OP_FROM_INT
+
+  /// from c string.
+  // XXX this is a must, to prevent pointer being converted to bool.
+  INLINE ap_private& operator=(const char* s) {
+    ap_private tmp(s); // XXX direct initialization, as ctor is explicit.
+    operator=(tmp);
+    return *this;
+  }
+
+  ///
+  /// @name Unary Operators
+  ///
+  /// @returns a new ap_private value representing *this incremented by one
+  /// @brief Postfix increment operator.
+  INLINE const ap_private operator++(int) {
+    ap_private API(*this);
+    ++(*this);
+    return API;
+  }
+
+  /// @returns *this incremented by one
+  /// @brief Prefix increment operator.
+  INLINE ap_private& operator++() {
+    ap_private_ops::add_1(pVal, pVal, _AP_N, 1);
+    clearUnusedBits();
+    return *this;
+  }
+
+  /// @returns a new ap_private representing *this decremented by one.
+  /// @brief Postfix decrement operator.
+  INLINE const ap_private operator--(int) {
+    ap_private API(*this);
+    --(*this);
+    return API;
+  }
+
+  /// @returns *this decremented by one.
+  /// @brief Prefix decrement operator.
+  INLINE ap_private& operator--() {
+    ap_private_ops::sub_1(pVal, _AP_N, 1);
+    clearUnusedBits();
+    return *this;
+  }
+
+  /// Performs a bitwise complement operation on this ap_private.
+  /// @returns an ap_private that is the bitwise complement of *this
+  /// @brief Unary bitwise complement operator.
+  INLINE ap_private<_AP_W + !_AP_S, true> operator~() const {
+    ap_private<_AP_W + !_AP_S, true> Result(*this);
+    Result.flip();
+    return Result;
+  }
+
+  /// Negates *this using two's complement logic.
+  /// @returns An ap_private value representing the negation of *this.
+  /// @brief Unary negation operator
+  INLINE typename RType<1, false>::minus operator-() const {
+    return ap_private<1, false>(0) - (*this);
+  }
+
+  /// Performs logical negation operation on this ap_private.
+  /// @returns true if *this is zero, false otherwise.
+  /// @brief Logical negation operator.
+  INLINE bool operator!() const {
+    for (int i = 0; i < _AP_N; ++i)
+      if (pVal[i]) return false;
+    return true;
+  }
+
+  template <bool _AP_S1>
+  INLINE ap_private<_AP_W, _AP_S || _AP_S1> And(
+      const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return this->operator&(RHS);
+  }
+  template <bool _AP_S1>
+  INLINE ap_private Or(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return this->operator|(RHS);
+  }
+  template <bool _AP_S1>
+  INLINE ap_private Xor(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return this->operator^(RHS);
+  }
+
+  INLINE ap_private Mul(const ap_private& RHS) const {
+    ap_private Result(*this);
+    Result *= RHS;
+    return Result;
+  }
+
+  INLINE ap_private Add(const ap_private& RHS) const {
+    ap_private Result(0);
+    ap_private_ops::add(Result.get_pVal(), pVal, RHS.get_pVal(), _AP_N, _AP_N,
+                        _AP_N, _AP_S, _AP_S);
+    Result.clearUnusedBits();
+    return Result;
+  }
+
+  INLINE ap_private Sub(const ap_private& RHS) const {
+    ap_private Result(0);
+    ap_private_ops::sub(Result.get_pVal(), pVal, RHS.get_pVal(), _AP_N, _AP_N,
+                        _AP_N, _AP_S, _AP_S);
+    Result.clearUnusedBits();
+    return Result;
+  }
+
+  /// Arithmetic right-shift this ap_private by shiftAmt.
+  /// @brief Arithmetic right-shift function.
+  INLINE ap_private ashr(uint32_t shiftAmt) const {
+    assert(shiftAmt <= BitWidth && "Invalid shift amount, too big");
+    // Handle a degenerate case
+    if (shiftAmt == 0) return ap_private(*this);
+
+    // If all the bits were shifted out, the result is, technically, undefined.
+    // We return -1 if it was negative, 0 otherwise. We check this early to
+    // avoid
+    // issues in the algorithm below.
+    if (shiftAmt == BitWidth) {
+      if (isNegative())
+        return ap_private(-1);
+      else
+        return ap_private(0);
+    }
+
+    // Create some space for the result.
+    ap_private Retval(0);
+    uint64_t* val = Retval.get_pVal();
+
+    // Compute some values needed by the following shift algorithms
+    uint32_t wordShift =
+        shiftAmt % APINT_BITS_PER_WORD;               // bits to shift per word
+    uint32_t offset = shiftAmt / APINT_BITS_PER_WORD; // word offset for shift
+    uint32_t breakWord = _AP_N - 1 - offset;          // last word affected
+    uint32_t bitsInWord = whichBit(BitWidth); // how many bits in last word?
+    if (bitsInWord == 0) bitsInWord = APINT_BITS_PER_WORD;
+
+    // If we are shifting whole words, just move whole words
+    if (wordShift == 0) {
+      // Move the words containing significant bits
+      for (uint32_t i = 0; i <= breakWord; ++i)
+        val[i] = pVal[i + offset]; // move whole word
+
+      // Adjust the top significant word for sign bit fill, if negative
+      if (isNegative())
+        if (bitsInWord < APINT_BITS_PER_WORD)
+          val[breakWord] |= ~0ULL << (bitsInWord); // set high bits
+    } else {
+      // Shift the low order words
+      for (uint32_t i = 0; i < breakWord; ++i) {
+        // This combines the shifted corresponding word with the low bits from
+        // the next word (shifted into this word's high bits).
+        val[i] = ((pVal[i + offset]) >> (wordShift));
+        val[i] |= ((pVal[i + offset + 1]) << (APINT_BITS_PER_WORD - wordShift));
+      }
+
+      // Shift the break word. In this case there are no bits from the next word
+      // to include in this word.
+      val[breakWord] = (pVal[breakWord + offset]) >> (wordShift);
+
+      // Deal with sign extenstion in the break word, and possibly the word
+      // before
+      // it.
+      if (isNegative()) {
+        if (wordShift > bitsInWord) {
+          if (breakWord > 0)
+            val[breakWord - 1] |=
+                ~0ULL << (APINT_BITS_PER_WORD - (wordShift - bitsInWord));
+          val[breakWord] |= ~0ULL;
+        } else
+          val[breakWord] |= (~0ULL << (bitsInWord - wordShift));
+      }
+    }
+
+    // Remaining words are 0 or -1, just assign them.
+    uint64_t fillValue = (isNegative() ? ~0ULL : 0);
+    for (int i = breakWord + 1; i < _AP_N; ++i) val[i] = fillValue;
+    Retval.clearUnusedBits();
+    return Retval;
+  }
+
+  /// Logical right-shift this ap_private by shiftAmt.
+  /// @brief Logical right-shift function.
+  INLINE ap_private lshr(uint32_t shiftAmt) const {
+    // If all the bits were shifted out, the result is 0. This avoids issues
+    // with shifting by the size of the integer type, which produces undefined
+    // results. We define these "undefined results" to always be 0.
+    if (shiftAmt == BitWidth) return ap_private(0);
+
+    // If none of the bits are shifted out, the result is *this. This avoids
+    // issues with shifting byt he size of the integer type, which produces
+    // undefined results in the code below. This is also an optimization.
+    if (shiftAmt == 0) return ap_private(*this);
+
+    // Create some space for the result.
+    ap_private Retval(0);
+    uint64_t* val = Retval.get_pVal();
+
+    // If we are shifting less than a word, compute the shift with a simple
+    // carry
+    if (shiftAmt < APINT_BITS_PER_WORD) {
+      uint64_t carry = 0;
+      for (int i = _AP_N - 1; i >= 0; --i) {
+        val[i] = ((pVal[i]) >> (shiftAmt)) | carry;
+        carry = (pVal[i]) << (APINT_BITS_PER_WORD - shiftAmt);
+      }
+      Retval.clearUnusedBits();
+      return Retval;
+    }
+
+    // Compute some values needed by the remaining shift algorithms
+    uint32_t wordShift = shiftAmt % APINT_BITS_PER_WORD;
+    uint32_t offset = shiftAmt / APINT_BITS_PER_WORD;
+
+    // If we are shifting whole words, just move whole words
+    if (wordShift == 0) {
+      for (uint32_t i = 0; i < _AP_N - offset; ++i) val[i] = pVal[i + offset];
+      for (uint32_t i = _AP_N - offset; i < _AP_N; i++) val[i] = 0;
+      Retval.clearUnusedBits();
+      return Retval;
+    }
+
+    // Shift the low order words
+    uint32_t breakWord = _AP_N - offset - 1;
+    for (uint32_t i = 0; i < breakWord; ++i)
+      val[i] = ((pVal[i + offset]) >> (wordShift)) |
+               ((pVal[i + offset + 1]) << (APINT_BITS_PER_WORD - wordShift));
+    // Shift the break word.
+    val[breakWord] = (pVal[breakWord + offset]) >> (wordShift);
+
+    // Remaining words are 0
+    for (int i = breakWord + 1; i < _AP_N; ++i) val[i] = 0;
+    Retval.clearUnusedBits();
+    return Retval;
+  }
+
+  /// Left-shift this ap_private by shiftAmt.
+  /// @brief Left-shift function.
+  INLINE ap_private shl(uint32_t shiftAmt) const {
+    assert(shiftAmt <= BitWidth && "Invalid shift amount, too big");
+    // If all the bits were shifted out, the result is 0. This avoids issues
+    // with shifting by the size of the integer type, which produces undefined
+    // results. We define these "undefined results" to always be 0.
+    if (shiftAmt == BitWidth) return ap_private(0);
+
+    // If none of the bits are shifted out, the result is *this. This avoids a
+    // lshr by the words size in the loop below which can produce incorrect
+    // results. It also avoids the expensive computation below for a common
+    // case.
+    if (shiftAmt == 0) return ap_private(*this);
+
+    // Create some space for the result.
+    ap_private Retval(0);
+    uint64_t* val = Retval.get_pVal();
+    // If we are shifting less than a word, do it the easy way
+    if (shiftAmt < APINT_BITS_PER_WORD) {
+      uint64_t carry = 0;
+      for (int i = 0; i < _AP_N; i++) {
+        val[i] = ((pVal[i]) << (shiftAmt)) | carry;
+        carry = (pVal[i]) >> (APINT_BITS_PER_WORD - shiftAmt);
+      }
+      Retval.clearUnusedBits();
+      return Retval;
+    }
+
+    // Compute some values needed by the remaining shift algorithms
+    uint32_t wordShift = shiftAmt % APINT_BITS_PER_WORD;
+    uint32_t offset = shiftAmt / APINT_BITS_PER_WORD;
+
+    // If we are shifting whole words, just move whole words
+    if (wordShift == 0) {
+      for (uint32_t i = 0; i < offset; i++) val[i] = 0;
+      for (int i = offset; i < _AP_N; i++) val[i] = pVal[i - offset];
+      Retval.clearUnusedBits();
+      return Retval;
+    }
+
+    // Copy whole words from this to Result.
+    uint32_t i = _AP_N - 1;
+    for (; i > offset; --i)
+      val[i] = (pVal[i - offset]) << (wordShift) |
+               (pVal[i - offset - 1]) >> (APINT_BITS_PER_WORD - wordShift);
+    val[offset] = (pVal[0]) << (wordShift);
+    for (i = 0; i < offset; ++i) val[i] = 0;
+    Retval.clearUnusedBits();
+    return Retval;
+  }
+
+  INLINE ap_private rotl(uint32_t rotateAmt) const {
+    if (rotateAmt == 0) return ap_private(*this);
+    // Don't get too fancy, just use existing shift/or facilities
+    ap_private hi(*this);
+    ap_private lo(*this);
+    hi.shl(rotateAmt);
+    lo.lshr(BitWidth - rotateAmt);
+    return hi | lo;
+  }
+
+  INLINE ap_private rotr(uint32_t rotateAmt) const {
+    if (rotateAmt == 0) return ap_private(*this);
+    // Don't get too fancy, just use existing shift/or facilities
+    ap_private hi(*this);
+    ap_private lo(*this);
+    lo.lshr(rotateAmt);
+    hi.shl(BitWidth - rotateAmt);
+    return hi | lo;
+  }
+
+  /// Perform an unsigned divide operation on this ap_private by RHS. Both this
+  /// and
+  /// RHS are treated as unsigned quantities for purposes of this division.
+  /// @returns a new ap_private value containing the division result
+  /// @brief Unsigned division operation.
+  INLINE ap_private udiv(const ap_private& RHS) const {
+    // Get some facts about the LHS and RHS number of bits and words
+    uint32_t rhsBits = RHS.getActiveBits();
+    uint32_t rhsWords = !rhsBits ? 0 : (whichWord(rhsBits - 1) + 1);
+    assert(rhsWords && "Divided by zero???");
+    uint32_t lhsBits = this->getActiveBits();
+    uint32_t lhsWords = !lhsBits ? 0 : (whichWord(lhsBits - 1) + 1);
+
+    // Deal with some degenerate cases
+    if (!lhsWords)
+      // 0 / X ===> 0
+      return ap_private(0);
+    else if (lhsWords < rhsWords || this->ult(RHS)) {
+      // X / Y ===> 0, iff X < Y
+      return ap_private(0);
+    } else if (*this == RHS) {
+      // X / X ===> 1
+      return ap_private(1);
+    } else if (lhsWords == 1 && rhsWords == 1) {
+      // All high words are zero, just use native divide
+      return ap_private(this->pVal[0] / RHS.get_pVal(0));
+    }
+
+    // We have to compute it the hard way. Invoke the Knuth divide algorithm.
+    ap_private Quotient(0); // to hold result.
+    ap_private_ops::divide(*this, lhsWords, RHS, rhsWords, &Quotient,
+                           (ap_private*)0);
+    return Quotient;
+  }
+
+  /// Signed divide this ap_private by ap_private RHS.
+  /// @brief Signed division function for ap_private.
+  INLINE ap_private sdiv(const ap_private& RHS) const {
+    if (isNegative())
+      if (RHS.isNegative())
+        return (-(*this)).udiv(-RHS);
+      else
+        return -((-(*this)).udiv(RHS));
+    else if (RHS.isNegative())
+      return -(this->udiv((ap_private)(-RHS)));
+    return this->udiv(RHS);
+  }
+
+  /// Perform an unsigned remainder operation on this ap_private with RHS being
+  /// the
+  /// divisor. Both this and RHS are treated as unsigned quantities for purposes
+  /// of this operation. Note that this is a true remainder operation and not
+  /// a modulo operation because the sign follows the sign of the dividend
+  /// which is *this.
+  /// @returns a new ap_private value containing the remainder result
+  /// @brief Unsigned remainder operation.
+  INLINE ap_private urem(const ap_private& RHS) const {
+    // Get some facts about the LHS
+    uint32_t lhsBits = getActiveBits();
+    uint32_t lhsWords = !lhsBits ? 0 : (whichWord(lhsBits - 1) + 1);
+
+    // Get some facts about the RHS
+    uint32_t rhsBits = RHS.getActiveBits();
+    uint32_t rhsWords = !rhsBits ? 0 : (whichWord(rhsBits - 1) + 1);
+    assert(rhsWords && "Performing remainder operation by zero ???");
+
+    // Check the degenerate cases
+    if (lhsWords == 0) {
+      // 0 % Y ===> 0
+      return ap_private(0);
+    } else if (lhsWords < rhsWords || this->ult(RHS)) {
+      // X % Y ===> X, iff X < Y
+      return *this;
+    } else if (*this == RHS) {
+      // X % X == 0;
+      return ap_private(0);
+    } else if (lhsWords == 1) {
+      // All high words are zero, just use native remainder
+      return ap_private(pVal[0] % RHS.get_pVal(0));
+    }
+
+    // We have to compute it the hard way. Invoke the Knuth divide algorithm.
+    ap_private Remainder(0);
+    ap_private_ops::divide(*this, lhsWords, RHS, rhsWords, (ap_private*)(0),
+                           &Remainder);
+    return Remainder;
+  }
+
+  INLINE ap_private urem(uint64_t RHS) const {
+    // Get some facts about the LHS
+    uint32_t lhsBits = getActiveBits();
+    uint32_t lhsWords = !lhsBits ? 0 : (whichWord(lhsBits - 1) + 1);
+    // Get some facts about the RHS
+    uint32_t rhsWords = 1; //! rhsBits ? 0 : (ap_private<_AP_W,
+                           //! _AP_S>::whichWord(rhsBits - 1) + 1);
+    assert(rhsWords && "Performing remainder operation by zero ???");
+    // Check the degenerate cases
+    if (lhsWords == 0) {
+      // 0 % Y ===> 0
+      return ap_private(0);
+    } else if (lhsWords < rhsWords || this->ult(RHS)) {
+      // X % Y ===> X, iff X < Y
+      return *this;
+    } else if (*this == RHS) {
+      // X % X == 0;
+      return ap_private(0);
+    } else if (lhsWords == 1) {
+      // All high words are zero, just use native remainder
+      return ap_private(pVal[0] % RHS);
+    }
+
+    // We have to compute it the hard way. Invoke the Knuth divide algorithm.
+    ap_private Remainder(0);
+    divide(*this, lhsWords, RHS, (ap_private*)(0), &Remainder);
+    return Remainder;
+  }
+
+  /// Signed remainder operation on ap_private.
+  /// @brief Function for signed remainder operation.
+  INLINE ap_private srem(const ap_private& RHS) const {
+    if (isNegative()) {
+      ap_private lhs = -(*this);
+      if (RHS.isNegative()) {
+        ap_private rhs = -RHS;
+        return -(lhs.urem(rhs));
+      } else
+        return -(lhs.urem(RHS));
+    } else if (RHS.isNegative()) {
+      ap_private rhs = -RHS;
+      return this->urem(rhs);
+    }
+    return this->urem(RHS);
+  }
+
+  /// Signed remainder operation on ap_private.
+  /// @brief Function for signed remainder operation.
+  INLINE ap_private srem(int64_t RHS) const {
+    if (isNegative())
+      if (RHS < 0)
+        return -((-(*this)).urem(-RHS));
+      else
+        return -((-(*this)).urem(RHS));
+    else if (RHS < 0)
+      return this->urem(-RHS);
+    return this->urem(RHS);
+  }
+
+  /// Compares this ap_private with RHS for the validity of the equality
+  /// relationship.
+  /// @returns true if *this == Val
+  /// @brief Equality comparison.
+  template <bool _AP_S1>
+  INLINE bool eq(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return (*this) == RHS;
+  }
+
+  /// Compares this ap_private with RHS for the validity of the inequality
+  /// relationship.
+  /// @returns true if *this != Val
+  /// @brief Inequality comparison
+  template <bool _AP_S1>
+  INLINE bool ne(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return !((*this) == RHS);
+  }
+
+  /// Regards both *this and RHS as unsigned quantities and compares them for
+  /// the validity of the less-than relationship.
+  /// @returns true if *this < RHS when both are considered unsigned.
+  /// @brief Unsigned less than comparison
+  template <bool _AP_S1>
+  INLINE bool ult(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    // Get active bit length of both operands
+    uint32_t n1 = getActiveBits();
+    uint32_t n2 = RHS.getActiveBits();
+
+    // If magnitude of LHS is less than RHS, return true.
+    if (n1 < n2) return true;
+
+    // If magnitude of RHS is greather than LHS, return false.
+    if (n2 < n1) return false;
+
+    // If they bot fit in a word, just compare the low order word
+    if (n1 <= APINT_BITS_PER_WORD && n2 <= APINT_BITS_PER_WORD)
+      return pVal[0] < RHS.get_pVal(0);
+
+    // Otherwise, compare all words
+    uint32_t topWord = whichWord(AESL_std::max(n1, n2) - 1);
+    for (int i = topWord; i >= 0; --i) {
+      if (pVal[i] > RHS.get_pVal(i)) return false;
+      if (pVal[i] < RHS.get_pVal(i)) return true;
+    }
+    return false;
+  }
+
+  INLINE bool ult(uint64_t RHS) const {
+    // Get active bit length of both operands
+    uint32_t n1 = getActiveBits();
+    uint32_t n2 =
+        64 - ap_private_ops::CountLeadingZeros_64(RHS); // RHS.getActiveBits();
+
+    // If magnitude of LHS is less than RHS, return true.
+    if (n1 < n2) return true;
+
+    // If magnitude of RHS is greather than LHS, return false.
+    if (n2 < n1) return false;
+
+    // If they bot fit in a word, just compare the low order word
+    if (n1 <= APINT_BITS_PER_WORD && n2 <= APINT_BITS_PER_WORD)
+      return pVal[0] < RHS;
+    assert(0);
+  }
+
+  template <bool _AP_S1>
+  INLINE bool slt(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    ap_private lhs(*this);
+    ap_private<_AP_W, _AP_S1> rhs(RHS);
+    bool lhsNeg = isNegative();
+    bool rhsNeg = rhs.isNegative();
+    if (lhsNeg) {
+      // Sign bit is set so perform two's complement to make it positive
+      lhs.flip();
+      lhs++;
+    }
+    if (rhsNeg) {
+      // Sign bit is set so perform two's complement to make it positive
+      rhs.flip();
+      rhs++;
+    }
+
+    // Now we have unsigned values to compare so do the comparison if necessary
+    // based on the negativeness of the values.
+    if (lhsNeg)
+      if (rhsNeg)
+        return lhs.ugt(rhs);
+      else
+        return true;
+    else if (rhsNeg)
+      return false;
+    else
+      return lhs.ult(rhs);
+  }
+
+  /// Regards both *this and RHS as unsigned quantities and compares them for
+  /// validity of the less-or-equal relationship.
+  /// @returns true if *this <= RHS when both are considered unsigned.
+  /// @brief Unsigned less or equal comparison
+  template <bool _AP_S1>
+  INLINE bool ule(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return ult(RHS) || eq(RHS);
+  }
+
+  /// Regards both *this and RHS as signed quantities and compares them for
+  /// validity of the less-or-equal relationship.
+  /// @returns true if *this <= RHS when both are considered signed.
+  /// @brief Signed less or equal comparison
+  template <bool _AP_S1>
+  INLINE bool sle(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return slt(RHS) || eq(RHS);
+  }
+
+  /// Regards both *this and RHS as unsigned quantities and compares them for
+  /// the validity of the greater-than relationship.
+  /// @returns true if *this > RHS when both are considered unsigned.
+  /// @brief Unsigned greather than comparison
+  template <bool _AP_S1>
+  INLINE bool ugt(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return !ult(RHS) && !eq(RHS);
+  }
+
+  /// Regards both *this and RHS as signed quantities and compares them for
+  /// the validity of the greater-than relationship.
+  /// @returns true if *this > RHS when both are considered signed.
+  /// @brief Signed greather than comparison
+  template <bool _AP_S1>
+  INLINE bool sgt(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return !slt(RHS) && !eq(RHS);
+  }
+
+  /// Regards both *this and RHS as unsigned quantities and compares them for
+  /// validity of the greater-or-equal relationship.
+  /// @returns true if *this >= RHS when both are considered unsigned.
+  /// @brief Unsigned greater or equal comparison
+  template <bool _AP_S1>
+  INLINE bool uge(const ap_private<_AP_W, _AP_S>& RHS) const {
+    return !ult(RHS);
+  }
+
+  /// Regards both *this and RHS as signed quantities and compares them for
+  /// validity of the greater-or-equal relationship.
+  /// @returns true if *this >= RHS when both are considered signed.
+  /// @brief Signed greather or equal comparison
+  template <bool _AP_S1>
+  INLINE bool sge(const ap_private<_AP_W, _AP_S1>& RHS) const {
+    return !slt(RHS);
+  }
+
+  // Sign extend to a new width.
+  template <int _AP_W1, bool _AP_S1>
+  INLINE void cpSext(const ap_private<_AP_W1, _AP_S1>& that) {
+    assert(_AP_W1 < BitWidth && "Invalid ap_private SignExtend request");
+    assert(_AP_W1 <= MAX_INT_BITS && "Too many bits");
+    // If the sign bit isn't set, this is the same as zext.
+    if (!that.isNegative()) {
+      cpZext(that);
+      return;
+    }
+
+    // The sign bit is set. First, get some facts
+    enum { wordBits = _AP_W1 % APINT_BITS_PER_WORD };
+    const int _AP_N1 = ap_private<_AP_W1, _AP_S1>::_AP_N;
+    // Mask the high order word appropriately
+    if (_AP_N1 == _AP_N) {
+      enum { newWordBits = _AP_W % APINT_BITS_PER_WORD };
+      // The extension is contained to the wordsBefore-1th word.
+      static const uint64_t mask = wordBits ? (~0ULL << (wordBits)) : 0ULL;
+      for (int i = 0; i < _AP_N; ++i) pVal[i] = that.get_pVal(i);
+      pVal[_AP_N - 1] |= mask;
+      return;
+    }
+
+    enum { newWordBits = _AP_W % APINT_BITS_PER_WORD };
+    // The extension is contained to the wordsBefore-1th word.
+    static const uint64_t mask = wordBits ? (~0ULL << (wordBits)) : 0ULL;
+    int i;
+    for (i = 0; i < _AP_N1; ++i) pVal[i] = that.get_pVal(i);
+    pVal[i - 1] |= mask;
+    for (; i < _AP_N - 1; i++) pVal[i] = ~0ULL;
+    pVal[i] = ~0ULL;
+    clearUnusedBits();
+    return;
+  }
+
+  //  Zero extend to a new width.
+  template <int _AP_W1, bool _AP_S1>
+  INLINE void cpZext(const ap_private<_AP_W1, _AP_S1>& that) {
+    assert(_AP_W1 < BitWidth && "Invalid ap_private ZeroExtend request");
+    assert(_AP_W1 <= MAX_INT_BITS && "Too many bits");
+    const int _AP_N1 = ap_private<_AP_W1, _AP_S1>::_AP_N;
+    int i = 0;
+    for (; i < _AP_N1; ++i) pVal[i] = that.get_pVal(i);
+    for (; i < _AP_N; ++i) pVal[i] = 0;
+    clearUnusedBits();
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE void cpZextOrTrunc(const ap_private<_AP_W1, _AP_S1>& that) {
+    if (BitWidth > _AP_W1)
+      cpZext(that);
+    else {
+      for (int i = 0; i < _AP_N; ++i) pVal[i] = that.get_pVal(i);
+      clearUnusedBits();
+    }
+  }
+
+  template <int _AP_W1, bool _AP_S1>
+  INLINE void cpSextOrTrunc(const ap_private<_AP_W1, _AP_S1>& that) {
+    if (BitWidth > _AP_W1)
+      cpSext(that);
+    else {
+      for (int i = 0; i < _AP_N; ++i) pVal[i] = that.get_pVal(i);
+      clearUnusedBits();
+    }
+  }
+
+  /// @}
+  /// @name Value Characterization Functions
+  /// @{
+
+  /// @returns the total number of bits.
+  INLINE uint32_t getBitWidth() const { return BitWidth; }
+
+  /// Here one word's bitwidth equals to that of uint64_t.
+  /// @returns the number of words to hold the integer value of this ap_private.
+  /// @brief Get the number of words.
+  INLINE uint32_t getNumWords() const {
+    return (BitWidth + APINT_BITS_PER_WORD - 1) / APINT_BITS_PER_WORD;
+  }
+
+  /// This function returns the number of active bits which is defined as the
+  /// bit width minus the number of leading zeros. This is used in several
+  /// computations to see how "wide" the value is.
+  /// @brief Compute the number of active bits in the value
+  INLINE uint32_t getActiveBits() const {
+    uint32_t bits = BitWidth - countLeadingZeros();
+    return bits ? bits : 1;
+  }
+
+  /// This method attempts to return the value of this ap_private as a zero
+  /// extended
+  /// uint64_t. The bitwidth must be <= 64 or the value must fit within a
+  /// uint64_t. Otherwise an assertion will result.
+  /// @brief Get zero extended value
+  INLINE uint64_t getZExtValue() const {
+    assert(getActiveBits() <= 64 && "Too many bits for uint64_t");
+    return *pVal;
+  }
+
+  /// This method attempts to return the value of this ap_private as a sign
+  /// extended
+  /// int64_t. The bit width must be <= 64 or the value must fit within an
+  /// int64_t. Otherwise an assertion will result.
+  /// @brief Get sign extended value
+  INLINE int64_t getSExtValue() const {
+    assert(getActiveBits() <= 64 && "Too many bits for int64_t");
+    return int64_t(pVal[0]);
+  }
+
+  /// This method determines how many bits are required to hold the ap_private
+  /// equivalent of the string given by \p str of length \p slen.
+  /// @brief Get bits required for string value.
+  INLINE static uint32_t getBitsNeeded(const char* str, uint32_t slen,
+                                       uint8_t radix) {
+    assert(str != 0 && "Invalid value string");
+    assert(slen > 0 && "Invalid string length");
+
+    // Each computation below needs to know if its negative
+    uint32_t isNegative = str[0] == '-';
+    if (isNegative) {
+      slen--;
+      str++;
+    }
+    // For radixes of power-of-two values, the bits required is accurately and
+    // easily computed
+    if (radix == 2) return slen + isNegative;
+    if (radix == 8) return slen * 3 + isNegative;
+    if (radix == 16) return slen * 4 + isNegative;
+
+    // Otherwise it must be radix == 10, the hard case
+    assert(radix == 10 && "Invalid radix");
+
+    // Convert to the actual binary value.
+    // ap_private<_AP_W, _AP_S> tmp(sufficient, str, slen, radix);
+
+    // Compute how many bits are required.
+    // return isNegative + tmp.logBase2() + 1;
+    return isNegative + slen * 4;
+  }
+
+  /// countLeadingZeros - This function is an ap_private version of the
+  /// countLeadingZeros_{32,64} functions in MathExtras.h. It counts the number
+  /// of zeros from the most significant bit to the first one bit.
+  /// @returns BitWidth if the value is zero.
+  /// @returns the number of zeros from the most significant bit to the first
+  /// one bits.
+  INLINE uint32_t countLeadingZeros() const {
+    enum {
+      msw_bits = (BitWidth % APINT_BITS_PER_WORD)
+                     ? (BitWidth % APINT_BITS_PER_WORD)
+                     : APINT_BITS_PER_WORD,
+      excessBits = APINT_BITS_PER_WORD - msw_bits
+    };
+    uint32_t Count = ap_private_ops::CountLeadingZeros_64(pVal[_AP_N - 1]);
+    if (Count >= excessBits) Count -= excessBits;
+    if (!pVal[_AP_N - 1]) {
+      for (int i = _AP_N - 1; i; --i) {
+        if (!pVal[i - 1])
+          Count += APINT_BITS_PER_WORD;
+        else {
+          Count += ap_private_ops::CountLeadingZeros_64(pVal[i - 1]);
+          break;
+        }
+      }
+    }
+    return Count;
+  }
+
+  /// countLeadingOnes - This function counts the number of contiguous 1 bits
+  /// in the high order bits. The count stops when the first 0 bit is reached.
+  /// @returns 0 if the high order bit is not set
+  /// @returns the number of 1 bits from the most significant to the least
+  /// @brief Count the number of leading one bits.
+  INLINE uint32_t countLeadingOnes() const {
+    if (isSingleWord())
+      return countLeadingOnes_64(get_VAL(), APINT_BITS_PER_WORD - BitWidth);
+
+    uint32_t highWordBits = BitWidth % APINT_BITS_PER_WORD;
+    uint32_t shift =
+        (highWordBits == 0 ? 0 : APINT_BITS_PER_WORD - highWordBits);
+    int i = _AP_N - 1;
+    uint32_t Count = countLeadingOnes_64(get_pVal(i), shift);
+    if (Count == highWordBits) {
+      for (i--; i >= 0; --i) {
+        if (get_pVal(i) == ~0ULL)
+          Count += APINT_BITS_PER_WORD;
+        else {
+          Count += countLeadingOnes_64(get_pVal(i), 0);
+          break;
+        }
+      }
+    }
+    return Count;
+  }
+
+  /// countTrailingZeros - This function is an ap_private version of the
+  /// countTrailingZoers_{32,64} functions in MathExtras.h. It counts
+  /// the number of zeros from the least significant bit to the first set bit.
+  /// @returns BitWidth if the value is zero.
+  /// @returns the number of zeros from the least significant bit to the first
+  /// one bit.
+  /// @brief Count the number of trailing zero bits.
+  INLINE uint32_t countTrailingZeros() const {
+    uint32_t Count = 0;
+    uint32_t i = 0;
+    for (; i < _AP_N && get_pVal(i) == 0; ++i) Count += APINT_BITS_PER_WORD;
+    if (i < _AP_N) Count += ap_private_ops::CountTrailingZeros_64(get_pVal(i));
+    return AESL_std::min(Count, BitWidth);
+  }
+  /// countPopulation - This function is an ap_private version of the
+  /// countPopulation_{32,64} functions in MathExtras.h. It counts the number
+  /// of 1 bits in the ap_private value.
+  /// @returns 0 if the value is zero.
+  /// @returns the number of set bits.
+  /// @brief Count the number of bits set.
+  INLINE uint32_t countPopulation() const {
+    uint32_t Count = 0;
+    for (int i = 0; i < _AP_N - 1; ++i)
+      Count += ap_private_ops::CountPopulation_64(pVal[i]);
+    Count += ap_private_ops::CountPopulation_64(pVal[_AP_N - 1] & mask);
+    return Count;
+  }
+
+  /// @}
+  /// @name Conversion Functions
+  /// @
+
+  /// This is used internally to convert an ap_private to a string.
+  /// @brief Converts an ap_private to a std::string
+  INLINE std::string toString(uint8_t radix, bool wantSigned) const;
+
+  /// Considers the ap_private to be unsigned and converts it into a string in
+  /// the
+  /// radix given. The radix can be 2, 8, 10 or 16.
+  /// @returns a character interpretation of the ap_private
+  /// @brief Convert unsigned ap_private to string representation.
+  INLINE std::string toStringUnsigned(uint8_t radix = 10) const {
+    return toString(radix, false);
+  }
+
+  /// Considers the ap_private to be unsigned and converts it into a string in
+  /// the
+  /// radix given. The radix can be 2, 8, 10 or 16.
+  /// @returns a character interpretation of the ap_private
+  /// @brief Convert unsigned ap_private to string representation.
+  INLINE std::string toStringSigned(uint8_t radix = 10) const {
+    return toString(radix, true);
+  }
+
+  /// @brief Converts this ap_private to a double value.
+  INLINE double roundToDouble(bool isSigned) const {
+    // Handle the simple case where the value is contained in one uint64_t.
+    if (isSingleWord() || getActiveBits() <= APINT_BITS_PER_WORD) {
+      uint64_t val = pVal[0];
+      if (isSigned) {
+        int64_t sext = ((int64_t(val)) << (64 - BitWidth)) >> (64 - BitWidth);
+        return double(sext);
+      } else
+        return double(val);
+    }
+
+    // Determine if the value is negative.
+    bool isNeg = isSigned ? (*this)[BitWidth - 1] : false;
+
+    // Construct the absolute value if we're negative.
+    ap_private<_AP_W, _AP_S> Tmp(isNeg ? -(*this) : (*this));
+
+    // Figure out how many bits we're using.
+    uint32_t n = Tmp.getActiveBits();
+
+    // The exponent (without bias normalization) is just the number of bits
+    // we are using. Note that the sign bit is gone since we constructed the
+    // absolute value.
+    uint64_t exp = n;
+
+    // Return infinity for exponent overflow
+    if (exp > 1023) {
+      if (!isSigned || !isNeg)
+        return std::numeric_limits<double>::infinity();
+      else
+        return -std::numeric_limits<double>::infinity();
+    }
+    exp += 1023; // Increment for 1023 bias
+
+    // Number of bits in mantissa is 52. To obtain the mantissa value, we must
+    // extract the high 52 bits from the correct words in pVal.
+    uint64_t mantissa;
+    unsigned hiWord = whichWord(n - 1);
+    if (hiWord == 0) {
+      mantissa = Tmp.get_pVal(0);
+      if (n > 52)
+        (mantissa) >>= (n - 52); // shift down, we want the top 52 bits.
+    } else {
+      assert(hiWord > 0 && "High word is negative?");
+      uint64_t hibits = (Tmp.get_pVal(hiWord))
+                        << (52 - n % APINT_BITS_PER_WORD);
+      uint64_t lobits =
+          (Tmp.get_pVal(hiWord - 1)) >> (11 + n % APINT_BITS_PER_WORD);
+      mantissa = hibits | lobits;
+    }
+
+    // The leading bit of mantissa is implicit, so get rid of it.
+    uint64_t sign = isNeg ? (1ULL << (APINT_BITS_PER_WORD - 1)) : 0;
+    union {
+      double __D;
+      uint64_t __I;
+    } __T;
+    __T.__I = sign | ((exp) << 52) | mantissa;
+    return __T.__D;
+  }
+
+  /// @brief Converts this unsigned ap_private to a double value.
+  INLINE double roundToDouble() const { return roundToDouble(false); }
+
+  /// @brief Converts this signed ap_private to a double value.
+  INLINE double signedRoundToDouble() const { return roundToDouble(true); }
+
+  /// The conversion does not do a translation from integer to double, it just
+  /// re-interprets the bits as a double. Note that it is valid to do this on
+  /// any bit width. Exactly 64 bits will be translated.
+  /// @brief Converts ap_private bits to a double
+  INLINE double bitsToDouble() const {
+    union {
+      uint64_t __I;
+      double __D;
+    } __T;
+    __T.__I = pVal[0];
+    return __T.__D;
+  }
+
+  /// The conversion does not do a translation from integer to float, it just
+  /// re-interprets the bits as a float. Note that it is valid to do this on
+  /// any bit width. Exactly 32 bits will be translated.
+  /// @brief Converts ap_private bits to a double
+  INLINE float bitsToFloat() const {
+    union {
+      uint32_t __I;
+      float __F;
+    } __T;
+    __T.__I = uint32_t(pVal[0]);
+    return __T.__F;
+  }
+
+  /// The conversion does not do a translation from double to integer, it just
+  /// re-interprets the bits of the double. Note that it is valid to do this on
+  /// any bit width but bits from V may get truncated.
+  /// @brief Converts a double to ap_private bits.
+  INLINE ap_private& doubleToBits(double __V) {
+    union {
+      uint64_t __I;
+      double __D;
+    } __T;
+    __T.__D = __V;
+    pVal[0] = __T.__I;
+    return *this;
+  }
+
+  /// The conversion does not do a translation from float to integer, it just
+  /// re-interprets the bits of the float. Note that it is valid to do this on
+  /// any bit width but bits from V may get truncated.
+  /// @brief Converts a float to ap_private bits.
+  INLINE ap_private& floatToBits(float __V) {
+    union {
+      uint32_t __I;
+      float __F;
+    } __T;
+    __T.__F = __V;
+    pVal[0] = __T.__I;
+  }
+
+  // Reduce operation
+  //-----------------------------------------------------------
+  INLINE bool and_reduce() const { return isMaxValue(); }
+
+  INLINE bool nand_reduce() const { return isMinValue(); }
+
+  INLINE bool or_reduce() const { return (bool)countPopulation(); }
+
+  INLINE bool nor_reduce() const { return countPopulation() == 0; }
+
+  INLINE bool xor_reduce() const {
+    unsigned int i = countPopulation();
+    return (i % 2) ? true : false;
+  }
+
+  INLINE bool xnor_reduce() const {
+    unsigned int i = countPopulation();
+    return (i % 2) ? false : true;
+  }
+  INLINE std::string to_string(uint8_t radix = 16, bool sign = false) const {
+    return toString(radix, radix == 10 ? _AP_S : sign);
+  }
+}; // End of class ap_private <_AP_W, _AP_S, false>
+
+namespace ap_private_ops {
+
+enum { APINT_BITS_PER_WORD = 64 };
+template <int _AP_W, bool _AP_S>
+INLINE bool operator==(uint64_t V1, const ap_private<_AP_W, _AP_S>& V2) {
+  return V2 == V1;
+}
+
+template <int _AP_W, bool _AP_S>
+INLINE bool operator!=(uint64_t V1, const ap_private<_AP_W, _AP_S>& V2) {
+  return V2 != V1;
+}
+
+template <int _AP_W, bool _AP_S, int index>
+INLINE bool get(const ap_private<_AP_W, _AP_S>& a) {
+  static const uint64_t mask = 1ULL << (index & 0x3f);
+  return ((mask & a.get_pVal((index) >> 6)) != 0);
+}
+
+template <int _AP_W, bool _AP_S, int msb_index, int lsb_index>
+INLINE void set(ap_private<_AP_W, _AP_S>& a,
+                const ap_private<AP_MAX(msb_index, 1), true>& mark1 = 0,
+                const ap_private<AP_MAX(lsb_index, 1), true>& mark2 = 0) {
+  enum {
+    APINT_BITS_PER_WORD = 64,
+    lsb_word = lsb_index / APINT_BITS_PER_WORD,
+    msb_word = msb_index / APINT_BITS_PER_WORD,
+    msb = msb_index % APINT_BITS_PER_WORD,
+    lsb = lsb_index % APINT_BITS_PER_WORD
+  };
+  if (msb_word == lsb_word) {
+    const uint64_t mask = ~0ULL >>
+                          (lsb) << (APINT_BITS_PER_WORD - msb + lsb - 1) >>
+                          (APINT_BITS_PER_WORD - msb - 1);
+    // a.set_pVal(msb_word, a.get_pVal(msb_word)  | mask);
+    a.get_pVal(msb_word) |= mask;
+  } else {
+    const uint64_t lsb_mask = ~0ULL >> (lsb) << (lsb);
+    const uint64_t msb_mask = ~0ULL << (APINT_BITS_PER_WORD - msb - 1) >>
+                              (APINT_BITS_PER_WORD - msb - 1);
+    // a.set_pVal(lsb_word, a.get_pVal(lsb_word) | lsb_mask);
+    a.get_pVal(lsb_word) |= lsb_mask;
+    for (int i = lsb_word + 1; i < msb_word; i++) {
+      a.set_pVal(i, ~0ULL);
+      // a.get_pVal(i)=0;
+    }
+    // a.set_pVal(msb_word, a.get_pVal(msb_word) | msb_mask);
+
+    a.get_pVal(msb_word) |= msb_mask;
+  }
+  a.clearUnusedBits();
+}
+
+template <int _AP_W, bool _AP_S, int msb_index, int lsb_index>
+INLINE void clear(ap_private<_AP_W, _AP_S>& a,
+                  const ap_private<AP_MAX(msb_index, 1), true>& mark1 = 0,
+                  const ap_private<AP_MAX(lsb_index, 1), true>& mark2 = 0) {
+  enum {
+    APINT_BITS_PER_WORD = 64,
+    lsb_word = lsb_index / APINT_BITS_PER_WORD,
+    msb_word = msb_index / APINT_BITS_PER_WORD,
+    msb = msb_index % APINT_BITS_PER_WORD,
+    lsb = lsb_index % APINT_BITS_PER_WORD
+  };
+  if (msb_word == lsb_word) {
+    const uint64_t mask =
+        ~(~0ULL >> (lsb) << (APINT_BITS_PER_WORD - msb + lsb - 1) >>
+          (APINT_BITS_PER_WORD - msb - 1));
+    // a.set_pVal(msb_word, a.get_pVal(msb_word) & mask);
+    a.get_pVal(msb_word) &= mask;
+  } else {
+    const uint64_t lsb_mask = ~(~0ULL >> (lsb) << (lsb));
+    const uint64_t msb_mask = ~(~0ULL << (APINT_BITS_PER_WORD - msb - 1) >>
+                                (APINT_BITS_PER_WORD - msb - 1));
+    // a.set_pVal(lsb_word, a.get_pVal(lsb_word) & lsb_mask);
+    a.get_pVal(lsb_word) &= lsb_mask;
+    for (int i = lsb_word + 1; i < msb_word; i++) {
+      // a.set_pVal(i, 0);
+      a.get_pVal(i) = 0;
+    }
+    // a.set_pVal(msb_word, a.get_pVal(msb_word) & msb_mask);
+    a.get_pVal(msb_word) &= msb_mask;
+  }
+  a.clearUnusedBits();
+}
+
+template <int _AP_W, bool _AP_S, int index>
+INLINE void set(ap_private<_AP_W, _AP_S>& a,
+                const ap_private<AP_MAX(index, 1), true>& mark = 0) {
+  enum { APINT_BITS_PER_WORD = 64, word = index / APINT_BITS_PER_WORD };
+  static const uint64_t mask = 1ULL << (index % APINT_BITS_PER_WORD);
+  // a.set_pVal(word, a.get_pVal(word) | mask);
+  a.get_pVal(word) |= mask;
+  a.clearUnusedBits();
+}
+
+template <int _AP_W, bool _AP_S, int index>
+INLINE void clear(ap_private<_AP_W, _AP_S>& a,
+                  const ap_private<AP_MAX(index, 1), true>& mark = 0) {
+  enum { APINT_BITS_PER_WORD = 64, word = index / APINT_BITS_PER_WORD };
+  static const uint64_t mask = ~(1ULL << (index % APINT_BITS_PER_WORD));
+  // a.set_pVal(word, a.get_pVal(word) & mask);
+  a.get_pVal(word) &= mask;
+  a.clearUnusedBits();
+}
+
+} // End of ap_private_ops namespace
+
+template <int _AP_W, bool _AP_S>
+INLINE std::string ap_private<_AP_W, _AP_S, false>::toString(
+    uint8_t radix, bool wantSigned) const {
+  assert((radix == 10 || radix == 8 || radix == 16 || radix == 2) &&
+         "Radix should be 2, 8, 10, or 16!");
+  static const char* digits[] = {"0", "1", "2", "3", "4", "5", "6", "7",
+                                 "8", "9", "A", "B", "C", "D", "E", "F"};
+  std::string result;
+
+  if (radix != 10) {
+    // For the 2, 8 and 16 bit cases, we can just shift instead of divide
+    // because the number of bits per digit (1,3 and 4 respectively) divides
+    // equaly. We just shift until there value is zero.
+
+    // First, check for a zero value and just short circuit the logic below.
+    if (*this == (uint64_t)(0))
+      result = "0";
+    else {
+      ap_private<_AP_W, false> tmp(*this);
+      size_t insert_at = 0;
+      bool leading_zero = true;
+      if (wantSigned && isNegative()) {
+        // They want to print the signed version and it is a negative value
+        // Flip the bits and add one to turn it into the equivalent positive
+        // value and put a '-' in the result.
+        tmp.flip();
+        tmp++;
+        tmp.clearUnusedBitsToZero();
+        result = "-";
+        insert_at = 1;
+        leading_zero = false;
+      }
+      switch (radix) {
+        case 2:
+          result += "0b";
+          break;
+        case 8:
+          result += "0o";
+          break;
+        case 16:
+          result += "0x";
+          break;
+        default:
+          assert("invalid radix" && 0);
+      }
+      insert_at += 2;
+      // Just shift tmp right for each digit width until it becomes zero
+      uint32_t shift = (radix == 16 ? 4 : (radix == 8 ? 3 : 1));
+      uint64_t mask = radix - 1;
+      ap_private<_AP_W, false> zero(0);
+      unsigned bits = 0;
+      while (tmp.ne(zero)) {
+        uint64_t digit = tmp.get_VAL() & mask;
+        result.insert(insert_at, digits[digit]);
+        tmp = tmp.lshr(shift);
+        ++bits;
+      }
+      bits *= shift;
+      if (bits < _AP_W && leading_zero) result.insert(insert_at, digits[0]);
+    }
+    return result;
+  }
+
+  ap_private<_AP_W, false> tmp(*this);
+  ap_private<_AP_W, false> divisor(radix);
+  ap_private<_AP_W, false> zero(0);
+  size_t insert_at = 0;
+  if (wantSigned && isNegative()) {
+    // They want to print the signed version and it is a negative value
+    // Flip the bits and add one to turn it into the equivalent positive
+    // value and put a '-' in the result.
+    tmp.flip();
+    tmp++;
+    tmp.clearUnusedBitsToZero();
+    result = "-";
+    insert_at = 1;
+  }
+  if (tmp == ap_private<_AP_W, false>(0))
+    result = "0";
+  else
+    while (tmp.ne(zero)) {
+      ap_private<_AP_W, false> APdigit(0);
+      ap_private<_AP_W, false> tmp2(0);
+      ap_private_ops::divide(tmp, tmp.getNumWords(), divisor,
+                             divisor.getNumWords(), &tmp2, &APdigit);
+      uint64_t digit = APdigit.getZExtValue();
+      assert(digit < radix && "divide failed");
+      result.insert(insert_at, digits[digit]);
+      tmp = tmp2;
+    }
+
+  return result;
+} // End of ap_private<_AP_W, _AP_S, false>::toString()
+
+template <int _AP_W, bool _AP_S>
+std::ostream &operator<<(std::ostream &os, const ap_private<_AP_W, _AP_S> &x) {
+  std::ios_base::fmtflags ff = std::cout.flags();
+  if (ff & std::cout.hex) {
+    os << x.toString(16, false); // don't print sign
+  } else if (ff & std::cout.oct) {
+    os << x.toString(8, false); // don't print sign
+  } else {
+    os << x.toString(10, _AP_S);
+  }
+  return os;
+}
+
+// ------------------------------------------------------------ //
+//           XXX moved here from ap_int_sim.h  XXX              //
+// ------------------------------------------------------------ //
+
+/// Concatination reference.
+/// Proxy class which allows concatination to be used as rvalue(for reading) and
+/// lvalue(for writing)
+// ----------------------------------------------------------------
+// template <int _AP_W1, typename _AP_T1, int _AP_W2, typename _AP_T2>
+// struct ap_concat_ref {
+//#ifdef _MSC_VER
+//#pragma warning(disable : 4521 4522)
+//#endif
+//  enum {
+//    _AP_WR = _AP_W1 + _AP_W2,
+//  };
+//  _AP_T1& mbv1;
+//  _AP_T2& mbv2;
+//
+//  INLINE ap_concat_ref(const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>&
+//  ref)
+//      : mbv1(ref.mbv1), mbv2(ref.mbv2) {}
+//
+//  INLINE ap_concat_ref(_AP_T1& bv1, _AP_T2& bv2) : mbv1(bv1), mbv2(bv2) {}
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE ap_concat_ref& operator=(const ap_private<_AP_W3, _AP_S3>& val) {
+//    ap_private<_AP_W1 + _AP_W2, false> vval(val);
+//    int W_ref1 = mbv1.length();
+//    int W_ref2 = mbv2.length();
+//    ap_private<_AP_W1, false> mask1(-1);
+//    mask1 >>= _AP_W1 - W_ref1;
+//    ap_private<_AP_W2, false> mask2(-1);
+//    mask2 >>= _AP_W2 - W_ref2;
+//    mbv1.set(ap_private<_AP_W1, false>((vval >> W_ref2) & mask1));
+//    mbv2.set(ap_private<_AP_W2, false>(vval & mask2));
+//    return *this;
+//  }
+//
+//  INLINE ap_concat_ref& operator=(unsigned long long val) {
+//    ap_private<_AP_W1 + _AP_W2, false> tmpVal(val);
+//    return operator=(tmpVal);
+//  }
+//
+//  template <int _AP_W3, typename _AP_T3, int _AP_W4, typename _AP_T4>
+//  INLINE ap_concat_ref& operator=(
+//      const ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4>& val) {
+//    ap_private<_AP_W1 + _AP_W2, false> tmpVal(val);
+//    return operator=(tmpVal);
+//  }
+//
+//  INLINE ap_concat_ref& operator=(
+//      const ap_concat_ref<_AP_W1, _AP_T1, _AP_W2, _AP_T2>& val) {
+//    ap_private<_AP_W1 + _AP_W2, false> tmpVal(val);
+//    return operator=(tmpVal);
+//  }
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE ap_concat_ref& operator=(const _private_bit_ref<_AP_W3, _AP_S3>&
+//  val) {
+//    ap_private<_AP_W1 + _AP_W2, false> tmpVal(val);
+//    return operator=(tmpVal);
+//  }
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE ap_concat_ref& operator=(const _private_range_ref<_AP_W3, _AP_S3>&
+//  val) {
+//    ap_private<_AP_W1 + _AP_W2, false> tmpVal(val);
+//    return operator=(tmpVal);
+//  }
+//
+//  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+//            ap_o_mode _AP_O3, int _AP_N3>
+//  INLINE ap_concat_ref& operator=(
+//      const af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>& val)
+//      {
+//    return operator=((const ap_private<_AP_W3, false>)(val));
+//  }
+//
+//  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+//            ap_o_mode _AP_O3, int _AP_N3>
+//  INLINE ap_concat_ref& operator=(
+//      const ap_fixed_base<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>&
+//          val) {
+//    return operator=(val.to_ap_private());
+//  }
+//
+//  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+//            ap_o_mode _AP_O3, int _AP_N3>
+//  INLINE ap_concat_ref& operator=(
+//      const af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>& val) {
+//    return operator=((unsigned long long)(bool)(val));
+//  }
+//
+//  INLINE operator ap_private<_AP_WR, false>() const { return get(); }
+//
+//  INLINE operator unsigned long long() const { return get().to_uint64(); }
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+//                       _private_range_ref<_AP_W3, _AP_S3> >
+//  operator,(const _private_range_ref<_AP_W3, _AP_S3> &a2) {
+//    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+//                         _private_range_ref<_AP_W3, _AP_S3> >(
+//        *this, const_cast<_private_range_ref<_AP_W3, _AP_S3>&>(a2));
+//  }
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE
+//      ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3, ap_private<_AP_W3, _AP_S3>
+//      >
+//      operator,(ap_private<_AP_W3, _AP_S3> &a2) {
+//    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+//                         ap_private<_AP_W3, _AP_S3> >(*this, a2);
+//  }
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE
+//      ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3, ap_private<_AP_W3, _AP_S3>
+//      >
+//      operator,(const ap_private<_AP_W3, _AP_S3> &a2) {
+//    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3,
+//                         ap_private<_AP_W3, _AP_S3> >(
+//        *this, const_cast<ap_private<_AP_W3, _AP_S3>&>(a2));
+//  }
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE ap_concat_ref<_AP_WR, ap_concat_ref, 1, _private_bit_ref<_AP_W3,
+//  _AP_S3> >
+//  operator,(const _private_bit_ref<_AP_W3, _AP_S3> &a2) {
+//    return ap_concat_ref<_AP_WR, ap_concat_ref, 1, _private_bit_ref<_AP_W3,
+//    _AP_S3> >(
+//        *this, const_cast<_private_bit_ref<_AP_W3, _AP_S3>&>(a2));
+//  }
+//
+//  template <int _AP_W3, typename _AP_T3, int _AP_W4, typename _AP_T4>
+//  INLINE ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3 + _AP_W4,
+//                       ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4> >
+//  operator,(const ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4> &a2) {
+//    return ap_concat_ref<_AP_WR, ap_concat_ref, _AP_W3 + _AP_W4,
+//                         ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4> >(
+//        *this, const_cast<ap_concat_ref<_AP_W3, _AP_T3, _AP_W4,
+//        _AP_T4>&>(a2));
+//  }
+//
+//  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+//            ap_o_mode _AP_O3, int _AP_N3>
+//  INLINE ap_concat_ref<
+//      _AP_WR, ap_concat_ref, _AP_W3,
+//      af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3> >
+//  operator,(
+//      const af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3> &a2)
+//      {
+//    return ap_concat_ref<
+//        _AP_WR, ap_concat_ref, _AP_W3,
+//        af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3> >(
+//        *this,
+//        const_cast<
+//            af_range_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3,
+//            _AP_N3>&>(a2));
+//  }
+//
+//  template <int _AP_W3, int _AP_I3, bool _AP_S3, ap_q_mode _AP_Q3,
+//            ap_o_mode _AP_O3, int _AP_N3>
+//  INLINE
+//      ap_concat_ref<_AP_WR, ap_concat_ref, 1,
+//                    af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3>
+//                    >
+//      operator,(const af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3,
+//      _AP_N3>
+//                    &a2) {
+//    return ap_concat_ref<
+//        _AP_WR, ap_concat_ref, 1,
+//        af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3, _AP_N3> >(
+//        *this,
+//        const_cast<af_bit_ref<_AP_W3, _AP_I3, _AP_S3, _AP_Q3, _AP_O3,
+//        _AP_N3>&>(
+//            a2));
+//  }
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE ap_private<AP_MAX(_AP_WR, _AP_W3), _AP_S3> operator&(
+//      const ap_private<_AP_W3, _AP_S3>& a2) {
+//    return get() & a2;
+//  }
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE ap_private<AP_MAX(_AP_WR, _AP_W3), _AP_S3> operator|(
+//      const ap_private<_AP_W3, _AP_S3>& a2) {
+//    return get() | a2;
+//  }
+//
+//  template <int _AP_W3, bool _AP_S3>
+//  INLINE ap_private<AP_MAX(_AP_WR, _AP_W3), _AP_S3> operator^(
+//      const ap_private<_AP_W3, _AP_S3>& a2) {
+//    return ap_private<AP_MAX(_AP_WR, _AP_W3), _AP_S3>(get() ^ a2);
+//  }
+//
+//  INLINE const ap_private<_AP_WR, false> get() const {
+//    ap_private<_AP_W1 + _AP_W2, false> tmpVal =
+//        ap_private<_AP_W1 + _AP_W2, false>(mbv1.get());
+//    ap_private<_AP_W1 + _AP_W2, false> tmpVal2 =
+//        ap_private<_AP_W1 + _AP_W2, false>(mbv2.get());
+//    int W_ref2 = mbv2.length();
+//    tmpVal <<= W_ref2;
+//    tmpVal |= tmpVal2;
+//    return tmpVal;
+//  }
+//
+//  INLINE const ap_private<_AP_WR, false> get() {
+//    ap_private<_AP_W1 + _AP_W2, false> tmpVal =
+//        ap_private<_AP_W1 + _AP_W2, false>(mbv1.get());
+//    ap_private<_AP_W1 + _AP_W2, false> tmpVal2 =
+//        ap_private<_AP_W1 + _AP_W2, false>(mbv2.get());
+//    int W_ref2 = mbv2.length();
+//    tmpVal <<= W_ref2;
+//    tmpVal |= tmpVal2;
+//    return tmpVal;
+//  }
+//
+//  template <int _AP_W3>
+//  INLINE void set(const ap_private<_AP_W3, false>& val) {
+//    ap_private<_AP_W1 + _AP_W2, false> vval(val);
+//    int W_ref1 = mbv1.length();
+//    int W_ref2 = mbv2.length();
+//    ap_private<_AP_W1, false> mask1(-1);
+//    mask1 >>= _AP_W1 - W_ref1;
+//    ap_private<_AP_W2, false> mask2(-1);
+//    mask2 >>= _AP_W2 - W_ref2;
+//    mbv1.set(ap_private<_AP_W1, false>((vval >> W_ref2) & mask1));
+//    mbv2.set(ap_private<_AP_W2, false>(vval & mask2));
+//  }
+//
+//  INLINE int length() const { return mbv1.length() + mbv2.length(); }
+//
+//  INLINE std::string to_string(uint8_t radix = 2) const {
+//    return get().to_string(radix);
+//  }
+//}; // struct ap_concat_ref.
+
+/// Range(slice) reference
+/// Proxy class, which allows part selection to be used as rvalue(for reading)
+/// and lvalue(for writing)
+//------------------------------------------------------------
+template <int _AP_W, bool _AP_S>
+struct _private_range_ref {
+#ifdef _MSC_VER
+#pragma warning(disable : 4521 4522)
+#endif
+  ap_private<_AP_W, _AP_S>& d_bv;
+  int l_index;
+  int h_index;
+
+ public:
+  /// copy ctor.
+  INLINE _private_range_ref(const _private_range_ref<_AP_W, _AP_S>& ref)
+      : d_bv(ref.d_bv), l_index(ref.l_index), h_index(ref.h_index) {}
+
+  /// direct ctor.
+  INLINE _private_range_ref(ap_private<_AP_W, _AP_S>* bv, int h, int l)
+      : d_bv(*bv), l_index(l), h_index(h) {
+    _AP_WARNING(h < 0 || l < 0,
+                "Higher bound (%d) and lower bound (%d) cannot be "
+                "negative.",
+                h, l);
+    _AP_WARNING(h >= _AP_W || l >= _AP_W,
+                "Higher bound (%d) or lower bound (%d) out of range (%d).", h, l,
+                _AP_W);
+  }
+
+  /// compound or assignment.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_range_ref<_AP_W, _AP_S>& operator|=(
+      const _private_range_ref<_AP_W2, _AP_S2>& ref) {
+    _AP_WARNING((h_index - l_index) != (ref.h_index - ref.l_index),
+                "Bitsize mismach for ap_private<>.range() &= "
+                "ap_private<>.range().");
+    this->d_bv |= ref.d_bv;
+    return *this;
+  }
+
+  /// compound or assignment with root type.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_range_ref<_AP_W, _AP_S>& operator|=(
+      const _AP_ROOT_TYPE<_AP_W2, _AP_S2>& ref) {
+    _AP_WARNING((h_index - l_index + 1) != _AP_W2,
+                "Bitsize mismach for ap_private<>.range() |= _AP_ROOT_TYPE<>.");
+    this->d_bv |= ref.V;
+    return *this;
+  }
+
+  /// compound and assignment.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_range_ref<_AP_W, _AP_S>& operator&=(
+      const _private_range_ref<_AP_W2, _AP_S2>& ref) {
+    _AP_WARNING((h_index - l_index) != (ref.h_index - ref.l_index),
+                "Bitsize mismach for ap_private<>.range() &= "
+                "ap_private<>.range().");
+    this->d_bv &= ref.d_bv;
+    return *this;
+  };
+
+  /// compound and assignment with root type.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_range_ref<_AP_W, _AP_S>& operator&=(
+      const _AP_ROOT_TYPE<_AP_W2, _AP_S2>& ref) {
+    _AP_WARNING((h_index - l_index + 1) != _AP_W2,
+                "Bitsize mismach for ap_private<>.range() &= _AP_ROOT_TYPE<>.");
+    this->d_bv &= ref.V;
+    return *this;
+  }
+
+  /// compound xor assignment.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_range_ref<_AP_W, _AP_S>& operator^=(
+      const _private_range_ref<_AP_W2, _AP_S2>& ref) {
+    _AP_WARNING((h_index - l_index) != (ref.h_index - ref.l_index),
+                "Bitsize mismach for ap_private<>.range() ^= "
+                "ap_private<>.range().");
+    this->d_bv ^= ref.d_bv;
+    return *this;
+  };
+
+  /// compound xor assignment with root type.
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_range_ref<_AP_W, _AP_S>& operator^=(
+      const _AP_ROOT_TYPE<_AP_W2, _AP_S2>& ref) {
+    _AP_WARNING((h_index - l_index + 1) != _AP_W2,
+                "Bitsize mismach for ap_private<>.range() ^= _AP_ROOT_TYPE<>.");
+    this->d_bv ^= ref.V;
+    return *this;
+  }
+
+  /// @name convertors.
+  //  @{
+  INLINE operator ap_private<_AP_W, false>() const {
+    ap_private<_AP_W, false> val(0);
+    if (h_index >= l_index) {
+      if (_AP_W > 64) {
+        val = d_bv;
+        ap_private<_AP_W, false> mask(-1);
+        mask >>= _AP_W - (h_index - l_index + 1);
+        val >>= l_index;
+        val &= mask;
+      } else {
+        const static uint64_t mask = (~0ULL >> (64 > _AP_W ? (64 - _AP_W) : 0));
+        val = (d_bv >> l_index) & (mask >> (_AP_W - (h_index - l_index + 1)));
+      }
+    } else {
+      for (int i = 0, j = l_index; j >= 0 && j >= h_index; j--, i++)
+        if ((d_bv)[j]) val.set(i);
+    }
+    return val;
+  }
+
+  INLINE operator unsigned long long() const { return to_uint64(); }
+  //  @}
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_range_ref& operator=(const ap_private<_AP_W2, _AP_S2>& val) {
+    ap_private<_AP_W, false> vval = ap_private<_AP_W, false>(val);
+    if (l_index > h_index) {
+      for (int i = 0, j = l_index; j >= 0 && j >= h_index; j--, i++)
+        (vval)[i] ? d_bv.set(j) : d_bv.clear(j);
+    } else {
+      if (_AP_W > 64) {
+        ap_private<_AP_W, false> mask(-1);
+        if (l_index > 0) {
+          mask <<= l_index;
+          vval <<= l_index;
+        }
+        if (h_index < _AP_W - 1) {
+          ap_private<_AP_W, false> mask2(-1);
+          mask2 >>= _AP_W - h_index - 1;
+          mask &= mask2;
+          vval &= mask2;
+        }
+        mask.flip();
+        d_bv &= mask;
+        d_bv |= vval;
+      } else {
+        unsigned shift = 64 - _AP_W;
+        uint64_t mask = ~0ULL >> (shift);
+        if (l_index > 0) {
+          vval = mask & vval << l_index;
+          mask = mask & mask << l_index;
+        }
+        if (h_index < _AP_W - 1) {
+          uint64_t mask2 = mask;
+          mask2 >>= (_AP_W - h_index - 1);
+          mask &= mask2;
+          vval &= mask2;
+        }
+        mask = ~mask;
+        d_bv &= mask;
+        d_bv |= vval;
+      }
+    }
+    return *this;
+  } // operator=(const ap_private<>&)
+
+  INLINE _private_range_ref& operator=(unsigned long long val) {
+    const ap_private<_AP_W, _AP_S> vval = val;
+    return operator=(vval);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_range_ref& operator=(
+      const _private_bit_ref<_AP_W2, _AP_S2>& val) {
+    return operator=((unsigned long long)(bool)val);
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE _private_range_ref& operator=(
+      const _private_range_ref<_AP_W2, _AP_S2>& val) {
+    const ap_private<_AP_W, false> tmpVal(val);
+    return operator=(tmpVal);
+  }
+
+//  template <int _AP_W3, typename _AP_T3, int _AP_W4, typename _AP_T4>
+//  INLINE _private_range_ref& operator=(
+//      const ap_concat_ref<_AP_W3, _AP_T3, _AP_W4, _AP_T4>& val) {
+//    const ap_private<_AP_W, false> tmpVal(val);
+//    return operator=(tmpVal);
+//  }
+
+  // TODO from ap_int_base, ap_bit_ref and ap_range_ref.
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE _private_range_ref& operator=(
+      const ap_fixed_base<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    return operator=(val.to_ap_int_base().V);
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE _private_range_ref& operator=(
+      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    return operator=(val.operator ap_int_base<_AP_W2, false>().V);
+  }
+
+  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+            ap_o_mode _AP_O2, int _AP_N2>
+  INLINE _private_range_ref& operator=(
+      const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>& val) {
+    return operator=((unsigned long long)(bool)val);
+  }
+
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, _private_range_ref, _AP_W2,
+//                       _private_range_ref<_AP_W2, _AP_S2> >
+//  operator,(const _private_range_ref<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, _private_range_ref, _AP_W2,
+//                         _private_range_ref<_AP_W2, _AP_S2> >(
+//        *this, const_cast<_private_range_ref<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, _private_range_ref, _AP_W2,
+//                       ap_private<_AP_W2, _AP_S2> >
+//  operator,(ap_private<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, _private_range_ref, _AP_W2,
+//                         ap_private<_AP_W2, _AP_S2> >(*this, a2);
+//  }
+//
+//  INLINE
+//  ap_concat_ref<_AP_W, _private_range_ref, _AP_W, ap_private<_AP_W, _AP_S> >
+//  operator,(ap_private<_AP_W, _AP_S>& a2) {
+//    return ap_concat_ref<_AP_W, _private_range_ref, _AP_W,
+//                         ap_private<_AP_W, _AP_S> >(*this, a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<_AP_W, _private_range_ref, 1,
+//                       _private_bit_ref<_AP_W2, _AP_S2> >
+//  operator,(const _private_bit_ref<_AP_W2, _AP_S2> &a2) {
+//    return ap_concat_ref<_AP_W, _private_range_ref, 1,
+//                         _private_bit_ref<_AP_W2, _AP_S2> >(
+//        *this, const_cast<_private_bit_ref<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_concat_ref<_AP_W, _private_range_ref, _AP_W2 + _AP_W3,
+//                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+//  operator,(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) {
+//    return ap_concat_ref<_AP_W, _private_range_ref, _AP_W2 + _AP_W3,
+//                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(
+//        *this, const_cast<ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>&>(a2));
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_concat_ref<
+//      _AP_W, _private_range_ref, _AP_W2,
+//      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//  operator,(
+//      const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> &a2) {
+//    return ap_concat_ref<
+//        _AP_W, _private_range_ref, _AP_W2,
+//        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+//        *this,
+//        const_cast<
+//            af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE
+//      ap_concat_ref<_AP_W, _private_range_ref, 1,
+//                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//      operator,(const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>
+//                    &a2) {
+//    return ap_concat_ref<
+//        _AP_W, _private_range_ref, 1,
+//        af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+//        *this,
+//        const_cast<af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2>&>(
+//            a2));
+//  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator==(const _private_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_private<_AP_W, false> lhs = get();
+    ap_private<_AP_W2, false> rhs = op2.get();
+    return lhs == rhs;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator!=(const _private_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_private<_AP_W, false> lhs = get();
+    ap_private<_AP_W2, false> rhs = op2.get();
+    return lhs != rhs;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>(const _private_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_private<_AP_W, false> lhs = get();
+    ap_private<_AP_W2, false> rhs = op2.get();
+    return lhs > rhs;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator>=(const _private_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_private<_AP_W, false> lhs = get();
+    ap_private<_AP_W2, false> rhs = op2.get();
+    return lhs >= rhs;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<(const _private_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_private<_AP_W, false> lhs = get();
+    ap_private<_AP_W2, false> rhs = op2.get();
+    return lhs < rhs;
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator<=(const _private_range_ref<_AP_W2, _AP_S2>& op2) {
+    ap_private<_AP_W, false> lhs = get();
+    ap_private<_AP_W2, false> rhs = op2.get();
+    return lhs <= rhs;
+  }
+
+  template <int _AP_W2>
+  INLINE void set(const ap_private<_AP_W2, false>& val) {
+    ap_private<_AP_W, _AP_S> vval = val;
+    if (l_index > h_index) {
+      for (int i = 0, j = l_index; j >= 0 && j >= h_index; j--, i++)
+        (vval)[i] ? d_bv.set(j) : d_bv.clear(j);
+    } else {
+      if (_AP_W > 64) {
+        ap_private<_AP_W, _AP_S> mask(-1);
+        if (l_index > 0) {
+          ap_private<_AP_W, false> mask1(-1);
+          mask1 >>= _AP_W - l_index;
+          mask1.flip();
+          mask = mask1;
+          // vval&=mask1;
+          vval <<= l_index;
+        }
+        if (h_index < _AP_W - 1) {
+          ap_private<_AP_W, false> mask2(-1);
+          mask2 <<= h_index + 1;
+          mask2.flip();
+          mask &= mask2;
+          vval &= mask2;
+        }
+        mask.flip();
+        d_bv &= mask;
+        d_bv |= vval;
+      } else {
+        uint64_t mask = ~0ULL >> (64 - _AP_W);
+        if (l_index > 0) {
+          uint64_t mask1 = mask;
+          mask1 = mask & (mask1 >> (_AP_W - l_index));
+          vval = mask & (vval << l_index);
+          mask = ~mask1 & mask;
+          // vval&=mask1;
+        }
+        if (h_index < _AP_W - 1) {
+          uint64_t mask2 = ~0ULL >> (64 - _AP_W);
+          mask2 = mask & (mask2 << (h_index + 1));
+          mask &= ~mask2;
+          vval &= ~mask2;
+        }
+        d_bv &= (~mask & (~0ULL >> (64 - _AP_W)));
+        d_bv |= vval;
+      }
+    }
+  }
+
+  INLINE ap_private<_AP_W, false> get() const {
+    ap_private<_AP_W, false> val(0);
+    if (h_index < l_index) {
+      for (int i = 0, j = l_index; j >= 0 && j >= h_index; j--, i++)
+        if ((d_bv)[j]) val.set(i);
+    } else {
+      val = d_bv;
+      val >>= l_index;
+      if (h_index < _AP_W - 1) {
+        if (_AP_W <= 64) {
+          const static uint64_t mask =
+              (~0ULL >> (64 > _AP_W ? (64 - _AP_W) : 0));
+          val &= (mask >> (_AP_W - (h_index - l_index + 1)));
+        } else {
+          ap_private<_AP_W, false> mask(-1);
+          mask >>= _AP_W - (h_index - l_index + 1);
+          val &= mask;
+        }
+      }
+    }
+    return val;
+  }
+
+  INLINE ap_private<_AP_W, false> get() {
+    ap_private<_AP_W, false> val(0);
+    if (h_index < l_index) {
+      for (int i = 0, j = l_index; j >= 0 && j >= h_index; j--, i++)
+        if ((d_bv)[j]) val.set(i);
+    } else {
+      val = d_bv;
+      val >>= l_index;
+      if (h_index < _AP_W - 1) {
+        if (_AP_W <= 64) {
+          static const uint64_t mask = ~0ULL >> (64 > _AP_W ? (64 - _AP_W) : 0);
+          return val &= ((mask) >> (_AP_W - (h_index - l_index + 1)));
+        } else {
+          ap_private<_AP_W, false> mask(-1);
+          mask >>= _AP_W - (h_index - l_index + 1);
+          val &= mask;
+        }
+      }
+    }
+    return val;
+  }
+
+  INLINE int length() const {
+    return h_index >= l_index ? h_index - l_index + 1 : l_index - h_index + 1;
+  }
+
+  INLINE int to_int() const {
+    ap_private<_AP_W, false> val = get();
+    return val.to_int();
+  }
+
+  INLINE unsigned int to_uint() const {
+    ap_private<_AP_W, false> val = get();
+    return val.to_uint();
+  }
+
+  INLINE long to_long() const {
+    ap_private<_AP_W, false> val = get();
+    return val.to_long();
+  }
+
+  INLINE unsigned long to_ulong() const {
+    ap_private<_AP_W, false> val = get();
+    return val.to_ulong();
+  }
+
+  INLINE ap_slong to_int64() const {
+    ap_private<_AP_W, false> val = get();
+    return val.to_int64();
+  }
+
+  INLINE ap_ulong to_uint64() const {
+    ap_private<_AP_W, false> val = get();
+    return val.to_uint64();
+  }
+
+  INLINE std::string to_string(uint8_t radix = 2) const {
+    return get().to_string(radix);
+  }
+
+  INLINE bool and_reduce() {
+    bool ret = true;
+    bool reverse = l_index > h_index;
+    unsigned low = reverse ? h_index : l_index;
+    unsigned high = reverse ? l_index : h_index;
+    for (unsigned i = low; i != high; ++i) ret &= d_bv[i];
+    return ret;
+  }
+
+  INLINE bool or_reduce() {
+    bool ret = false;
+    bool reverse = l_index > h_index;
+    unsigned low = reverse ? h_index : l_index;
+    unsigned high = reverse ? l_index : h_index;
+    for (unsigned i = low; i != high; ++i) ret |= d_bv[i];
+    return ret;
+  }
+
+  INLINE bool xor_reduce() {
+    bool ret = false;
+    bool reverse = l_index > h_index;
+    unsigned low = reverse ? h_index : l_index;
+    unsigned high = reverse ? l_index : h_index;
+    for (unsigned i = low; i != high; ++i) ret ^= d_bv[i];
+    return ret;
+  }
+}; // struct _private_range_ref.
+
+/// Bit reference
+/// Proxy class, which allows bit selection to be used as rvalue(for reading)
+/// and lvalue(for writing)
+//--------------------------------------------------------------
+template <int _AP_W, bool _AP_S>
+struct _private_bit_ref {
+#ifdef _MSC_VER
+#pragma warning(disable : 4521 4522)
+#endif
+  ap_private<_AP_W, _AP_S>& d_bv;
+  int d_index;
+
+ public:
+  // copy ctor.
+  INLINE _private_bit_ref(const _private_bit_ref<_AP_W, _AP_S>& ref)
+      : d_bv(ref.d_bv), d_index(ref.d_index) {}
+
+  // director ctor.
+  INLINE _private_bit_ref(ap_private<_AP_W, _AP_S>& bv, int index = 0)
+      : d_bv(bv), d_index(index) {
+    _AP_WARNING(d_index < 0, "Index of bit vector  (%d) cannot be negative.\n",
+                d_index);
+    _AP_WARNING(d_index >= _AP_W,
+                "Index of bit vector (%d) out of range (%d).\n", d_index, _AP_W);
+  }
+
+  INLINE operator bool() const { return d_bv.get_bit(d_index); }
+
+  INLINE bool to_bool() const { return operator bool(); }
+
+  template <typename T>
+  INLINE _private_bit_ref& operator=(const T& val) {
+    if (!!val)
+      d_bv.set(d_index);
+    else
+      d_bv.clear(d_index);
+    return *this;
+  }
+
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<1, _private_bit_ref, _AP_W2, ap_private<_AP_W2,
+//  _AP_S2> >
+//  operator,(ap_private<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<1, _private_bit_ref, _AP_W2, ap_private<_AP_W2,
+//    _AP_S2> >(
+//        const_cast<_private_bit_ref<_AP_W, _AP_S>&>(*this), a2);
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<1, _private_bit_ref, _AP_W2,
+//  _private_range_ref<_AP_W2,
+//  _AP_S2> >
+//  operator,(const _private_range_ref<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<1, _private_bit_ref, _AP_W2,
+//    _private_range_ref<_AP_W2,
+//    _AP_S2> >(
+//        const_cast<_private_bit_ref<_AP_W, _AP_S>&>(*this),
+//        const_cast<_private_range_ref<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, bool _AP_S2>
+//  INLINE ap_concat_ref<1, _private_bit_ref, 1, _private_bit_ref<_AP_W2,
+//  _AP_S2> > operator,(
+//      const _private_bit_ref<_AP_W2, _AP_S2> &a2) const {
+//    return ap_concat_ref<1, _private_bit_ref, 1,
+//    _private_bit_ref<_AP_W2, _AP_S2> >(
+//        const_cast<_private_bit_ref<_AP_W, _AP_S>&>(*this),
+//        const_cast<_private_bit_ref<_AP_W2, _AP_S2>&>(a2));
+//  }
+//
+//  INLINE ap_concat_ref<1, _private_bit_ref, 1, _private_bit_ref>
+//  operator,(
+//      const _private_bit_ref &a2) const {
+//    return ap_concat_ref<1, _private_bit_ref, 1, _private_bit_ref>(
+//        const_cast<_private_bit_ref<_AP_W, _AP_S>&>(*this),
+//        const_cast<_private_bit_ref&>(a2));
+//  }
+//
+//  template <int _AP_W2, typename _AP_T2, int _AP_W3, typename _AP_T3>
+//  INLINE ap_concat_ref<1, _private_bit_ref, _AP_W2 + _AP_W3,
+//                       ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >
+//  operator,(const ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> &a2) const {
+//    return ap_concat_ref<1, _private_bit_ref, _AP_W2 + _AP_W3,
+//                         ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3> >(
+//        const_cast<_private_bit_ref<_AP_W, _AP_S>&>(*this),
+//        const_cast<ap_concat_ref<_AP_W2, _AP_T2, _AP_W3, _AP_T3>&>(a2));
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE ap_concat_ref<
+//      1, _private_bit_ref, _AP_W2,
+//      af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >
+//  operator,(const af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2,
+//  _AP_N2>
+//                &a2) const {
+//    return ap_concat_ref<
+//        1, _private_bit_ref, _AP_W2,
+//        af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2, _AP_N2> >(
+//        const_cast<_private_bit_ref<_AP_W, _AP_S>&>(*this),
+//        const_cast<
+//            af_range_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2,
+//            _AP_N2>&>(a2));
+//  }
+//
+//  template <int _AP_W2, int _AP_I2, bool _AP_S2, ap_q_mode _AP_Q2,
+//            ap_o_mode _AP_O2, int _AP_N2>
+//  INLINE
+//      ap_concat_ref<1, _private_bit_ref, 1,
+//                    af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2,
+//                    _AP_N2> >
+//      operator,(const af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2,
+//      _AP_N2>
+//                    &a2) const {
+//    return ap_concat_ref<1, _private_bit_ref, 1, af_bit_ref<_AP_W2,
+//    _AP_I2, _AP_S2,
+//                                                      _AP_Q2, _AP_O2,
+//                                                      _AP_N2> >(
+//        const_cast<_private_bit_ref<_AP_W, _AP_S>&>(*this),
+//        const_cast<af_bit_ref<_AP_W2, _AP_I2, _AP_S2, _AP_Q2, _AP_O2,
+//        _AP_N2>&>(
+//            a2));
+//  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator==(const _private_bit_ref<_AP_W2, _AP_S2>& op) const {
+    return get() == op.get();
+  }
+
+  template <int _AP_W2, bool _AP_S2>
+  INLINE bool operator!=(const _private_bit_ref<_AP_W2, _AP_S2>& op) const {
+    return get() != op.get();
+  }
+
+  INLINE bool get() const { return operator bool(); }
+
+  //  template <int _AP_W3>
+  //  INLINE void set(const ap_private<_AP_W3, false>& val) {
+  //    operator=(val);
+  //  }
+
+  //  INLINE bool operator~() const {
+  //    bool bit = (d_bv)[d_index];
+  //    return bit ? false : true;
+  //  }
+
+  INLINE int length() const { return 1; }
+
+  //  INLINE std::string to_string() const {
+  //    bool val = get();
+  //    return val ? "1" : "0";
+  //  }
+
+}; // struct _private_bit_ref.
+
+// char a[100];
+// char* ptr = a;
+// ap_int<2> n = 3;
+// char* ptr2 = ptr + n*2;
+// avoid ambiguous errors
+#define OP_BIN_MIX_PTR(BIN_OP)                                           \
+  template <typename PTR_TYPE, int _AP_W, bool _AP_S>                    \
+  INLINE PTR_TYPE* operator BIN_OP(PTR_TYPE* i_op,                       \
+                                   const ap_private<_AP_W, _AP_S>& op) { \
+    typename ap_private<_AP_W, _AP_S>::ValType op2 = op;                 \
+    return i_op BIN_OP op2;                                              \
+  }                                                                      \
+  template <typename PTR_TYPE, int _AP_W, bool _AP_S>                    \
+  INLINE PTR_TYPE* operator BIN_OP(const ap_private<_AP_W, _AP_S>& op,   \
+                                   PTR_TYPE* i_op) {                     \
+    typename ap_private<_AP_W, _AP_S>::ValType op2 = op;                 \
+    return op2 BIN_OP i_op;                                              \
+  }
+
+OP_BIN_MIX_PTR(+)
+OP_BIN_MIX_PTR(-)
+#undef OP_BIN_MIX_PTR
+
+// float OP ap_int
+// when ap_int<wa>'s width > 64, then trunc ap_int<w> to ap_int<64>
+#define OP_BIN_MIX_FLOAT(BIN_OP, C_TYPE)                              \
+  template <int _AP_W, bool _AP_S>                                    \
+  INLINE C_TYPE operator BIN_OP(C_TYPE i_op,                          \
+                                const ap_private<_AP_W, _AP_S>& op) { \
+    typename ap_private<_AP_W, _AP_S>::ValType op2 = op;              \
+    return i_op BIN_OP op2;                                           \
+  }                                                                   \
+  template <int _AP_W, bool _AP_S>                                    \
+  INLINE C_TYPE operator BIN_OP(const ap_private<_AP_W, _AP_S>& op,   \
+                                C_TYPE i_op) {                        \
+    typename ap_private<_AP_W, _AP_S>::ValType op2 = op;              \
+    return op2 BIN_OP i_op;                                           \
+  }
+
+#define OPS_MIX_FLOAT(C_TYPE) \
+  OP_BIN_MIX_FLOAT(*, C_TYPE) \
+  OP_BIN_MIX_FLOAT(/, C_TYPE) \
+  OP_BIN_MIX_FLOAT(+, C_TYPE) \
+  OP_BIN_MIX_FLOAT(-, C_TYPE)
+
+OPS_MIX_FLOAT(float)
+OPS_MIX_FLOAT(double)
+#undef OP_BIN_MIX_FLOAT
+#undef OPS_MIX_FLOAT
+
+/// Operators mixing Integers with AP_Int
+// ----------------------------------------------------------------
+
+// partially specialize template argument _AP_C in order that:
+// for _AP_W > 64, we will explicitly convert operand with native data type
+// into corresponding ap_private
+// for _AP_W <= 64, we will implicitly convert operand with ap_private into
+// (unsigned) long long
+#define OP_BIN_MIX_INT(BIN_OP, C_TYPE, _AP_WI, _AP_SI, RTYPE)                  \
+  template <int _AP_W, bool _AP_S>                                             \
+  INLINE                                                                       \
+      typename ap_private<_AP_WI, _AP_SI>::template RType<_AP_W, _AP_S>::RTYPE \
+      operator BIN_OP(C_TYPE i_op, const ap_private<_AP_W, _AP_S>& op) {       \
+    return ap_private<_AP_WI, _AP_SI>(i_op).operator BIN_OP(op);               \
+  }                                                                            \
+  template <int _AP_W, bool _AP_S>                                             \
+  INLINE                                                                       \
+      typename ap_private<_AP_W, _AP_S>::template RType<_AP_WI, _AP_SI>::RTYPE \
+      operator BIN_OP(const ap_private<_AP_W, _AP_S>& op, C_TYPE i_op) {       \
+    return op.operator BIN_OP(ap_private<_AP_WI, _AP_SI>(i_op));               \
+  }
+
+#define OP_REL_MIX_INT(REL_OP, C_TYPE, _AP_W2, _AP_S2)                     \
+  template <int _AP_W, bool _AP_S>                                         \
+  INLINE bool operator REL_OP(const ap_private<_AP_W, _AP_S>& op,          \
+                              C_TYPE op2) {                                \
+    return op.operator REL_OP(ap_private<_AP_W2, _AP_S2>(op2));            \
+  }                                                                        \
+  template <int _AP_W, bool _AP_S>                                         \
+  INLINE bool operator REL_OP(C_TYPE op2,                                  \
+                              const ap_private<_AP_W, _AP_S, false>& op) { \
+    return ap_private<_AP_W2, _AP_S2>(op2).operator REL_OP(op);            \
+  }
+
+#define OP_ASSIGN_MIX_INT(ASSIGN_OP, C_TYPE, _AP_W2, _AP_S2)       \
+  template <int _AP_W, bool _AP_S>                                 \
+  INLINE ap_private<_AP_W, _AP_S>& operator ASSIGN_OP(             \
+      ap_private<_AP_W, _AP_S>& op, C_TYPE op2) {                  \
+    return op.operator ASSIGN_OP(ap_private<_AP_W2, _AP_S2>(op2)); \
+  }
+
+#define OP_BIN_SHIFT_INT(BIN_OP, C_TYPE, _AP_WI, _AP_SI, RTYPE)                \
+  template <int _AP_W, bool _AP_S>                                             \
+  C_TYPE operator BIN_OP(C_TYPE i_op,                                          \
+                         const ap_private<_AP_W, _AP_S, false>& op) {          \
+    return i_op BIN_OP(op.get_VAL());                                          \
+  }                                                                            \
+  template <int _AP_W, bool _AP_S>                                             \
+  INLINE                                                                       \
+      typename ap_private<_AP_W, _AP_S>::template RType<_AP_WI, _AP_SI>::RTYPE \
+      operator BIN_OP(const ap_private<_AP_W, _AP_S>& op, C_TYPE i_op) {       \
+    return op.operator BIN_OP(i_op);                                           \
+  }
+
+#define OP_ASSIGN_RSHIFT_INT(ASSIGN_OP, C_TYPE, _AP_W2, _AP_S2) \
+  template <int _AP_W, bool _AP_S>                              \
+  INLINE ap_private<_AP_W, _AP_S>& operator ASSIGN_OP(          \
+      ap_private<_AP_W, _AP_S>& op, C_TYPE op2) {               \
+    op = op.operator>>(op2);                                    \
+    return op;                                                  \
+  }
+
+#define OP_ASSIGN_LSHIFT_INT(ASSIGN_OP, C_TYPE, _AP_W2, _AP_S2) \
+  template <int _AP_W, bool _AP_S>                              \
+  INLINE ap_private<_AP_W, _AP_S>& operator ASSIGN_OP(          \
+      ap_private<_AP_W, _AP_S>& op, C_TYPE op2) {               \
+    op = op.operator<<(op2);                                    \
+    return op;                                                  \
+  }
+
+#define OPS_MIX_INT(C_TYPE, _AP_W2, _AP_S2)              \
+  OP_BIN_MIX_INT(*, C_TYPE, (_AP_W2), (_AP_S2), mult)    \
+  OP_BIN_MIX_INT(+, C_TYPE, (_AP_W2), (_AP_S2), plus)    \
+  OP_BIN_MIX_INT(-, C_TYPE, (_AP_W2), (_AP_S2), minus)   \
+  OP_BIN_MIX_INT(/, C_TYPE, (_AP_W2), (_AP_S2), div)     \
+  OP_BIN_MIX_INT(%, C_TYPE, (_AP_W2), (_AP_S2), mod)     \
+  OP_BIN_MIX_INT(&, C_TYPE, (_AP_W2), (_AP_S2), logic)   \
+  OP_BIN_MIX_INT(|, C_TYPE, (_AP_W2), (_AP_S2), logic)   \
+  OP_BIN_MIX_INT (^, C_TYPE, (_AP_W2), (_AP_S2), logic)  \
+  OP_BIN_SHIFT_INT(>>, C_TYPE, (_AP_W2), (_AP_S2), arg1) \
+  OP_BIN_SHIFT_INT(<<, C_TYPE, (_AP_W2), (_AP_S2), arg1) \
+                                                         \
+  OP_ASSIGN_MIX_INT(+=, C_TYPE, (_AP_W2), (_AP_S2))      \
+  OP_ASSIGN_MIX_INT(-=, C_TYPE, (_AP_W2), (_AP_S2))      \
+  OP_ASSIGN_MIX_INT(*=, C_TYPE, (_AP_W2), (_AP_S2))      \
+  OP_ASSIGN_MIX_INT(/=, C_TYPE, (_AP_W2), (_AP_S2))      \
+  OP_ASSIGN_MIX_INT(%=, C_TYPE, (_AP_W2), (_AP_S2))      \
+  OP_ASSIGN_MIX_INT(&=, C_TYPE, (_AP_W2), (_AP_S2))      \
+  OP_ASSIGN_MIX_INT(|=, C_TYPE, (_AP_W2), (_AP_S2))      \
+  OP_ASSIGN_MIX_INT(^=, C_TYPE, (_AP_W2), (_AP_S2))      \
+  OP_ASSIGN_RSHIFT_INT(>>=, C_TYPE, (_AP_W2), (_AP_S2))  \
+  OP_ASSIGN_LSHIFT_INT(<<=, C_TYPE, (_AP_W2), (_AP_S2))  \
+                                                         \
+  OP_REL_MIX_INT(>, C_TYPE, (_AP_W2), (_AP_S2))          \
+  OP_REL_MIX_INT(<, C_TYPE, (_AP_W2), (_AP_S2))          \
+  OP_REL_MIX_INT(>=, C_TYPE, (_AP_W2), (_AP_S2))         \
+  OP_REL_MIX_INT(<=, C_TYPE, (_AP_W2), (_AP_S2))         \
+  OP_REL_MIX_INT(==, C_TYPE, (_AP_W2), (_AP_S2))         \
+  OP_REL_MIX_INT(!=, C_TYPE, (_AP_W2), (_AP_S2))
+
+OPS_MIX_INT(bool, 1, false)
+OPS_MIX_INT(char, 8, CHAR_IS_SIGNED)
+OPS_MIX_INT(signed char, 8, true)
+OPS_MIX_INT(unsigned char, 8, false)
+OPS_MIX_INT(short, sizeof(short) * 8, true)
+OPS_MIX_INT(unsigned short, sizeof(unsigned short) * 8, false)
+OPS_MIX_INT(int, sizeof(int) * 8, true)
+OPS_MIX_INT(unsigned int, sizeof(unsigned int) * 8, false)
+OPS_MIX_INT(long, sizeof(long) * 8, true)
+OPS_MIX_INT(unsigned long, sizeof(unsigned long) * 8, false)
+OPS_MIX_INT(ap_slong, sizeof(ap_slong) * 8, true)
+OPS_MIX_INT(ap_ulong, sizeof(ap_ulong) * 8, false)
+
+#undef OP_BIN_MIX_INT
+#undef OP_BIN_SHIFT_INT
+#undef OP_ASSIGN_MIX_INT
+#undef OP_ASSIGN_RSHIFT_INT
+#undef OP_ASSIGN_LSHIFT_INT
+#undef OP_REL_MIX_INT
+#undef OPS_MIX_INT
+
+#define OP_BIN_MIX_RANGE(BIN_OP, RTYPE)                                     \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>               \
+  INLINE typename ap_private<_AP_W1, _AP_S1>::template RType<_AP_W2,        \
+                                                             _AP_S2>::RTYPE \
+  operator BIN_OP(const _private_range_ref<_AP_W1, _AP_S1>& op1,            \
+                  const ap_private<_AP_W2, _AP_S2>& op2) {                  \
+    return ap_private<_AP_W1, false>(op1).operator BIN_OP(op2);             \
+  }                                                                         \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>               \
+  INLINE typename ap_private<_AP_W1, _AP_S1>::template RType<_AP_W2,        \
+                                                             _AP_S2>::RTYPE \
+  operator BIN_OP(const ap_private<_AP_W1, _AP_S1>& op1,                    \
+                  const _private_range_ref<_AP_W2, _AP_S2>& op2) {          \
+    return op1.operator BIN_OP(ap_private<_AP_W2, false>(op2));             \
+  }
+
+#define OP_ASSIGN_MIX_RANGE(ASSIGN_OP)                             \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>      \
+  INLINE ap_private<_AP_W1, _AP_S1>& operator ASSIGN_OP(           \
+      ap_private<_AP_W1, _AP_S1>& op1,                             \
+      const _private_range_ref<_AP_W2, _AP_S2>& op2) {             \
+    return op1.operator ASSIGN_OP(ap_private<_AP_W2, false>(op2)); \
+  }                                                                \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>      \
+  INLINE _private_range_ref<_AP_W1, _AP_S1>& operator ASSIGN_OP(   \
+      _private_range_ref<_AP_W1, _AP_S1>& op1,                     \
+      ap_private<_AP_W2, _AP_S2>& op2) {                           \
+    ap_private<_AP_W1, false> tmp(op1);                            \
+    tmp.operator ASSIGN_OP(op2);                                   \
+    op1 = tmp;                                                     \
+    return op1;                                                    \
+  }
+
+#define OP_REL_MIX_RANGE(REL_OP)                                               \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                  \
+  INLINE bool operator REL_OP(const _private_range_ref<_AP_W1, _AP_S1>& op1,   \
+                              const ap_private<_AP_W2, _AP_S2>& op2) {         \
+    return ap_private<_AP_W1, false>(op1).operator REL_OP(op2);                \
+  }                                                                            \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                  \
+  INLINE bool operator REL_OP(const ap_private<_AP_W1, _AP_S1>& op1,           \
+                              const _private_range_ref<_AP_W2, _AP_S2>& op2) { \
+    return op1.operator REL_OP(op2.operator ap_private<_AP_W2, false>());      \
+  }
+
+OP_BIN_MIX_RANGE(+, plus)
+OP_BIN_MIX_RANGE(-, minus)
+OP_BIN_MIX_RANGE(*, mult)
+OP_BIN_MIX_RANGE(/, div)
+OP_BIN_MIX_RANGE(%, mod)
+OP_BIN_MIX_RANGE(&, logic)
+OP_BIN_MIX_RANGE(|, logic)
+OP_BIN_MIX_RANGE(^, logic)
+OP_BIN_MIX_RANGE(>>, arg1)
+OP_BIN_MIX_RANGE(<<, arg1)
+#undef OP_BIN_MIX_RANGE
+
+OP_ASSIGN_MIX_RANGE(+=)
+OP_ASSIGN_MIX_RANGE(-=)
+OP_ASSIGN_MIX_RANGE(*=)
+OP_ASSIGN_MIX_RANGE(/=)
+OP_ASSIGN_MIX_RANGE(%=)
+OP_ASSIGN_MIX_RANGE(&=)
+OP_ASSIGN_MIX_RANGE(|=)
+OP_ASSIGN_MIX_RANGE(^=)
+OP_ASSIGN_MIX_RANGE(>>=)
+OP_ASSIGN_MIX_RANGE(<<=)
+#undef OP_ASSIGN_MIX_RANGE
+
+OP_REL_MIX_RANGE(>)
+OP_REL_MIX_RANGE(<)
+OP_REL_MIX_RANGE(>=)
+OP_REL_MIX_RANGE(<=)
+OP_REL_MIX_RANGE(==)
+OP_REL_MIX_RANGE(!=)
+#undef OP_REL_MIX_RANGE
+
+#define OP_BIN_MIX_BIT(BIN_OP, RTYPE)                                         \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                 \
+  INLINE typename ap_private<1, false>::template RType<_AP_W2, _AP_S2>::RTYPE \
+  operator BIN_OP(const _private_bit_ref<_AP_W1, _AP_S1>& op1,                \
+                  const ap_private<_AP_W2, _AP_S2>& op2) {                    \
+    return ap_private<1, false>(op1).operator BIN_OP(op2);                    \
+  }                                                                           \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                 \
+  INLINE typename ap_private<_AP_W1, _AP_S1>::template RType<1, false>::RTYPE \
+  operator BIN_OP(const ap_private<_AP_W1, _AP_S1>& op1,                      \
+                  const _private_bit_ref<_AP_W2, _AP_S2>& op2) {              \
+    return op1.operator BIN_OP(ap_private<1, false>(op2));                    \
+  }
+
+#define OP_ASSIGN_MIX_BIT(ASSIGN_OP)                           \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>  \
+  INLINE ap_private<_AP_W1, _AP_S1>& operator ASSIGN_OP(       \
+      ap_private<_AP_W1, _AP_S1>& op1,                         \
+      _private_bit_ref<_AP_W2, _AP_S2>& op2) {                 \
+    return op1.operator ASSIGN_OP(ap_private<1, false>(op2));  \
+  }                                                            \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>  \
+  INLINE _private_bit_ref<_AP_W1, _AP_S1>& operator ASSIGN_OP( \
+      _private_bit_ref<_AP_W1, _AP_S1>& op1,                   \
+      ap_private<_AP_W2, _AP_S2>& op2) {                       \
+    ap_private<1, false> tmp(op1);                             \
+    tmp.operator ASSIGN_OP(op2);                               \
+    op1 = tmp;                                                 \
+    return op1;                                                \
+  }
+
+#define OP_REL_MIX_BIT(REL_OP)                                               \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                \
+  INLINE bool operator REL_OP(const _private_bit_ref<_AP_W1, _AP_S1>& op1,   \
+                              const ap_private<_AP_W2, _AP_S2>& op2) {       \
+    return ap_private<_AP_W1, false>(op1).operator REL_OP(op2);              \
+  }                                                                          \
+  template <int _AP_W1, bool _AP_S1, int _AP_W2, bool _AP_S2>                \
+  INLINE bool operator REL_OP(const ap_private<_AP_W1, _AP_S1>& op1,         \
+                              const _private_bit_ref<_AP_W2, _AP_S2>& op2) { \
+    return op1.operator REL_OP(ap_private<1, false>(op2));                   \
+  }
+
+OP_ASSIGN_MIX_BIT(+=)
+OP_ASSIGN_MIX_BIT(-=)
+OP_ASSIGN_MIX_BIT(*=)
+OP_ASSIGN_MIX_BIT(/=)
+OP_ASSIGN_MIX_BIT(%=)
+OP_ASSIGN_MIX_BIT(&=)
+OP_ASSIGN_MIX_BIT(|=)
+OP_ASSIGN_MIX_BIT(^=)
+OP_ASSIGN_MIX_BIT(>>=)
+OP_ASSIGN_MIX_BIT(<<=)
+#undef OP_ASSIGN_MIX_BIT
+
+OP_BIN_MIX_BIT(+, plus)
+OP_BIN_MIX_BIT(-, minus)
+OP_BIN_MIX_BIT(*, mult)
+OP_BIN_MIX_BIT(/, div)
+OP_BIN_MIX_BIT(%, mod)
+OP_BIN_MIX_BIT(&, logic)
+OP_BIN_MIX_BIT(|, logic)
+OP_BIN_MIX_BIT(^, logic)
+OP_BIN_MIX_BIT(>>, arg1)
+OP_BIN_MIX_BIT(<<, arg1)
+#undef OP_BIN_MIX_BIT
+
+OP_REL_MIX_BIT(>)
+OP_REL_MIX_BIT(<)
+OP_REL_MIX_BIT(<=)
+OP_REL_MIX_BIT(>=)
+OP_REL_MIX_BIT(==)
+OP_REL_MIX_BIT(!=)
+#undef OP_REL_MIX_BIT
+
+#define REF_REL_OP_MIX_INT(REL_OP, C_TYPE, _AP_W2, _AP_S2)                  \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE bool operator REL_OP(const _private_range_ref<_AP_W, _AP_S>& op,   \
+                              C_TYPE op2) {                                 \
+    return (ap_private<_AP_W, false>(op))                                   \
+        .                                                                   \
+        operator REL_OP(ap_private<_AP_W2, _AP_S2>(op2));                   \
+  }                                                                         \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE bool operator REL_OP(C_TYPE op2,                                   \
+                              const _private_range_ref<_AP_W, _AP_S>& op) { \
+    return ap_private<_AP_W2, _AP_S2>(op2).operator REL_OP(                 \
+        ap_private<_AP_W, false>(op));                                      \
+  }                                                                         \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE bool operator REL_OP(const _private_bit_ref<_AP_W, _AP_S>& op,     \
+                              C_TYPE op2) {                                 \
+    return (bool(op))REL_OP op2;                                            \
+  }                                                                         \
+  template <int _AP_W, bool _AP_S>                                          \
+  INLINE bool operator REL_OP(C_TYPE op2,                                   \
+                              const _private_bit_ref<_AP_W, _AP_S>& op) {   \
+    return op2 REL_OP(bool(op));                                            \
+  }
+
+#define REF_REL_MIX_INT(C_TYPE, _AP_W2, _AP_S2)      \
+  REF_REL_OP_MIX_INT(>, C_TYPE, (_AP_W2), (_AP_S2))  \
+  REF_REL_OP_MIX_INT(<, C_TYPE, (_AP_W2), (_AP_S2))  \
+  REF_REL_OP_MIX_INT(>=, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_REL_OP_MIX_INT(<=, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_REL_OP_MIX_INT(==, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_REL_OP_MIX_INT(!=, C_TYPE, (_AP_W2), (_AP_S2))
+
+REF_REL_MIX_INT(bool, 1, false)
+REF_REL_MIX_INT(char, 8, CHAR_IS_SIGNED)
+REF_REL_MIX_INT(signed char, 8, true)
+REF_REL_MIX_INT(unsigned char, 8, false)
+REF_REL_MIX_INT(short, sizeof(short) * 8, true)
+REF_REL_MIX_INT(unsigned short, sizeof(unsigned short) * 8, false)
+REF_REL_MIX_INT(int, sizeof(int) * 8, true)
+REF_REL_MIX_INT(unsigned int, sizeof(unsigned int) * 8, false)
+REF_REL_MIX_INT(long, sizeof(long) * 8, true)
+REF_REL_MIX_INT(unsigned long, sizeof(unsigned long) * 8, false)
+REF_REL_MIX_INT(ap_slong, sizeof(ap_slong) * 8, true)
+REF_REL_MIX_INT(ap_ulong, sizeof(ap_ulong) * 8, false)
+#undef REF_REL_OP_MIX_INT
+#undef REF_REL_MIX_INT
+
+#define REF_BIN_OP_MIX_INT(BIN_OP, RTYPE, C_TYPE, _AP_W2, _AP_S2)              \
+  template <int _AP_W, bool _AP_S>                                             \
+  INLINE                                                                       \
+      typename ap_private<_AP_W, false>::template RType<_AP_W2, _AP_S2>::RTYPE \
+      operator BIN_OP(const _private_range_ref<_AP_W, _AP_S>& op,              \
+                      C_TYPE op2) {                                            \
+    return (ap_private<_AP_W, false>(op))                                      \
+        .                                                                      \
+        operator BIN_OP(ap_private<_AP_W2, _AP_S2>(op2));                      \
+  }                                                                            \
+  template <int _AP_W, bool _AP_S>                                             \
+  INLINE                                                                       \
+      typename ap_private<_AP_W2, _AP_S2>::template RType<_AP_W, false>::RTYPE \
+      operator BIN_OP(C_TYPE op2,                                              \
+                      const _private_range_ref<_AP_W, _AP_S>& op) {            \
+    return ap_private<_AP_W2, _AP_S2>(op2).operator BIN_OP(                    \
+        ap_private<_AP_W, false>(op));                                         \
+  }
+
+#define REF_BIN_MIX_INT(C_TYPE, _AP_W2, _AP_S2)            \
+  REF_BIN_OP_MIX_INT(+, plus, C_TYPE, (_AP_W2), (_AP_S2))  \
+  REF_BIN_OP_MIX_INT(-, minus, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_BIN_OP_MIX_INT(*, mult, C_TYPE, (_AP_W2), (_AP_S2))  \
+  REF_BIN_OP_MIX_INT(/, div, C_TYPE, (_AP_W2), (_AP_S2))   \
+  REF_BIN_OP_MIX_INT(%, mod, C_TYPE, (_AP_W2), (_AP_S2))   \
+  REF_BIN_OP_MIX_INT(&, logic, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_BIN_OP_MIX_INT(|, logic, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_BIN_OP_MIX_INT(^, logic, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_BIN_OP_MIX_INT(>>, arg1, C_TYPE, (_AP_W2), (_AP_S2)) \
+  REF_BIN_OP_MIX_INT(<<, arg1, C_TYPE, (_AP_W2), (_AP_S2))
+
+REF_BIN_MIX_INT(bool, 1, false)
+REF_BIN_MIX_INT(char, 8, CHAR_IS_SIGNED)
+REF_BIN_MIX_INT(signed char, 8, true)
+REF_BIN_MIX_INT(unsigned char, 8, false)
+REF_BIN_MIX_INT(short, sizeof(short) * 8, true)
+REF_BIN_MIX_INT(unsigned short, sizeof(unsigned short) * 8, false)
+REF_BIN_MIX_INT(int, sizeof(int) * 8, true)
+REF_BIN_MIX_INT(unsigned int, sizeof(unsigned int) * 8, false)
+REF_BIN_MIX_INT(long, sizeof(long) * 8, true)
+REF_BIN_MIX_INT(unsigned long, sizeof(unsigned long) * 8, false)
+REF_BIN_MIX_INT(ap_slong, sizeof(ap_slong) * 8, true)
+REF_BIN_MIX_INT(ap_ulong, sizeof(ap_ulong) * 8, false)
+#undef REF_BIN_OP_MIX_INT
+#undef REF_BIN_MIX_INT
+
+#define REF_BIN_OP(BIN_OP, RTYPE)                                             \
+  template <int _AP_W, bool _AP_S, int _AP_W2, bool _AP_S2>                   \
+  INLINE                                                                      \
+      typename ap_private<_AP_W, false>::template RType<_AP_W2, false>::RTYPE \
+      operator BIN_OP(const _private_range_ref<_AP_W, _AP_S>& lhs,            \
+                      const _private_range_ref<_AP_W2, _AP_S2>& rhs) {        \
+    return ap_private<_AP_W, false>(lhs).operator BIN_OP(                     \
+        ap_private<_AP_W2, false>(rhs));                                      \
+  }
+
+REF_BIN_OP(+, plus)
+REF_BIN_OP(-, minus)
+REF_BIN_OP(*, mult)
+REF_BIN_OP(/, div)
+REF_BIN_OP(%, mod)
+REF_BIN_OP(&, logic)
+REF_BIN_OP(|, logic)
+REF_BIN_OP(^, logic)
+REF_BIN_OP(>>, arg1)
+REF_BIN_OP(<<, arg1)
+#undef REF_BIN_OP
+
+//************************************************************************
+//  Implement
+//      ap_private<M+N> = ap_concat_ref<M> OP ap_concat_ref<N>
+//  for operators  +, -, *, /, %, >>, <<, &, |, ^
+//  Without these operators the operands are converted to int64 and
+//  larger results lose informations (higher order bits).
+//
+//                       operand OP
+//                      /          |
+//              left-concat        right-concat
+//                /     |           /         |
+//         <LW1,LT1>  <LW2,LT2>   <RW1,RT1>   <RW2,RT2>
+//
+//      _AP_LW1, _AP_LT1 (width and type of left-concat's left side)
+//      _AP_LW2, _AP_LT2 (width and type of left-concat's right side)
+//  Similarly for RHS of operand OP: _AP_RW1, AP_RW2, _AP_RT1, _AP_RT2
+//
+//  In Verilog 2001 result of concatenation is always unsigned even
+//  when both sides are signed.
+//************************************************************************
+
+#endif // ifndef __AP_PRIVATE_H__
+
+// -*- cpp -*-
diff --git a/model_1/hls4ml_prj/firmware/ap_types/hls_stream.h b/model_1/hls4ml_prj/firmware/ap_types/hls_stream.h
new file mode 100644
index 0000000..f516c39
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/hls_stream.h
@@ -0,0 +1,263 @@
+/*
+#-  (c) Copyright 2011-2018 Xilinx, Inc. All rights reserved.
+#-
+#-  This file contains confidential and proprietary information
+#-  of Xilinx, Inc. and is protected under U.S. and
+#-  international copyright and other intellectual property
+#-  laws.
+#-
+#-  DISCLAIMER
+#-  This disclaimer is not a license and does not grant any
+#-  rights to the materials distributed herewith. Except as
+#-  otherwise provided in a valid license issued to you by
+#-  Xilinx, and to the maximum extent permitted by applicable
+#-  law: (1) THESE MATERIALS ARE MADE AVAILABLE "AS IS" AND
+#-  WITH ALL FAULTS, AND XILINX HEREBY DISCLAIMS ALL WARRANTIES
+#-  AND CONDITIONS, EXPRESS, IMPLIED, OR STATUTORY, INCLUDING
+#-  BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, NON-
+#-  INFRINGEMENT, OR FITNESS FOR ANY PARTICULAR PURPOSE; and
+#-  (2) Xilinx shall not be liable (whether in contract or tort,
+#-  including negligence, or under any other theory of
+#-  liability) for any loss or damage of any kind or nature
+#-  related to, arising under or in connection with these
+#-  materials, including for any direct, or any indirect,
+#-  special, incidental, or consequential loss or damage
+#-  (including loss of data, profits, goodwill, or any type of
+#-  loss or damage suffered as a result of any action brought
+#-  by a third party) even if such damage or loss was
+#-  reasonably foreseeable or Xilinx had been advised of the
+#-  possibility of the same.
+#-
+#-  CRITICAL APPLICATIONS
+#-  Xilinx products are not designed or intended to be fail-
+#-  safe, or for use in any application requiring fail-safe
+#-  performance, such as life-support or safety devices or
+#-  systems, Class III medical devices, nuclear facilities,
+#-  applications related to the deployment of airbags, or any
+#-  other applications that could lead to death, personal
+#-  injury, or severe property or environmental damage
+#-  (individually and collectively, "Critical
+#-  Applications"). Customer assumes the sole risk and
+#-  liability of any use of Xilinx products in Critical
+#-  Applications, subject only to applicable laws and
+#-  regulations governing limitations on product liability.
+#-
+#-  THIS COPYRIGHT NOTICE AND DISCLAIMER MUST BE RETAINED AS
+#-  PART OF THIS FILE AT ALL TIMES. 
+#- ************************************************************************
+
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+*/
+
+#ifndef X_HLS_STREAM_SIM_H
+#define X_HLS_STREAM_SIM_H
+
+/*
+ * This file contains a C++ model of hls::stream.
+ * It defines C simulation model.
+ */
+#ifndef __cplusplus
+
+#error C++ is required to include this header file
+
+#else
+
+//////////////////////////////////////////////
+// C level simulation models for hls::stream
+//////////////////////////////////////////////
+#include <queue>
+#include <iostream>
+#include <typeinfo>
+#include <string>
+#include <sstream>
+
+#ifdef HLS_STREAM_THREAD_SAFE
+#include <mutex>
+#include <condition_variable>
+#endif
+
+#ifndef _MSC_VER
+#include <cxxabi.h>
+#include <stdlib.h>
+#endif
+
+namespace hls {
+
+template<typename __STREAM_T__>
+class stream
+{
+  protected:
+    std::string _name;
+    std::deque<__STREAM_T__> _data; // container for the elements
+#ifdef HLS_STREAM_THREAD_SAFE
+    std::mutex _mutex;
+    std::condition_variable _condition_var;
+#endif    
+
+  public:
+    /// Constructors
+    // Keep consistent with the synthesis model's constructors
+    stream() {
+        static unsigned _counter = 1;
+        std::stringstream ss;
+#ifndef _MSC_VER
+        char* _demangle_name = abi::__cxa_demangle(typeid(*this).name(), 0, 0, 0);
+        if (_demangle_name) {
+            _name = _demangle_name;
+            free(_demangle_name);
+        }
+        else {
+            _name = "hls_stream";
+        }
+#else
+        _name = typeid(*this).name();
+#endif
+
+        ss << _counter++;
+        _name += "." + ss.str();
+    }
+
+    stream(const std::string name) {
+    // default constructor,
+    // capacity set to predefined maximum
+        _name = name;
+    }
+
+  /// Make copy constructor and assignment operator private
+  private:
+    stream(const stream< __STREAM_T__ >& chn):
+        _name(chn._name), _data(chn._data) {
+    }
+
+    stream& operator = (const stream< __STREAM_T__ >& chn) {
+        _name = chn._name;
+        _data = chn._data;
+        return *this;
+    }
+
+  public:
+    /// Overload >> and << operators to implement read() and write()
+    void operator >> (__STREAM_T__& rdata) {
+        read(rdata);
+    }
+
+    void operator << (const __STREAM_T__& wdata) {
+        write(wdata);
+    }
+
+
+  public:
+    /// Destructor
+    /// Check status of the queue
+    virtual ~stream() {
+        if (!_data.empty())
+        {
+            std::cout << "WARNING: Hls::stream '" 
+                      << _name 
+                      << "' contains leftover data,"
+                      << " which may result in RTL simulation hanging."
+                      << std::endl;
+        }
+    }
+
+    /// Status of the queue
+    bool empty() {
+#ifdef HLS_STREAM_THREAD_SAFE
+        std::lock_guard<std::mutex> lg(_mutex);
+#endif
+        return _data.empty();
+    }    
+
+    bool full() const { return false; }
+
+    /// Blocking read
+    void read(__STREAM_T__& head) {
+        head = read();
+    }
+
+#ifdef HLS_STREAM_THREAD_SAFE
+    __STREAM_T__ read() {
+        std::unique_lock<std::mutex> ul(_mutex);
+        while (_data.empty()) {
+            _condition_var.wait(ul);
+        }
+
+        __STREAM_T__ elem;
+        elem = _data.front();
+        _data.pop_front();
+        return elem;
+    }
+#else
+    __STREAM_T__ read() {
+        __STREAM_T__ elem;
+        if (_data.empty()) {
+            std::cout << "WARNING: Hls::stream '"
+                      << _name 
+                      << "' is read while empty,"
+                      << " which may result in RTL simulation hanging."
+                      << std::endl;
+            elem = __STREAM_T__();
+        } else {
+            elem = _data.front();
+            _data.pop_front();
+        }
+        return elem;
+    }
+#endif
+
+    /// Blocking write
+    void write(const __STREAM_T__& tail) { 
+#ifdef HLS_STREAM_THREAD_SAFE
+        std::unique_lock<std::mutex> ul(_mutex);
+#endif
+        _data.push_back(tail);
+#ifdef HLS_STREAM_THREAD_SAFE
+        _condition_var.notify_one();
+#endif
+    }
+
+    /// Nonblocking read
+    bool read_nb(__STREAM_T__& head) {
+#ifdef HLS_STREAM_THREAD_SAFE
+        std::lock_guard<std::mutex> lg(_mutex);
+#endif    
+        bool is_empty = _data.empty();
+        if (is_empty) {
+            head = __STREAM_T__();
+        } else {
+            __STREAM_T__ elem(_data.front());
+            _data.pop_front();
+            head = elem;
+        }
+        return !is_empty;
+    }
+
+    /// Nonblocking write
+    bool write_nb(const __STREAM_T__& tail) {
+        bool is_full = full();
+        write(tail);
+        return !is_full;
+    }
+
+    /// Fifo size
+    size_t size() {
+        return _data.size();
+    }
+};
+
+} // namespace hls
+
+#endif // __cplusplus
+#endif  // X_HLS_STREAM_SIM_H
+
diff --git a/model_1/hls4ml_prj/firmware/ap_types/utils/x_hls_utils.h b/model_1/hls4ml_prj/firmware/ap_types/utils/x_hls_utils.h
new file mode 100644
index 0000000..3e751c3
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/ap_types/utils/x_hls_utils.h
@@ -0,0 +1,80 @@
+#ifndef X_HLS_UTILS_H
+#define X_HLS_UTILS_H
+#include "ap_fixed.h"
+#include <limits>
+
+namespace hls {
+
+    template<typename T>
+    class numeric_limits {
+    public:
+        static T max()     { return std::numeric_limits<T>::max(); }
+        static T min()     { return std::numeric_limits<T>::min(); }
+        static T epsilon() { return std::numeric_limits<T>::epsilon(); }
+    };
+
+    template <int W, int I, ap_q_mode Q, ap_o_mode O>
+    class numeric_limits<ap_fixed<W,I,Q,O> > {
+    public:
+        static ap_fixed<W,I,Q,O> max() {
+            ap_int<W> m = ::hls::numeric_limits<ap_int<W> >::max();
+            ap_fixed<W,I,Q,O> x;
+            x(W-1,0) = m(W-1,0);
+            return x;
+        }
+        static ap_fixed<W,I,Q,O> min() {
+            ap_int<W> m = ::hls::numeric_limits<ap_int<W> >::min();
+            ap_fixed<W,I,Q,O> x;
+            x(W-1,0) = m(W-1,0);
+            return x;
+        }
+        static ap_fixed<W,I,Q,O> epsilon() {
+          ap_fixed<W,I,Q,O> x = 0;
+          x[0] = 1;
+          return x;
+        }
+    };
+
+    template <int W, int I, ap_q_mode Q, ap_o_mode O>
+    class numeric_limits<ap_ufixed<W,I,Q,O> > {
+    public:
+        static ap_ufixed<W,I,Q,O> max() {
+            ap_uint<W> m = ::hls::numeric_limits<ap_uint<W> >::max();
+            ap_ufixed<W,I,Q,O> x;
+            x(W-1,0) = m(W-1,0);
+            return x;
+        }
+        static ap_ufixed<W,I,Q,O> min() { return 0; }
+        static ap_ufixed<W,I,Q,O> epsilon() {
+          ap_ufixed<W,I,Q,O> x = 0;
+          x[0] = 1;
+          return x;
+        }
+    };
+
+    template <int W>
+    class numeric_limits<ap_int<W> > {
+    public:
+        static ap_int<W> max() { ap_int<W> m = min(); return ~m; }
+        static ap_int<W> min() { ap_int<W> m = 0; m[W-1] = 1; return m; }
+        static ap_int<W> epsilon() {
+          ap_int<W> x = 0;
+          x[0] = 1;
+          return x;
+        }
+    };
+
+    template <int W>
+    class numeric_limits<ap_uint<W> > {
+    public:
+        static ap_uint<W> max() { ap_uint<W> zero = 0; return ~zero; }
+        static ap_uint<W> min() { return 0; }
+        static ap_uint<W> epsilon() {
+          ap_uint<W> x = 0;
+          x[0] = 1;
+          return x;
+        }
+    };
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/defines.h b/model_1/hls4ml_prj/firmware/defines.h
new file mode 100644
index 0000000..30a2e38
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/defines.h
@@ -0,0 +1,35 @@
+#ifndef DEFINES_H_
+#define DEFINES_H_
+
+#include "ap_int.h"
+#include "ap_fixed.h"
+#include "nnet_utils/nnet_types.h"
+#include <cstddef>
+#include <cstdio>
+
+//hls-fpga-machine-learning insert numbers
+#define N_INPUT_1_1 784
+#define N_LAYER_2 128
+#define N_LAYER_4 64
+#define N_LAYER_6 2
+#define N_LAYER_8 64
+#define N_LAYER_10 128
+#define N_LAYER_12 784
+
+//hls-fpga-machine-learning insert layer-precision
+typedef ap_fixed<16,6> model_default_t;
+typedef ap_fixed<16,6> input_t;
+typedef ap_fixed<16,6> layer2_t;
+typedef ap_fixed<16,6> layer3_t;
+typedef ap_fixed<16,6> layer4_t;
+typedef ap_fixed<16,6> layer5_t;
+typedef ap_fixed<16,6> layer6_t;
+typedef ap_fixed<16,6> layer7_t;
+typedef ap_fixed<16,6> layer8_t;
+typedef ap_fixed<16,6> layer9_t;
+typedef ap_fixed<16,6> layer10_t;
+typedef ap_fixed<16,6> layer11_t;
+typedef ap_fixed<16,6> layer12_t;
+typedef ap_fixed<16,6> result_t;
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/myproject.cpp b/model_1/hls4ml_prj/firmware/myproject.cpp
new file mode 100644
index 0000000..f1d9a1a
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/myproject.cpp
@@ -0,0 +1,112 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+#include <iostream>
+
+#include "myproject.h"
+#include "parameters.h"
+
+void myproject(
+    input_t encoder_input[N_INPUT_1_1],
+    result_t layer13_out[N_LAYER_12],
+    unsigned short &const_size_in_1,
+    unsigned short &const_size_out_1
+) {
+
+    //hls-fpga-machine-learning insert IO
+    #pragma HLS ARRAY_RESHAPE variable=encoder_input complete dim=0
+    #pragma HLS ARRAY_PARTITION variable=layer13_out complete dim=0
+    #pragma HLS INTERFACE ap_vld port=encoder_input,layer13_out 
+    #pragma HLS PIPELINE 
+
+    const_size_in_1 = N_INPUT_1_1;
+    const_size_out_1 = N_LAYER_12;
+
+#ifndef __SYNTHESIS__
+    static bool loaded_weights = false;
+    if (!loaded_weights) {
+        //hls-fpga-machine-learning insert load weights
+        nnet::load_weights_from_txt<model_default_t, 100352>(w2, "w2.txt");
+        nnet::load_weights_from_txt<model_default_t, 128>(b2, "b2.txt");
+        nnet::load_weights_from_txt<model_default_t, 8192>(w4, "w4.txt");
+        nnet::load_weights_from_txt<model_default_t, 64>(b4, "b4.txt");
+        nnet::load_weights_from_txt<model_default_t, 128>(w6, "w6.txt");
+        nnet::load_weights_from_txt<model_default_t, 2>(b6, "b6.txt");
+        nnet::load_weights_from_txt<model_default_t, 128>(w8, "w8.txt");
+        nnet::load_weights_from_txt<model_default_t, 64>(b8, "b8.txt");
+        nnet::load_weights_from_txt<model_default_t, 8192>(w10, "w10.txt");
+        nnet::load_weights_from_txt<model_default_t, 128>(b10, "b10.txt");
+        nnet::load_weights_from_txt<model_default_t, 100352>(w12, "w12.txt");
+        nnet::load_weights_from_txt<model_default_t, 784>(b12, "b12.txt");
+        loaded_weights = true;
+    }
+#endif
+
+    // ****************************************
+    // NETWORK INSTANTIATION
+    // ****************************************
+
+    //hls-fpga-machine-learning insert layers
+
+    layer2_t layer2_out[N_LAYER_2];
+    #pragma HLS ARRAY_PARTITION variable=layer2_out complete dim=0
+    nnet::dense<input_t, layer2_t, config2>(encoder_input, layer2_out, w2, b2); // dense
+
+    layer3_t layer3_out[N_LAYER_2];
+    #pragma HLS ARRAY_PARTITION variable=layer3_out complete dim=0
+    nnet::relu<layer2_t, layer3_t, relu_config3>(layer2_out, layer3_out); // dense_relu
+
+    layer4_t layer4_out[N_LAYER_4];
+    #pragma HLS ARRAY_PARTITION variable=layer4_out complete dim=0
+    nnet::dense<layer3_t, layer4_t, config4>(layer3_out, layer4_out, w4, b4); // dense_1
+
+    layer5_t layer5_out[N_LAYER_4];
+    #pragma HLS ARRAY_PARTITION variable=layer5_out complete dim=0
+    nnet::relu<layer4_t, layer5_t, relu_config5>(layer4_out, layer5_out); // dense_1_relu
+
+    layer6_t layer6_out[N_LAYER_6];
+    #pragma HLS ARRAY_PARTITION variable=layer6_out complete dim=0
+    nnet::dense<layer5_t, layer6_t, config6>(layer5_out, layer6_out, w6, b6); // encoder_output
+
+    layer7_t layer7_out[N_LAYER_6];
+    #pragma HLS ARRAY_PARTITION variable=layer7_out complete dim=0
+    nnet::relu<layer6_t, layer7_t, relu_config7>(layer6_out, layer7_out); // encoder_output_relu
+
+    layer8_t layer8_out[N_LAYER_8];
+    #pragma HLS ARRAY_PARTITION variable=layer8_out complete dim=0
+    nnet::dense<layer7_t, layer8_t, config8>(layer7_out, layer8_out, w8, b8); // dense_2
+
+    layer9_t layer9_out[N_LAYER_8];
+    #pragma HLS ARRAY_PARTITION variable=layer9_out complete dim=0
+    nnet::relu<layer8_t, layer9_t, relu_config9>(layer8_out, layer9_out); // dense_2_relu
+
+    layer10_t layer10_out[N_LAYER_10];
+    #pragma HLS ARRAY_PARTITION variable=layer10_out complete dim=0
+    nnet::dense<layer9_t, layer10_t, config10>(layer9_out, layer10_out, w10, b10); // dense_3
+
+    layer11_t layer11_out[N_LAYER_10];
+    #pragma HLS ARRAY_PARTITION variable=layer11_out complete dim=0
+    nnet::relu<layer10_t, layer11_t, relu_config11>(layer10_out, layer11_out); // dense_3_relu
+
+    layer12_t layer12_out[N_LAYER_12];
+    #pragma HLS ARRAY_PARTITION variable=layer12_out complete dim=0
+    nnet::dense<layer11_t, layer12_t, config12>(layer11_out, layer12_out, w12, b12); // ecoder_output
+
+    nnet::sigmoid<layer12_t, result_t, sigmoid_config13>(layer12_out, layer13_out); // ecoder_output_sigmoid
+
+}
diff --git a/model_1/hls4ml_prj/firmware/myproject.h b/model_1/hls4ml_prj/firmware/myproject.h
new file mode 100644
index 0000000..7c6a081
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/myproject.h
@@ -0,0 +1,37 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef MYPROJECT_H_
+#define MYPROJECT_H_
+
+#include "ap_int.h"
+#include "ap_fixed.h"
+#include "hls_stream.h"
+
+#include "defines.h"
+
+// Prototype of top level function for C-synthesis
+void myproject(
+    input_t encoder_input[N_INPUT_1_1],
+    result_t layer13_out[N_LAYER_12],
+    unsigned short &const_size_in_1,
+    unsigned short &const_size_out_1
+);
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_activation.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_activation.h
new file mode 100644
index 0000000..83f73ad
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_activation.h
@@ -0,0 +1,861 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_ACTIVATION_H_
+#define NNET_ACTIVATION_H_
+
+#include <cmath>
+#include "ap_fixed.h"
+#include "nnet_common.h"
+
+namespace nnet {
+
+struct activ_config
+{
+    // IO size
+    static const unsigned n_in = 10;
+
+    // Internal info
+    static const unsigned table_size = 1024;
+
+    // Resource reuse info
+    static const unsigned io_type = io_parallel;
+    static const unsigned reuse_factor = 1;
+
+    // Internal data type definitions
+    typedef ap_fixed<18,8> table_t;
+};
+
+// *************************************************
+//       LINEAR Activation -- See Issue 53
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void  linear(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        res[ii] = data[ii];
+    }
+}
+
+
+
+// *************************************************
+//       RELU Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void  relu(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    data_T datareg;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];
+        if (datareg > 0) res[ii] = datareg;
+        else res[ii] = 0;
+    }
+}
+
+template<class data_T, class res_T, int MAX_INT, typename CONFIG_T>
+void  relu_max(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    data_T datareg;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];
+        if (datareg < 0) res[ii] = 0;
+        else if (datareg > MAX_INT) res[ii] = MAX_INT;
+        else res[ii] = datareg;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  relu6(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    relu_max<data_T, res_T, 6, CONFIG_T>(data, res);
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  relu1(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    relu_max<data_T, res_T, 1, CONFIG_T>(data, res);
+}
+
+// *************************************************
+//       Sigmoid Activation
+// *************************************************
+inline float sigmoid_fcn_float(float input) {
+    return 1.0 / (1 + std::exp(-input));
+}
+
+template<typename CONFIG_T, int N_TABLE>
+void init_sigmoid_table(typename CONFIG_T::table_t table_out[N_TABLE])
+{
+    // Default logistic sigmoid function:
+    //   result = 1/(1+e^(-x))
+    for (int ii = 0; ii < N_TABLE; ii++) {
+        // First, convert from table index to X-value (signed 8-bit, range -8 to +8)
+        float in_val = 2*8.0*(ii-float(N_TABLE)/2.0)/float(N_TABLE);
+        // Next, compute lookup table function
+        typename CONFIG_T::table_t real_val = sigmoid_fcn_float(in_val);
+        //std::cout << "Lookup table In Value: " << in_val << " Result: " << real_val << std::endl;
+        table_out[ii] = real_val;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  sigmoid(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t sigmoid_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t sigmoid_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_sigmoid_table<CONFIG_T, CONFIG_T::table_size>(sigmoid_table);
+        initialized = true;
+    }
+
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    // Index into the lookup table based on data
+    int data_round;
+    int index;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        data_round = data[ii]*CONFIG_T::table_size/16;
+        index = data_round + 8*CONFIG_T::table_size/16;
+        if (index < 0)   index = 0;
+        if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+        res[ii] = (res_T) sigmoid_table[index];
+    }
+}
+
+// *************************************************
+//       Softmax Activation
+// *************************************************
+
+enum class softmax_implementation {latency=0, legacy=1, stable=2};
+
+inline float exp_fcn_float(float input) {
+    return std::exp(input);
+}
+
+template<class data_T, typename CONFIG_T>
+inline float softmax_real_val_from_idx(unsigned i){
+    // Treat the index as the top N bits
+    static constexpr int N = ceillog2(CONFIG_T::table_size); // number of address bits for table
+    data_T x(0);
+    x(x.width-1, x.width-N) = i;
+    return (float) x;
+}
+
+template<class data_T, typename CONFIG_T>
+inline unsigned softmax_idx_from_real_val(data_T x){
+    // Slice the top N bits to get an index into the table
+    static constexpr int N = ceillog2(CONFIG_T::table_size); // number of address bits for table
+    ap_uint<N> y = x(x.width-1, x.width-N); // slice the top N bits of input
+    return (unsigned) y(N-1, 0);
+}
+
+template<class data_T, typename CONFIG_T>
+void init_exp_table(typename CONFIG_T::exp_table_t table_out[CONFIG_T::table_size]){
+    // The template data_T is the data type used to address the table
+    for(unsigned i = 0; i < CONFIG_T::table_size; i++){
+        // Slicing bits for address is going to round towards 0, so take the central value
+        float x = softmax_real_val_from_idx<data_T, CONFIG_T>(i);
+        typename CONFIG_T::exp_table_t exp_x = exp_fcn_float(x);
+        table_out[i] = exp_x;
+    }
+}
+
+template<class data_T, typename CONFIG_T>
+void init_invert_table(typename CONFIG_T::inv_table_t table_out[CONFIG_T::table_size]){
+    // The template data_T is the data type used to address the table
+    for(unsigned i = 0; i < CONFIG_T::table_size; i++){
+        float x = softmax_real_val_from_idx<data_T, CONFIG_T>(i);
+        typename CONFIG_T::inv_table_t inv_x = 1 / x;
+        table_out[i] = inv_x;
+    }
+}
+
+template <class data_T, class res_T, typename CONFIG_T>
+void softmax_latency(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in]){
+    #pragma HLS pipeline
+    // Initialize the lookup tables
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::exp_table_t exp_table[CONFIG_T::table_size];
+    typename CONFIG_T::inv_table_t invert_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::exp_table_t exp_table[CONFIG_T::table_size];
+    static typename CONFIG_T::inv_table_t invert_table[CONFIG_T::table_size];
+
+#endif
+    if (!initialized) {
+        // Note we are exponentiating the inputs, which have type data_T
+        init_exp_table<data_T, CONFIG_T>(exp_table);
+        // Note we are inverting the exponentials, which have type exp_table_t
+        init_invert_table<typename CONFIG_T::exp_table_t, CONFIG_T>(invert_table);
+        initialized = true;
+    }
+
+    // Calculate all the e^x's
+    typename CONFIG_T::exp_table_t exp_res[CONFIG_T::n_in];
+    #pragma HLS array_partition variable=exp_res complete
+    typename CONFIG_T::exp_table_t exp_sum(0);
+    for(unsigned i = 0; i < CONFIG_T::n_in; i++){
+        #pragma HLS unroll
+        unsigned x = softmax_idx_from_real_val<data_T, CONFIG_T>(data[i]);
+        exp_res[i] = exp_table[x];
+    }
+
+    // Explicitly sum the results with an adder tree.
+    // Rounding & Saturation mode, which improve accuracy, prevent Vivado from expression balancing
+    Op_add<typename CONFIG_T::exp_table_t> op_add;
+    exp_sum = reduce<typename CONFIG_T::exp_table_t, CONFIG_T::n_in, Op_add<typename CONFIG_T::exp_table_t>>(exp_res, op_add);
+
+    typename CONFIG_T::inv_table_t inv_exp_sum = invert_table[softmax_idx_from_real_val<typename CONFIG_T::exp_table_t,CONFIG_T>(exp_sum)];
+    for(unsigned i = 0; i < CONFIG_T::n_in; i++){
+        #pragma HLS unroll
+        res[i] = exp_res[i] * inv_exp_sum;
+    }
+}
+
+template <class data_T, class res_T, typename CONFIG_T>
+void softmax_stable(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in]){
+    #pragma HLS pipeline
+    // Initialize the lookup tables
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::exp_table_t exp_table[CONFIG_T::table_size];
+    typename CONFIG_T::inv_table_t invert_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::exp_table_t exp_table[CONFIG_T::table_size];
+    static typename CONFIG_T::inv_table_t invert_table[CONFIG_T::table_size];
+
+#endif
+    if (!initialized) {
+        // Note we are exponentiating the inputs, which have type data_T
+        init_exp_table<data_T, CONFIG_T>(exp_table);
+        // Note we are inverting the exponentials, which have type exp_table_t
+        init_invert_table<typename CONFIG_T::exp_table_t, CONFIG_T>(invert_table);
+        initialized = true;
+    }
+
+    // Find the max and compute all delta(x_i, x_max)
+    Op_max<data_T> op_max;
+    data_T x_max = reduce<data_T, CONFIG_T::n_in, Op_max<data_T>>(data, op_max);
+
+    // For the diffs, use the same type as the input but force rounding and saturation
+    ap_fixed<data_T::width, data_T::iwidth,AP_RND,AP_SAT> d_xi_xmax[CONFIG_T::n_in];
+    for(unsigned i = 0; i < CONFIG_T::n_in; i++){
+        #pragma HLS unroll
+        d_xi_xmax[i] = data[i] - x_max;
+    }
+
+    // Calculate all the e^x's
+    typename CONFIG_T::exp_table_t exp_res[CONFIG_T::n_in];
+    #pragma HLS array_partition variable=exp_res complete
+    typename CONFIG_T::exp_table_t exp_sum(0);
+    for(unsigned i = 0; i < CONFIG_T::n_in; i++){
+        #pragma HLS unroll
+        unsigned x = softmax_idx_from_real_val<data_T, CONFIG_T>(d_xi_xmax[i]);
+        exp_res[i] = exp_table[x];
+    }
+
+    // Explicitly sum the results with an adder tree.
+    // Rounding & Saturation mode, which improve accuracy, prevent Vivado from expression balancing
+    Op_add<typename CONFIG_T::exp_table_t> op_add;
+    exp_sum = reduce<typename CONFIG_T::exp_table_t, CONFIG_T::n_in, Op_add<typename CONFIG_T::exp_table_t>>(exp_res, op_add);
+
+    typename CONFIG_T::inv_table_t inv_exp_sum = invert_table[softmax_idx_from_real_val<typename CONFIG_T::exp_table_t,CONFIG_T>(exp_sum)];
+    for(unsigned i = 0; i < CONFIG_T::n_in; i++){
+        #pragma HLS unroll
+        res[i] = exp_res[i] * inv_exp_sum;
+    }
+}
+
+template<typename CONFIG_T, int N_TABLE>
+void init_exp_table_legacy(typename CONFIG_T::table_t table_out[N_TABLE])
+{
+    for (int ii = 0; ii < N_TABLE; ii++) {
+        // First, convert from table index to X-value (signed 8-bit, range -8 to +8)
+        float in_val = 2*8.0*(ii-float(N_TABLE)/2.0)/float(N_TABLE);
+        // Next, compute lookup table function
+        typename CONFIG_T::table_t real_val = exp_fcn_float(in_val);
+        //std::cout << "Lookup table In Value: " << in_val << " Result: " << real_val << std::endl;
+        table_out[ii] = real_val;
+    }
+}
+
+template<typename CONFIG_T, int N_TABLE>
+void init_invert_table_legacy(typename CONFIG_T::table_t table_out[N_TABLE])
+{
+    // Inversion function:
+    //   result = 1/x
+    for (int ii = 0; ii < N_TABLE; ii++) {
+        // First, convert from table index to X-value (signed 8-bit, range 0 to +64)
+        float in_val = 64.0*ii/float(N_TABLE);
+        // Next, compute lookup table function
+        if (in_val > 0.0) table_out[ii] = 1.0/in_val;
+        else table_out[ii] = 0.0;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  softmax_legacy(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t exp_table[CONFIG_T::table_size];
+    typename CONFIG_T::table_t invert_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t exp_table[CONFIG_T::table_size];
+    static typename CONFIG_T::table_t invert_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_exp_table_legacy<CONFIG_T, CONFIG_T::table_size>(exp_table);
+        init_invert_table_legacy<CONFIG_T, CONFIG_T::table_size>(invert_table);
+        initialized = true;
+    }
+
+    if (CONFIG_T::io_type == io_parallel){
+        // Note: This is going to be a resource hog to run with pipeline, but hey, whatever
+        #pragma HLS PIPELINE
+    }
+
+    // Index into the lookup table based on data for exponentials
+    typename CONFIG_T::table_t exp_res[CONFIG_T::n_in];// different, independent, fixed point precision
+    typename CONFIG_T::table_t exp_diff_res;// different, independent, fixed point precision
+    data_T data_cache[CONFIG_T::n_in];
+    int data_round;
+    int index;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        data_cache[ii] = data[ii];
+        exp_res[ii] = 0;
+    }
+
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial) {
+            #pragma HLS PIPELINE
+        }
+        for (int jj=0; jj<CONFIG_T::n_in; jj++) {
+            if (ii==jj) exp_diff_res = 1;
+            else {
+                data_round = (data_cache[jj]-data_cache[ii])*CONFIG_T::table_size/16;
+                index = data_round + 8*CONFIG_T::table_size/16;
+                if (index < 0)   index = 0;
+                if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+                exp_diff_res = exp_table[index];
+            }
+            exp_res[ii] += exp_diff_res;
+        }
+    }
+
+    //Second loop to invert
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        int exp_res_index = exp_res[ii]*CONFIG_T::table_size/64;
+        if (exp_res_index < 0)   exp_res_index = 0;
+        if (exp_res_index > CONFIG_T::table_size-1) exp_res_index = CONFIG_T::table_size-1;
+        //typename CONFIG_T::table_t exp_res_invert = invert_table[exp_res_index];
+        res[ii] = (res_T) invert_table[exp_res_index];
+    }
+
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void softmax(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in]){
+    #pragma HLS inline
+    switch(CONFIG_T::implementation){
+    case softmax_implementation::latency:
+        softmax_latency<data_T, res_T, CONFIG_T>(data, res);
+        break;
+    case softmax_implementation::stable:
+        softmax_stable<data_T, res_T, CONFIG_T>(data, res);
+        break;
+    case softmax_implementation::legacy:
+        softmax_legacy<data_T, res_T, CONFIG_T>(data, res);
+        break;
+    }
+}
+
+// *************************************************
+//       TanH Activation
+// *************************************************
+template<typename CONFIG_T, int N_TABLE>
+void init_tanh_table(typename CONFIG_T::table_t table_out[N_TABLE])
+{
+    // Implement tanh lookup
+    for (int ii = 0; ii < N_TABLE; ii++) {
+        // First, convert from table index to X-value (signed 8-bit, range -4 to +4)
+        float in_val = 2*4.0*(ii-float(N_TABLE)/2.0)/float(N_TABLE);
+        // Next, compute lookup table function
+        typename CONFIG_T::table_t real_val = tanh(in_val);
+        //std::cout << "Tanh:  Lookup table Index: " <<  ii<< " In Value: " << in_val << " Result: " << real_val << std::endl;
+        table_out[ii] = real_val;
+    }
+}
+
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  tanh(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t tanh_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t tanh_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_tanh_table<CONFIG_T, CONFIG_T::table_size>(tanh_table);
+        initialized = true;
+    }
+
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    // Index into the lookup table based on data
+    int data_round;
+    int index;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        data_round = data[ii]*CONFIG_T::table_size/8;
+        index = data_round + 4*CONFIG_T::table_size/8;
+        //std::cout << "Input: "  << data[ii] << " Round: " << data_round << " Index: " << index << std::endl;
+        if (index < 0)   index = 0;
+        if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+        res[ii] = (res_T) tanh_table[index];
+    }
+}
+
+// *************************************************
+//       Hard sigmoid Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void  hard_sigmoid(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    data_T datareg;
+    data_T slope = (data_T) 0.2;
+    data_T shift = (data_T) 0.5;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = slope * data[ii] + shift;
+        if (datareg > 1) datareg = 1;
+        else if (datareg < 0) datareg = 0;
+        res[ii] = datareg;
+    }
+}
+
+// *************************************************
+//       Leaky RELU Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void  leaky_relu(data_T data[CONFIG_T::n_in], data_T alpha, res_T res[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    data_T datareg;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];
+        if (datareg > 0) res[ii] = datareg;
+        else res[ii] = alpha * datareg;
+    }
+}
+
+// *************************************************
+//       Thresholded RELU Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void  thresholded_relu(data_T data[CONFIG_T::n_in], data_T theta, res_T res[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    data_T datareg;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];
+        if (datareg > theta) res[ii] = datareg;
+        else res[ii] = 0;
+    }
+}
+
+// *************************************************
+//       Softplus Activation
+// *************************************************
+inline float softplus_fcn_float(float input) {
+    return std::log(std::exp(input) + 1.);
+}
+
+template<typename CONFIG_T, int N_TABLE>
+void init_softplus_table(typename CONFIG_T::table_t table_out[N_TABLE])
+{
+    // Default softplus function:
+    //   result = log(exp(x) + 1)
+    for (int ii = 0; ii < N_TABLE; ii++) {
+        // First, convert from table index to X-value (signed 8-bit, range -8 to +8)
+        float in_val = 2*8.0*(ii-float(N_TABLE)/2.0)/float(N_TABLE);
+        // Next, compute lookup table function
+        typename CONFIG_T::table_t real_val = softplus_fcn_float(in_val);
+        //std::cout << "Lookup table In Value: " << in_val << " Result: " << real_val << std::endl;
+        table_out[ii] = real_val;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  softplus(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t softplus_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t softplus_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_softplus_table<CONFIG_T, CONFIG_T::table_size>(softplus_table);
+        initialized = true;
+    }
+
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    // Index into the lookup table based on data
+    int data_round;
+    int index;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        data_round = data[ii]*CONFIG_T::table_size/16;
+        index = data_round + 8*CONFIG_T::table_size/16;
+        if (index < 0)   index = 0;
+        if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+        res[ii] = (res_T) softplus_table[index];
+    }
+}
+
+// *************************************************
+//       Softsign Activation
+// *************************************************
+inline float softsign_fcn_float(float input) {
+    return input / (std::abs(input) + 1.);
+}
+
+template<typename CONFIG_T, int N_TABLE>
+void init_softsign_table(typename CONFIG_T::table_t table_out[N_TABLE])
+{
+    // Default softsign function:
+    //   result = x / (abs(x) + 1)
+    for (int ii = 0; ii < N_TABLE; ii++) {
+        // First, convert from table index to X-value (signed 8-bit, range -8 to +8)
+        float in_val = 2*8.0*(ii-float(N_TABLE)/2.0)/float(N_TABLE);
+        // Next, compute lookup table function
+        typename CONFIG_T::table_t real_val = softsign_fcn_float(in_val);
+        //std::cout << "Lookup table In Value: " << in_val << " Result: " << real_val << std::endl;
+        table_out[ii] = real_val;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  softsign(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t softsign_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t softsign_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_softsign_table<CONFIG_T, CONFIG_T::table_size>(softsign_table);
+        initialized = true;
+    }
+
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    // Index into the lookup table based on data
+    int data_round;
+    int index;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        data_round = data[ii]*CONFIG_T::table_size/16;
+        index = data_round + 8*CONFIG_T::table_size/16;
+        if (index < 0)   index = 0;
+        if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+        res[ii] = (res_T) softsign_table[index];
+    }
+}
+
+// *************************************************
+//       ELU Activation
+// *************************************************
+inline float elu_fcn_float(float input) {
+    return std::exp(input) - 1.;
+}
+
+template<typename CONFIG_T, int N_TABLE>
+void init_elu_table(typename CONFIG_T::table_t table_out[N_TABLE])
+{
+    // Default ELU function:
+    //   result = alpha * (e^(x) - 1)
+    for (int ii = 0; ii < N_TABLE; ii++) {
+        // First, convert from table index to X-value (signed 8-bit, range -8 to 0)
+        float in_val = -8.0*ii/float(N_TABLE);
+        // Next, compute lookup table function
+        typename CONFIG_T::table_t real_val = elu_fcn_float(in_val);
+        //std::cout << "Lookup table In Value: " << in_val << " Result: " << real_val << std::endl;
+        table_out[ii] = real_val;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  elu(data_T data[CONFIG_T::n_in], const res_T alpha, res_T res[CONFIG_T::n_in])
+{
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t elu_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t elu_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_elu_table<CONFIG_T, CONFIG_T::table_size>(elu_table);
+        initialized = true;
+    }
+
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    data_T datareg;
+    // Index into the lookup table based on data
+    int index;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];
+        if (datareg >= 0) {
+            res[ii] = datareg;
+        } else {
+            index = datareg*CONFIG_T::table_size/-8;
+            if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+            res[ii] = alpha * elu_table[index];
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  elu(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    elu<data_T, res_T, CONFIG_T>(data, 1.0, res);
+}
+
+// *************************************************
+//       SELU Activation
+// *************************************************
+inline float selu_fcn_float(float input) {
+    return 1.0507009873554804934193349852946 * (1.6732632423543772848170429916717 * (std::exp(input) - 1.));
+}
+
+template<typename CONFIG_T, int N_TABLE>
+void init_selu_table(typename CONFIG_T::table_t table_out[N_TABLE])
+{
+    // Default SELU function:
+    //   result = 1.05 * (1.673 * (e^(x) - 1))
+    for (int ii = 0; ii < N_TABLE; ii++) {
+        // First, convert from table index to X-value (signed 8-bit, range -8 to 0)
+        float in_val = -8.0*ii/float(N_TABLE);
+        // Next, compute lookup table function
+        typename CONFIG_T::table_t real_val = selu_fcn_float(in_val);
+        //std::cout << "Lookup table In Value: " << in_val << " Result: " << real_val << std::endl;
+        table_out[ii] = real_val;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void  selu(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t selu_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t selu_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_selu_table<CONFIG_T, CONFIG_T::table_size>(selu_table);
+        initialized = true;
+    }
+
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    data_T datareg;
+    // Index into the lookup table based on data
+    int index;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];
+        if (datareg >= 0) {
+            res[ii] = res_T(1.0507009873554804934193349852946) * datareg;
+        } else {
+            index = datareg*CONFIG_T::table_size/-8;
+            if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+            res[ii] = selu_table[index];
+        }
+    }
+}
+
+// *************************************************
+//       PReLU Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void  prelu(data_T data[CONFIG_T::n_in], data_T alpha[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    data_T datareg;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];
+        if (datareg > 0) res[ii] = datareg;
+        else res[ii] = alpha[ii] * datareg;
+    }
+}
+
+// *************************************************
+//       Binary TanH Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void  binary_tanh(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+
+    data_T datareg;
+    res_T cache;
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];
+        if( datareg > 0 ) cache = 1;
+        else cache = -1;
+
+        res[ii] = (res_T) cache;
+    }
+}
+
+// *************************************************
+//       Ternary TanH Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void  ternary_tanh(data_T data[CONFIG_T::n_in], res_T res[CONFIG_T::n_in])
+{
+
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+    }
+  
+    data_T datareg;   
+    res_T cache; 
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial) {
+            #pragma HLS PIPELINE
+        }
+        datareg = 2*data[ii];
+        if( datareg > 1 ) cache = 1;
+        else if( datareg > -1 && datareg <= 1) cache=0;
+        else cache = -1;
+  
+        res[ii] = (res_T) cache;
+    }
+ 
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_activation_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_activation_stream.h
new file mode 100644
index 0000000..e213803
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_activation_stream.h
@@ -0,0 +1,643 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_ACTIVATION_STREAM_H_
+#define NNET_ACTIVATION_STREAM_H_
+
+#include <cmath>
+#include "ap_fixed.h"
+#include "hls_stream.h"
+#include "nnet_common.h"
+#include "nnet_types.h"
+#include "nnet_stream.h"
+#include "nnet_activation.h"
+
+namespace nnet {
+
+// *************************************************
+//       LINEAR Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void linear(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    LinearActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        LinearPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            out_data[j] = in_data[j];
+        }
+
+        res.write(out_data);
+    }
+}
+
+
+// *************************************************
+//       RELU Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void relu(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    ReLUActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        ReLUPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            if (in_data[j] > 0) out_data[j] = in_data[j];
+            else out_data[j] = 0;
+        }
+
+        res.write(out_data);
+    }
+}
+
+// *************************************************
+//       Sigmoid Activation
+// *************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void sigmoid(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t sigmoid_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t sigmoid_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_sigmoid_table<CONFIG_T, CONFIG_T::table_size>(sigmoid_table);
+        initialized = true;
+    }
+
+    SigmoidActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        SigmoidPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            int data_round = in_data[j]*CONFIG_T::table_size/16;
+            int index = data_round + 8*CONFIG_T::table_size/16;
+            if (index < 0)   index = 0;
+            else if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+            out_data[j] = sigmoid_table[index];
+        }
+
+        res.write(out_data);
+    }
+}
+
+
+// *************************************************
+//       Softmax Activation
+// *************************************************
+
+template <class data_T, class res_T, typename CONFIG_T>
+void softmax_latency(hls::stream<data_T> &data, hls::stream<res_T> &res){
+    // Initialize the lookup tables
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::exp_table_t exp_table[CONFIG_T::table_size];
+    typename CONFIG_T::inv_table_t invert_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::exp_table_t exp_table[CONFIG_T::table_size];
+    static typename CONFIG_T::inv_table_t invert_table[CONFIG_T::table_size];
+
+#endif
+    if (!initialized) {
+        // Note we are exponentiating the inputs, which have type data_T
+        init_exp_table<typename data_T::value_type, CONFIG_T>(exp_table);
+        // Note we are inverting the exponentials, which have type exp_table_t
+        init_invert_table<typename CONFIG_T::exp_table_t, CONFIG_T>(invert_table);
+        initialized = true;
+    }
+
+    constexpr unsigned multiplier_limit = DIV_ROUNDUP(data_T::size, CONFIG_T::reuse_factor);
+    constexpr unsigned ii = data_T::size / multiplier_limit;
+
+    // Calculate all the e^x's
+    typename CONFIG_T::exp_table_t exp_res[data_T::size];
+    #pragma HLS array_partition variable=exp_res complete
+    typename CONFIG_T::exp_table_t exp_sum(0);
+    SoftmaxExpLoop: for(unsigned i = 0; i < CONFIG_T::n_in / data_T::size; i++){
+        #pragma HLS PIPELINE II=ii
+
+        data_T in_pack = data.read();
+        SoftmaxExpPackLoop: for(unsigned j = 0; j < data_T::size; j++){
+            #pragma HLS UNROLL
+            unsigned x = softmax_idx_from_real_val<typename data_T::value_type, CONFIG_T>(in_pack[j]);
+            exp_res[j] = exp_table[x];
+        }
+
+        // Explicitly sum the results with an adder tree.
+        // Rounding & Saturation mode, which improve accuracy, prevent Vivado from expression balancing
+        Op_add<typename CONFIG_T::exp_table_t> op_add;
+        exp_sum = reduce<typename CONFIG_T::exp_table_t, data_T::size, Op_add<typename CONFIG_T::exp_table_t>>(exp_res, op_add);
+
+        typename CONFIG_T::inv_table_t inv_exp_sum = invert_table[softmax_idx_from_real_val<typename CONFIG_T::exp_table_t,CONFIG_T>(exp_sum)];
+
+        res_T out_pack;
+        #pragma HLS DATA_PACK variable=out_pack
+        SoftmaxInvPackLoop: for(unsigned j = 0; j < res_T::size; j++){
+            #pragma HLS UNROLL
+            #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
+            out_pack[j] = exp_res[j] * inv_exp_sum;
+        }
+        res.write(out_pack);
+    }
+}
+
+template <class data_T, class res_T, typename CONFIG_T>
+void softmax_stable(hls::stream<data_T> &data, hls::stream<res_T> &res){
+    // Initialize the lookup tables
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::exp_table_t exp_table[CONFIG_T::table_size];
+    typename CONFIG_T::inv_table_t invert_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::exp_table_t exp_table[CONFIG_T::table_size];
+    static typename CONFIG_T::inv_table_t invert_table[CONFIG_T::table_size];
+
+#endif
+    if (!initialized) {
+        // Note we are exponentiating the inputs, which have type data_T
+        init_exp_table<typename data_T::value_type, CONFIG_T>(exp_table);
+        // Note we are inverting the exponentials, which have type exp_table_t
+        init_invert_table<typename CONFIG_T::exp_table_t, CONFIG_T>(invert_table);
+        initialized = true;
+    }
+
+    constexpr unsigned multiplier_limit = DIV_ROUNDUP(data_T::size, CONFIG_T::reuse_factor);
+    constexpr unsigned ii = data_T::size / multiplier_limit;
+
+    typename data_T::value_type data_array[data_T::size];
+    #pragma HLS ARRAY_PARTITION variable=data_array complete
+    SoftmaxArrayLoop: for(unsigned i = 0; i < CONFIG_T::n_in / data_T::size; i++){
+        #pragma HLS PIPELINE II=ii
+
+        data_T in_pack = data.read();
+        SoftmaxArrayPackLoop: for(unsigned j = 0; j < data_T::size; j++){
+            #pragma HLS UNROLL
+            data_array[j] = in_pack[j];
+        }
+
+        // Find the max and compute all delta(x_i, x_max)
+        Op_max<typename data_T::value_type> op_max;
+        typename data_T::value_type x_max = reduce<typename data_T::value_type, data_T::size, Op_max<typename data_T::value_type>>(data_array, op_max);
+
+        // For the diffs, use the same type as the input but force rounding and saturation
+        ap_fixed<data_T::value_type::width, data_T::value_type::iwidth,AP_RND,AP_SAT> d_xi_xmax[data_T::size];
+        for(unsigned j = 0; j < data_T::size; j++){
+            #pragma HLS UNROLL
+            d_xi_xmax[j] = data_array[j] - x_max;
+        }
+
+        // Calculate all the e^x's
+        typename CONFIG_T::exp_table_t exp_res[data_T::size];
+        #pragma HLS ARRAY_PARTITION variable=exp_res complete
+        typename CONFIG_T::exp_table_t exp_sum(0);
+        for(unsigned j = 0; j < data_T::size; j++){
+            #pragma HLS UNROLL
+            unsigned x = softmax_idx_from_real_val<typename data_T::value_type, CONFIG_T>(d_xi_xmax[j]);
+            exp_res[j] = exp_table[x];
+        }
+
+        // Explicitly sum the results with an adder tree.
+        // Rounding & Saturation mode, which improve accuracy, prevent Vivado from expression balancing
+        Op_add<typename CONFIG_T::exp_table_t> op_add;
+        exp_sum = reduce<typename CONFIG_T::exp_table_t, data_T::size, Op_add<typename CONFIG_T::exp_table_t>>(exp_res, op_add);
+
+        typename CONFIG_T::inv_table_t inv_exp_sum = invert_table[softmax_idx_from_real_val<typename CONFIG_T::exp_table_t,CONFIG_T>(exp_sum)];
+
+        res_T out_pack;
+        #pragma HLS DATA_PACK variable=out_pack
+        SoftmaxInvPackLoop: for(unsigned j = 0; j < res_T::size; j++){
+            #pragma HLS UNROLL
+            #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
+            out_pack[j] = exp_res[j] * inv_exp_sum;
+        }
+        res.write(out_pack);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void softmax_legacy(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t exp_table[CONFIG_T::table_size];
+    typename CONFIG_T::table_t invert_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t exp_table[CONFIG_T::table_size];
+    static typename CONFIG_T::table_t invert_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_exp_table_legacy<CONFIG_T, CONFIG_T::table_size>(exp_table);
+        init_invert_table_legacy<CONFIG_T, CONFIG_T::table_size>(invert_table);
+        initialized = true;
+    }
+
+    // Index into the lookup table based on data for exponentials
+    typename CONFIG_T::table_t exp_res[data_T::size];
+    typename CONFIG_T::table_t exp_diff_res;
+    typename data_T::value_type data_cache[data_T::size];
+
+    SoftmaxInitLoop: for(unsigned s = 0; s < CONFIG_T::n_in / data_T::size; s++) {
+        #pragma HLS PIPELINE
+        data_T in_pack = data.read();
+        SoftmaxInitPackLoop: for(unsigned j = 0; j < data_T::size; j++) {
+            #pragma HLS UNROLL
+            data_cache[j] = in_pack[j];
+            exp_res[j] = 0;
+        }
+
+        SoftmaxExpLoop: for (int i = 0; i < data_T::size; i++) {
+            #pragma HLS UNROLL
+            SoftmaxExpInner: for (int j = 0; j < data_T::size; j++) {
+                #pragma HLS UNROLL
+
+                if (i == j) {
+                    exp_diff_res = 1;
+                } else {
+                    int data_round = (data_cache[j] - data_cache[i]) * CONFIG_T::table_size / 16;
+                    int index = data_round + 8 * CONFIG_T::table_size / 16;
+                    if (index < 0) index = 0;
+                    if (index > CONFIG_T::table_size - 1) index = CONFIG_T::table_size - 1;
+                    exp_diff_res = exp_table[index];
+                }
+
+                exp_res[i] += exp_diff_res;
+            }
+        }
+
+        res_T out_pack;
+        #pragma HLS DATA_PACK variable=out_pack
+        SoftmaxInvPackLoop: for(unsigned j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+
+            int exp_res_index = exp_res[j] * CONFIG_T::table_size / 64;
+            if (exp_res_index < 0) exp_res_index = 0;
+            if (exp_res_index > CONFIG_T::table_size - 1) exp_res_index = CONFIG_T::table_size - 1;
+
+            out_pack[j] = (typename res_T::value_type) invert_table[exp_res_index];
+        }
+        res.write(out_pack);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void softmax(hls::stream<data_T> &data, hls::stream<res_T> &res){
+    assert(CONFIG_T::axis == -1);
+
+    switch(CONFIG_T::implementation){
+    case softmax_implementation::latency:
+        softmax_latency<data_T, res_T, CONFIG_T>(data, res);
+        break;
+    case softmax_implementation::stable:
+        softmax_stable<data_T, res_T, CONFIG_T>(data, res);
+        break;
+    case softmax_implementation::legacy:
+        softmax_legacy<data_T, res_T, CONFIG_T>(data, res);
+        break;
+    }    
+}
+
+// *************************************************
+//       TanH Activation
+// *************************************************
+
+
+template<class data_T, class res_T, typename CONFIG_T>
+void tanh(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t tanh_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t tanh_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_tanh_table<CONFIG_T, CONFIG_T::table_size>(tanh_table);
+        initialized = true;
+    }
+
+    TanHActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        TanHPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            int data_round = in_data[j]*CONFIG_T::table_size/8;
+            int index = data_round + 4*CONFIG_T::table_size/8;
+            if (index < 0)   index = 0;
+            else if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+            out_data[j] = tanh_table[index];
+        }
+
+        res.write(out_data);
+    }
+}
+
+
+// *************************************************
+//       Hard sigmoid Activation
+// *************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void hard_sigmoid(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    typename data_T::value_type slope = (typename data_T::value_type) 0.2;
+    typename data_T::value_type shift = (typename data_T::value_type) 0.5;
+
+    HardSigmoidActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        HardSigmoidPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            typename data_T::value_type datareg = slope * in_data[j] + shift;
+            if (datareg > 1) datareg = 1;
+            else if (datareg < 0) datareg = 0;
+            out_data[j] = datareg;
+        }
+
+        res.write(out_data);
+    }
+}
+
+
+// *************************************************
+//       Leaky RELU Activation
+// *************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void leaky_relu(hls::stream<data_T> &data, typename data_T::value_type alpha, hls::stream<res_T> &res) {
+    LeakyReLUActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        LeakyReLUPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            if (in_data[j] > 0) out_data[j] = in_data[j];
+            else out_data[j] = alpha * in_data[j];
+        }
+        res.write(out_data);
+    }
+}
+
+
+// *************************************************
+//       Thresholded RELU Activation
+// *************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void thresholded_relu(hls::stream<data_T> &data, typename data_T::value_type theta, hls::stream<res_T> &res) {
+    ThresholdedReLUActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        ThresholdedReLUPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            if (in_data[j] > theta) out_data[j] = in_data[j];
+            else out_data[j] = 0;
+        }
+
+        res.write(out_data);
+    }
+}
+
+
+// *************************************************
+//       Softplus Activation
+// *************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void softplus(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t softplus_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t softplus_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_softplus_table<CONFIG_T, CONFIG_T::table_size>(softplus_table);
+        initialized = true;
+    }
+
+    SoftplusActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        SoftplusPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            int data_round = in_data[j]*CONFIG_T::table_size/16;
+            int index = data_round + 8*CONFIG_T::table_size/16;
+            if (index < 0)   index = 0;
+            else if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+            out_data[j] = softplus_table[index];
+        }
+        res.write(out_data);
+    }
+}
+
+
+// *************************************************
+//       Softsign Activation
+// *************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void softsign(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t softsign_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t softsign_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_softsign_table<CONFIG_T, CONFIG_T::table_size>(softsign_table);
+        initialized = true;
+    }
+
+    SoftsignActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        SoftsignPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            int data_round = in_data[j]*CONFIG_T::table_size/16;
+            int index = data_round + 8*CONFIG_T::table_size/16;
+            if (index < 0)   index = 0;
+            else if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+            out_data[j] = softsign_table[index];
+        }
+        res.write(out_data);
+    }
+}
+
+
+// *************************************************
+//       ELU Activation
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void elu(hls::stream<data_T> &data, typename data_T::value_type alpha, hls::stream<res_T> &res) {
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t elu_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t elu_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_elu_table<CONFIG_T, CONFIG_T::table_size>(elu_table);
+        initialized = true;
+    }
+
+    EluActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        EluPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            
+            typename data_T::value_type datareg = in_data[j];
+            if (datareg >= 0) {
+                out_data[j] = datareg;
+            } else {
+                int index = datareg*CONFIG_T::table_size/-8;
+                if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+                out_data[j] = alpha * elu_table[index];
+            }
+        }
+        res.write(out_data);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void elu(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    elu<data_T, res_T, CONFIG_T>(data, 1.0, res);
+}
+
+// *************************************************
+//       SELU Activation
+// *************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void selu(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    // Initialize the lookup table
+#ifdef __HLS_SYN__
+    bool initialized = false;
+    typename CONFIG_T::table_t selu_table[CONFIG_T::table_size];
+#else
+    static bool initialized = false;
+    static typename CONFIG_T::table_t selu_table[CONFIG_T::table_size];
+#endif
+    if (!initialized) {
+        init_selu_table<CONFIG_T, CONFIG_T::table_size>(selu_table);
+        initialized = true;
+    }
+
+    SeluActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        SeluPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+
+            typename data_T::value_type datareg = in_data[j];
+            if (datareg >= 0) {
+                out_data[j] = (typename data_T::value_type) 1.0507009873554804934193349852946 * datareg;
+            } else {
+                int index = datareg*CONFIG_T::table_size/-8;
+                if (index > CONFIG_T::table_size-1) index = CONFIG_T::table_size-1;
+                out_data[j] = selu_table[index];
+            }
+        }
+        res.write(out_data);
+    }
+}
+
+
+// *************************************************
+//       PReLU Activation
+// *************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void prelu(hls::stream<data_T> &data, typename data_T::value_type alpha[CONFIG_T::n_in], hls::stream<res_T> &res) {
+    PReLUActLoop: for (int i = 0; i < CONFIG_T::n_in / res_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        PReLUPackLoop: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            if (in_data[j] > 0) out_data[j] = in_data[j];
+            else out_data[j] = alpha[i*res_T::size+j] * in_data[j];
+        }
+        res.write(out_data);
+    }
+}
+
+
+}
+
+#endif
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_array.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_array.h
new file mode 100644
index 0000000..e39c534
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_array.h
@@ -0,0 +1,56 @@
+#ifndef NNET_ARRAY_H_
+#define NNET_ARRAY_H_
+
+#include <math.h>
+
+namespace nnet {
+
+struct transpose_config {
+    static const unsigned height = 10;
+    static const unsigned width = 10;
+    static const unsigned depth = 10;
+    static constexpr unsigned perm[3] = {2, 0, 1};
+};
+
+template<class data_T, typename CONFIG_T>
+void transpose_2d(
+    data_T data[CONFIG_T::height * CONFIG_T::width],
+    data_T data_t[CONFIG_T::height * CONFIG_T::width]
+) {
+    #pragma HLS PIPELINE
+
+    for (int i = 0; i < CONFIG_T::height; i++) {
+        for (int j = 0; j < CONFIG_T::width; j++) {
+            data_t[j * CONFIG_T::height + i] = data[i * CONFIG_T::width + j];
+        }
+    }
+}
+
+template<class data_T, typename CONFIG_T>
+void transpose_3d(
+    data_T data[CONFIG_T::depth * CONFIG_T::height * CONFIG_T::width],
+    data_T data_t[CONFIG_T::depth * CONFIG_T::height * CONFIG_T::width]
+) {
+    unsigned dims[3] = { CONFIG_T::depth, CONFIG_T::height, CONFIG_T::width };
+    unsigned dims_t[3];
+    dims_t[0] = dims[CONFIG_T::perm[0]];
+    dims_t[1] = dims[CONFIG_T::perm[1]];
+    dims_t[2] = dims[CONFIG_T::perm[2]];
+
+    int idx[3] = {0}, idx_t[3] = {0};
+    for (idx[0] = 0; idx[0] < dims[0]; idx[0]++) {
+        for (idx[1] = 0; idx[1] < dims[1]; idx[1]++) {
+            for (idx[2] = 0; idx[2] < dims[2]; idx[2]++) {
+                idx_t[0] = idx[CONFIG_T::perm[0]];
+                idx_t[1] = idx[CONFIG_T::perm[1]];
+                idx_t[2] = idx[CONFIG_T::perm[2]];
+
+                data_t[idx_t[0] * dims_t[1] * dims_t[2] + idx_t[1] * dims_t[2] + idx_t[2]] = data[idx[0] * dims[1] * dims[2] + idx[1] * dims[2] + idx[2]];
+            }
+        }
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_batchnorm.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_batchnorm.h
new file mode 100644
index 0000000..9a5cff0
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_batchnorm.h
@@ -0,0 +1,164 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_BATCHNORM_H_
+#define NNET_BATCHNORM_H_
+
+#include "nnet_common.h"
+#include "nnet_dense.h"
+#include "hls_stream.h"
+#include <math.h>
+
+namespace nnet {
+
+struct batchnorm_config
+{
+    // Internal data type definitions
+    typedef float bias_t;
+    typedef float scale_t;
+
+    // Layer Sizes
+    static const unsigned n_in = 10;
+    static const unsigned n_filt = -1;
+    
+    // Resource reuse info
+    static const unsigned io_type = io_parallel;
+    static const unsigned reuse_factor = 1;
+    static const bool store_weights_in_bram = false;
+    static const unsigned n_zeros = 0;
+    // partitioning arrays cyclically to go with roll factors?
+    template<class x_T, class y_T, class res_T>
+    using product = nnet::product::mult<x_T, y_T, res_T>;
+};
+
+template<class data_T, class res_T, typename CONFIG_T>
+void normalize(
+    data_T    data[CONFIG_T::n_in],
+    res_T     res[CONFIG_T::n_in],
+    typename CONFIG_T::scale_t  scale[CONFIG_T::n_in],
+    typename CONFIG_T::bias_t   bias[CONFIG_T::n_in]
+)
+{
+    data_T cache;
+   
+    // Use a function_instantiate in case it helps to explicitly optimize unchanging weights/biases
+    #pragma HLS function_instantiate variable=scale,bias
+
+    if (CONFIG_T::io_type == io_parallel) {
+        // For parallel inputs:
+        //   - completely partition arrays -- target fabric
+        //   - if we have an unroll factor, limit number of multipliers
+        #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+
+        // #pragma HLS ARRAY_PARTITION variable=weights complete // remove this line for now, it breaks compression sometimes
+        #pragma HLS ARRAY_PARTITION variable=scale complete
+        #pragma HLS ARRAY_PARTITION variable=bias complete
+
+        int multiplier_limit  = ceil(float(CONFIG_T::n_in) / float(CONFIG_T::reuse_factor));
+        CONFIG_T::template product<data_T, typename CONFIG_T::scale_t, res_T>::limit(multiplier_limit);
+
+    } else if (CONFIG_T::io_type == io_serial) {
+        #pragma HLS ARRAY_RESHAPE variable=scale complete dim=1
+        #pragma HLS ARRAY_RESHAPE variable=bias complete dim=1
+        #pragma HLS DATAFLOW
+    }            
+
+    // Calcuate result
+    Result: for (int ires = 0; ires < CONFIG_T::n_in; ires++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS UNROLL
+            #pragma HLS PIPELINE
+        }
+        
+        if (CONFIG_T::n_filt==-1) {
+            res[ires] = CONFIG_T::template product<data_T, typename CONFIG_T::scale_t, res_T>::product(data[ires], scale[ires]) + bias[ires];
+	    } else {
+            int norm_index = ires%CONFIG_T::n_filt;
+            res[ires] = CONFIG_T::template product<data_T, typename CONFIG_T::scale_t, res_T>::product(data[ires], scale[norm_index]) + bias[norm_index];
+        }
+	}
+}
+
+// ****************************************************
+//       Merged Batch Normalization and Quantized Tanh
+// ****************************************************
+struct batchnorm_quantized_tanh_config
+{
+    // Layer Sizes
+    static const unsigned n_in = 10;
+    static const unsigned n_filt = -1;
+    
+    // Resource reuse info
+    static const unsigned io_type = io_parallel;
+    static const unsigned reuse_factor = 1;
+    static const unsigned n_zeros = 0;
+};
+
+template<class data_T, typename CONFIG_T>
+void  normalize_binary_tanh(data_T data[CONFIG_T::n_in], ap_uint<1> res[CONFIG_T::n_in], data_T threshold[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+        #pragma HLS ARRAY_PARTITION variable=res complete
+    }
+
+    data_T datareg;   
+    ap_uint<1> cache; 
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];	 
+        int norm_index = CONFIG_T::n_filt==-1 ? ii : ii%CONFIG_T::n_filt;
+        if( datareg > threshold[norm_index] ) cache = 1;
+        else cache = 0;
+
+        res[ii] = (ap_uint<1>) cache;
+ 
+    }   
+}
+
+template<class data_T, typename CONFIG_T>
+void  normalize_ternary_tanh(data_T data[CONFIG_T::n_in], ap_int<2> res[CONFIG_T::n_in], data_T threshold_hi[CONFIG_T::n_in], data_T threshold_lo[CONFIG_T::n_in])
+{
+    if (CONFIG_T::io_type == io_parallel){
+        #pragma HLS PIPELINE
+        #pragma HLS ARRAY_PARTITION variable=res complete
+    }
+
+    data_T datareg;   
+    ap_int<2> cache; 
+    for (int ii=0; ii<CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        datareg = data[ii];
+        int norm_index = CONFIG_T::n_filt==-1 ? ii : ii%CONFIG_T::n_filt;
+        if( datareg > threshold_hi[norm_index] ) cache = 1;
+        else if( datareg <= threshold_lo[norm_index]) cache = -1;
+        else cache = 0;
+
+        res[ii] = (ap_int<2>) cache;
+
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_batchnorm_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_batchnorm_stream.h
new file mode 100644
index 0000000..382887f
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_batchnorm_stream.h
@@ -0,0 +1,134 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_BATCHNORM_STREAM_H_
+#define NNET_BATCHNORM_STREAM_H_
+
+#include "nnet_common.h"
+#include "nnet_mult.h"
+#include "nnet_types.h"
+#include "hls_stream.h"
+
+namespace nnet {
+
+// ****************************************************
+//       Streaming Batch Normalization
+// ****************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void normalize(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::scale_t scale[CONFIG_T::n_in],
+    typename CONFIG_T::bias_t  bias[CONFIG_T::n_in]
+) {
+    #pragma HLS ARRAY_PARTITION variable=scale complete
+    #pragma HLS ARRAY_PARTITION variable=bias complete
+
+    constexpr unsigned multiplier_limit = DIV_ROUNDUP(CONFIG_T::n_in, CONFIG_T::reuse_factor);
+    constexpr unsigned ii = CONFIG_T::n_in / multiplier_limit;
+    CONFIG_T::template product<typename data_T::value_type, typename CONFIG_T::scale_t, typename res_T::value_type>::limit(multiplier_limit);
+
+    BatchNormLoop: for (int i = 0; i < CONFIG_T::n_in / data_T::size; i++) {
+        #pragma HLS PIPELINE II=ii
+
+        data_T in_data = data.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        BatchNormpack: for (int j = 0; j < data_T::size; j++) {
+            #pragma HLS UNROLL
+            int norm_index;
+            if (CONFIG_T::n_filt==-1) {
+                norm_index = i * data_T::size + j;
+            } else {
+                norm_index = j % CONFIG_T::n_filt;
+            }
+            out_data[j] = CONFIG_T::template product<typename data_T::value_type, typename CONFIG_T::scale_t, typename res_T::value_type>::product(in_data[j], scale[norm_index]) + bias[norm_index];
+        }
+
+        res.write(out_data);
+    }
+}
+
+// ****************************************************
+//       Merged Batch Normalization and Quantized Tanh
+// ****************************************************
+template<class data_T, typename CONFIG_T>
+void normalize_binary_tanh(
+    hls::stream<data_T> &data,
+    hls::stream<nnet::array<ap_uint<1>, CONFIG_T::n_in>> &res,
+    typename data_T::value_type threshold[CONFIG_T::n_in]
+) {
+    #pragma HLS ARRAY_PARTITION variable=threshold complete
+
+    BinaryNormLoop: for (int i = 0; i < CONFIG_T::n_in / data_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        nnet::array<ap_uint<1>, CONFIG_T::n_in> out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        BatchNormPack: for (int j = 0; j < data_T::size; j++) {
+            #pragma HLS UNROLL
+            out_data[j] = (in_data[j] > threshold[i * data_T::size + j]) ? 1 : 0;
+        }
+
+        res.write(out_data);
+    }
+}
+
+template<class data_T, typename CONFIG_T>
+void normalize_ternary_tanh(
+    hls::stream<data_T> &data,
+    hls::stream<nnet::array<ap_int<2>, CONFIG_T::n_in>> &res,
+    typename data_T::value_type threshold_hi[CONFIG_T::n_in],
+    typename data_T::value_type threshold_lo[CONFIG_T::n_in]
+) {
+    #pragma HLS ARRAY_PARTITION variable=threshold_hi complete
+    #pragma HLS ARRAY_PARTITION variable=threshold_lo complete
+
+    TernaryNormLoop: for (int i = 0; i < CONFIG_T::n_in / data_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        nnet::array<ap_int<2>, CONFIG_T::n_in> out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        BatchNormPack: for (int j = 0; j < data_T::size; j++) {
+            #pragma HLS UNROLL
+            
+            int norm_index = i * data_T::size + j;
+
+            if (in_data[j] > threshold_hi[norm_index]) {
+                out_data[j] = 1;
+            } else if (in_data[j] <= threshold_lo[norm_index]) {
+                out_data[j] = -1;
+            } else {
+                out_data[j] = 0;
+            }
+        }
+
+        res.write(out_data);
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_common.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_common.h
new file mode 100644
index 0000000..7ad303d
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_common.h
@@ -0,0 +1,100 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_COMMON_H_
+#define NNET_COMMON_H_
+
+#include "ap_fixed.h"
+
+// This is a substitute for "ceil(n/(float)d)".
+#define DIV_ROUNDUP(n,d) ((n + d - 1) / d)
+#define MIN(n,d) (n > d ? d : n)
+#define MAX(n,d) (n > d ? n : d)
+
+namespace nnet {
+
+// Common type definitions
+enum io_type {io_parallel = 0, io_serial, io_stream};
+enum strategy { latency, resource };
+
+ /* ---
+  * Balanced tree reduce implementation.
+  * For use in scenarios where Vivado cannot expression balance
+  * Reduces an array of inputs to a single value using the template binary operator 'Op',
+  * for example summing all elements with Op_add, or finding the maximum with Op_max
+  * Use only when the input array is fully unrolled. Or, slice out a fully unrolled section
+  * before applying and accumulate the result over the rolled dimension.
+  * --- */
+ template<class T, int N, class Op>
+ T reduce(const T* x, Op op)
+ {
+     static constexpr int leftN = pow2(floorlog2(N - 1)) > 0 ? pow2(floorlog2(N - 1)) : 0;
+     static constexpr int rightN = N - leftN > 0 ? N - leftN : 0;
+     if (N == 1){
+         return x[0];
+     }
+     if (N == 2){
+         return op(x[0],x[1]);
+     }
+     return op(reduce<T,leftN,Op>(x, op), reduce<T,rightN,Op>(x+leftN, op));
+ } 
+
+ template<class T>
+ class Op_add{
+ public:
+	 T operator()(T a, T b){
+		 return a + b;
+	 }
+ };
+
+ template<class T>
+ class Op_and{
+ public:
+	 T operator()(T a, T b){
+		 return a && b;
+	 }
+ };
+
+ template<class T>
+ class Op_or{
+ public:
+	 T operator()(T a, T b){
+		 return a || b;
+	 }
+ };
+
+ template<class T>
+ class Op_max{
+ public:
+     T operator()(T a, T b){
+        return a >= b ? a : b;
+     }
+ };
+
+ template<class T>
+ class Op_min{
+ public:
+     T operator()(T a, T b){
+        return a <= b ? a : b;
+     }
+ };
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d.h
new file mode 100644
index 0000000..fcc1644
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d.h
@@ -0,0 +1,70 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_CONV1D_H_
+#define NNET_CONV1D_H_
+
+#include "nnet_common.h"
+#include "nnet_conv1d_latency.h"
+#include "nnet_conv1d_resource.h"
+#include <cstdlib>
+
+namespace nnet {
+
+struct conv1d_config
+{
+    // Internal data type definitions
+    typedef float bias_t;
+    typedef float weight_t;
+    typedef float accum_t;
+
+    // Convolutional parameters
+    static const unsigned pad_left = 0;
+    static const unsigned pad_right = 0;
+    static const unsigned in_width = 10;
+    static const unsigned n_chan = 0;
+    static const unsigned filt_width = 1;
+    static const unsigned kernel_size = filt_width;
+    static const unsigned n_filt = 1;
+    static const unsigned stride_width = 1;
+    static const unsigned dilation = 1;
+    static const unsigned out_width = 10; //(N_IN + PAD_LEFT * PAD_RIGHT - (DILATION * (FILT_WIDTH - 1) + 1)) / STRIDE + 1
+
+    static const unsigned reuse_factor = 1;
+    static const bool store_weights_in_bram = false;
+    static const unsigned n_zeros = 0; // not used yet
+};
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_1d_cl(
+    data_T data[CONFIG_T::in_width * CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    if (CONFIG_T::strategy == nnet::latency) {
+        conv_1d_latency_cl<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    } else {
+        conv_1d_resource_cl<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    }
+}
+
+}//end namespace
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d_latency.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d_latency.h
new file mode 100644
index 0000000..d4f4d5f
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d_latency.h
@@ -0,0 +1,195 @@
+#ifndef NNET_CONV1D_LATENCY_H_
+#define NNET_CONV1D_LATENCY_H_
+
+#include "nnet_common.h"
+#include <cstdlib>
+
+namespace nnet {
+
+//Computes multiplier limit
+//This function should not be synthesized into firmware
+template<typename CONFIG_T>
+int compute_multiplier_limit(
+    typename CONFIG_T::weight_t  weights[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt]
+)
+{
+    int n_mult = 0;
+    for(int ii = 0; ii < CONFIG_T::out_width; ii++) {
+        for(int ff = 0; ff < CONFIG_T::n_filt; ff++){
+            for(int cc = 0; cc < CONFIG_T::n_chan; cc++){
+                for(int jj = 0; jj < CONFIG_T::filt_width; jj++){
+
+                    int index_weight = jj*CONFIG_T::n_chan*CONFIG_T::n_filt + cc*CONFIG_T::n_filt + ff;
+
+                    if((ii*CONFIG_T::stride_width+jj) < CONFIG_T::pad_left || (ii*CONFIG_T::stride_width+jj) >= (CONFIG_T::pad_left + CONFIG_T::in_width)){
+                        //padded -- do nothing
+                        continue;
+                    } else {
+                        //need to tune this cut?
+                        if( weights[index_weight] > 1e-20 || weights[index_weight] < -1e-20 ){
+                            n_mult++;
+                        }//end if nonzero weight
+                    }//end not padding
+                }//end loop accross filter
+            }//end channel loop
+        }//end filter loop
+    }//end output loop
+
+    return ceil( float(n_mult) / float(CONFIG_T::reuse_factor) );
+
+}//end compute_n_mult
+
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_1d_latency_cl(
+    data_T data[CONFIG_T::in_width * CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt]
+)
+{
+
+    typename CONFIG_T::accum_t mult[CONFIG_T::out_width * CONFIG_T::n_filt * CONFIG_T::n_chan * CONFIG_T::filt_width];
+    typename CONFIG_T::accum_t acc[CONFIG_T::out_width][CONFIG_T::n_filt];
+
+    #pragma HLS ARRAY_PARTITION variable=mult complete dim=0
+    #pragma HLS ARRAY_PARTITION variable=acc complete dim=0
+
+    // Use a function_instantiate in case it helps to explicitly optimize unchanging weights/biases
+    #pragma HLS function_instantiate variable=weights,biases
+
+    // Parallel mode
+    #pragma HLS PIPELINE
+    #pragma HLS ARRAY_PARTITION variable=biases complete dim=0
+
+    // Limit multipliers to control parallelization
+    const int multiplier_limit = compute_multiplier_limit<CONFIG_T>(weights);
+    #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
+
+    // Convolve, saving all multiplication results to accumulate later
+    ConvOut: for(int ii = 0; ii < CONFIG_T::out_width; ii++) {
+        ConvFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++){
+            ConvChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++){
+                ConvMult: for(int jj = 0; jj < CONFIG_T::filt_width; jj++){
+
+                    int index_mult   = ii*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_width + ff*CONFIG_T::n_chan*CONFIG_T::filt_width + cc*CONFIG_T::filt_width + jj;
+                    int index_weight = jj*CONFIG_T::n_chan*CONFIG_T::n_filt + cc*CONFIG_T::n_filt + ff;
+                    int index_data   = (ii*CONFIG_T::stride_width+jj-CONFIG_T::pad_left) * CONFIG_T::n_chan + cc;
+
+                    if((ii*CONFIG_T::stride_width+jj) < CONFIG_T::pad_left || (ii*CONFIG_T::stride_width+jj) >= (CONFIG_T::pad_left + CONFIG_T::in_width)){
+                        mult[index_mult] = 0;
+                    }
+                    else {
+                        mult[index_mult] = data[index_data] * weights[index_weight];
+                    }
+                }
+            }//end channel loop
+        }//end filter loop
+    }//end output loop
+
+
+    // Initialize accumulator with input biases
+    for(int ii = 0; ii < CONFIG_T::out_width; ii++) {
+        for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+            acc[ii][ff]=biases[ff];
+        }
+    }
+
+
+    // Accumulate multiplication result
+    AccumOut: for(int ii = 0; ii < CONFIG_T::out_width; ii++) {
+        AccumFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+            //Do "dot product" sum within filter and sum over channels
+            AccumChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++){
+                AccumDot: for(int jj = 0; jj < CONFIG_T::filt_width; jj++){
+                    int index_mult = ii*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_width + ff*CONFIG_T::n_chan*CONFIG_T::filt_width + cc*CONFIG_T::filt_width + jj;
+                    acc[ii][ff] += mult[index_mult];
+                }//end dot product loop
+            }//end channel loop
+        }//end filter loop
+    }//end output loop
+
+
+    // Cast to "res_t" type
+    for(int ii = 0; ii < CONFIG_T::out_width; ii++) {
+        for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+            res[ii * CONFIG_T::n_filt + ff] = (res_T)(acc[ii][ff]);
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pointwise_conv_1d_cl(
+    data_T data[CONFIG_T::in_width * CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    assert(CONFIG_T::filt_width == 1);
+
+    typename CONFIG_T::accum_t mult[CONFIG_T::out_width * CONFIG_T::n_filt * CONFIG_T::n_chan];
+    typename CONFIG_T::accum_t acc[CONFIG_T::out_width][CONFIG_T::n_filt];
+
+    #pragma HLS ARRAY_PARTITION variable=mult complete dim=0
+    #pragma HLS ARRAY_PARTITION variable=acc complete dim=0
+
+    // Use a function_instantiate in case it helps to explicitly optimize unchanging weights/biases
+    #pragma HLS function_instantiate variable=weights,biases
+
+    // Parallel mode
+    #pragma HLS PIPELINE
+    #pragma HLS ARRAY_PARTITION variable=biases complete dim=0
+
+    // Limit multipliers to control parallelization
+    const int multiplier_limit = compute_multiplier_limit<CONFIG_T>(weights);
+    #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
+
+    // Convolve, saving all multiplication results to accumulate later
+    ConvOut: for(int ii = 0; ii < CONFIG_T::out_width; ii++) {
+        ConvFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+            ConvChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++) {
+                int index_mult   = ii*CONFIG_T::n_filt*CONFIG_T::n_chan + ff*CONFIG_T::n_chan + cc;
+                int index_weight = cc*CONFIG_T::n_filt + ff;
+                int index_data   = (ii*CONFIG_T::stride_width-CONFIG_T::pad_left) * CONFIG_T::n_chan + cc;
+
+                if((ii*CONFIG_T::stride_width) < CONFIG_T::pad_left || (ii*CONFIG_T::stride_width) >= (CONFIG_T::pad_left + CONFIG_T::in_width)){
+                    mult[index_mult] = 0;
+                }
+                else {
+                    mult[index_mult] = data[index_data] * weights[index_weight];
+                }
+            }//end channel loop
+        }//end filter loop
+    }//end output loop
+
+
+    // Initialize accumulator with input biases
+    for(int ii = 0; ii < CONFIG_T::out_width; ii++) {
+        for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+            acc[ii][ff]=biases[ff];
+        }
+    }
+
+
+    // Accumulate multiplication result
+    AccumOut: for(int ii = 0; ii < CONFIG_T::out_width; ii++) {
+        AccumFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+            //Do "dot product" sum within filter and sum over channels
+            AccumChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++) {
+                int index_mult = ii*CONFIG_T::n_filt*CONFIG_T::n_chan + ff*CONFIG_T::n_chan + cc;
+                acc[ii][ff] += mult[index_mult];
+            }//end channel loop
+        }//end filter loop
+    }//end output loop
+
+
+    // Cast to "res_t" type
+    for(int ii = 0; ii < CONFIG_T::out_width; ii++) {
+        for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+            res[ii * CONFIG_T::n_filt + ff] = (res_T)(acc[ii][ff]);
+        }
+    }
+}
+
+}
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d_resource.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d_resource.h
new file mode 100644
index 0000000..043812c
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d_resource.h
@@ -0,0 +1,199 @@
+#ifndef NNET_CONV1D_RESOURCE_H_
+#define NNET_CONV1D_RESOURCE_H_
+
+#include "nnet_common.h"
+#include "nnet_dense.h"
+
+namespace nnet {
+
+template<class data_T, typename CONFIG_T>
+void im2col_1d(data_T data[CONFIG_T::in_width * CONFIG_T::n_chan], data_T data_col[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::out_width]) {
+    //int index = 0;
+    for (int channel = CONFIG_T::n_chan; channel--; data += CONFIG_T::in_width) {
+        #pragma HLS PIPELINE II=1 rewind
+		for (int kernel_col = 0; kernel_col < CONFIG_T::filt_width; kernel_col++) {
+            #pragma HLS UNROLL
+            int input_col = -CONFIG_T::pad_left + kernel_col * CONFIG_T::dilation;
+            for (int output_col = CONFIG_T::out_width; output_col; output_col--) {
+                #pragma HLS UNROLL
+                if (input_col >= 0 && input_col < CONFIG_T::in_width) {
+                    *(data_col++) = data[input_col];
+                    //data_col[index] = data[input_col];
+                } else {
+                    *(data_col++) = 0;
+                    //data_col[index] = 0;
+                }
+                //index++;
+                input_col += CONFIG_T::stride_width;
+            }
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_1d_full(
+    data_T data[CONFIG_T::in_width * CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt]
+)
+{
+    data_T data_conv[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::out_width];
+    data_T data_col[CONFIG_T::filt_width * CONFIG_T::n_chan];
+    res_T res_col[CONFIG_T::n_filt];
+
+    //#pragma HLS ARRAY_PARTITION variable=data_conv complete
+    #pragma HLS ARRAY_PARTITION variable=data_col complete
+    #pragma HLS ARRAY_PARTITION variable=res_col complete
+
+    im2col_1d<data_T, CONFIG_T>(data, data_conv);
+
+    for (int i = 0; i < CONFIG_T::out_width; i++) {
+        #pragma HLS UNROLL
+        for (int j = 0; j < CONFIG_T::filt_width * CONFIG_T::n_chan; j++) {
+            data_col[j] = data_conv[j * CONFIG_T::out_width + i];
+        }
+        dense_resource<data_T, res_T, typename CONFIG_T::mult_config>(data_col, res_col, weights, biases);
+        for (int j = 0; j < CONFIG_T::n_filt; j++) {
+            //res[i * CONFIG_T::n_filt + j] = res_col[j];
+            res[j * CONFIG_T::out_width + i] = res_col[j]; // Transposed order
+        }
+    }
+}
+
+template<class data_T, typename CONFIG_T>
+void im2col_1d_cf_idx(data_T data[CONFIG_T::in_width * CONFIG_T::n_chan], data_T data_col[CONFIG_T::filt_width * CONFIG_T::n_chan], const int col) {
+    ChannelLoop:
+    for (int channel = 0; channel < CONFIG_T::n_chan; channel++) {
+		//#pragma HLS UNROLL
+        #pragma HLS PIPELINE II=1 rewind
+        KernelLoop:
+        for (int kernel_col = 0; kernel_col < CONFIG_T::filt_width; kernel_col++) {
+            #pragma HLS UNROLL
+            int input_col = -CONFIG_T::pad_left + kernel_col * CONFIG_T::dilation + col * CONFIG_T::stride_width;
+            if (input_col >= 0 && input_col < CONFIG_T::in_width) {
+                //*(data_col++) = data[input_col];
+                data_col[channel * CONFIG_T::filt_width + kernel_col] = data[channel * CONFIG_T::in_width + input_col];
+            } else {
+                //*(data_col++) = 0;
+                data_col[channel * CONFIG_T::filt_width + kernel_col] = 0;
+            }
+        }
+    }
+}
+
+template<class data_T, typename CONFIG_T>
+void im2col_1d_cf(data_T data[CONFIG_T::in_width * CONFIG_T::n_chan], data_T data_col[CONFIG_T::n_chan * CONFIG_T::filt_width], const int col) {
+    int index = 0;
+    ChannelLoop:
+    for (int channel = CONFIG_T::n_chan; channel--; data += CONFIG_T::in_width) {
+		#pragma HLS UNROLL
+        KernelLoop:
+        for (int kernel_col = 0; kernel_col < CONFIG_T::filt_width; kernel_col++) {
+            int input_col = -CONFIG_T::pad_left + kernel_col * CONFIG_T::dilation + col * CONFIG_T::stride_width;
+            if (input_col >= 0 && input_col < CONFIG_T::in_width) {
+                //*(data_col++) = data[input_col];
+                data_col[index] = data[input_col];
+            } else {
+                //*(data_col++) = 0;
+                data_col[index] = 0;
+            }
+            index++;
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_1d_resource_cf(
+    data_T data[CONFIG_T::n_chan * CONFIG_T::in_width],
+    res_T  res[CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt]
+)
+{
+    const int nin = CONFIG_T::n_chan * CONFIG_T::filt_width;
+    const int nout = CONFIG_T::n_filt;
+    const int rufactor = CONFIG_T::reuse_factor;
+    const int block_factor = DIV_ROUNDUP(nin*nout, rufactor);
+
+    //#pragma HLS function_instantiate variable=weights,biases
+    //#pragma HLS RESOURCE         variable=weights core=RAM_2P_BRAM Commenting out the deisgnation HLS seems to choose correctly
+    //#pragma HLS ARRAY_RESHAPE   variable=weights block factor=block_factor
+    //#pragma HLS ARRAY_PARTITION variable=biases complete
+
+    data_T data_col[CONFIG_T::filt_width * CONFIG_T::n_chan];
+    res_T res_col[CONFIG_T::n_filt];
+
+    #pragma HLS ARRAY_PARTITION variable=data_col complete
+    #pragma HLS ARRAY_PARTITION variable=res_col complete
+
+    ColLoop:
+    for (int i = 0; i < CONFIG_T::out_width; i++) {
+        #pragma HLS PIPELINE
+        im2col_1d_cf<data_T, CONFIG_T>(data, data_col, i);
+        dense_resource<data_T, res_T, typename CONFIG_T::mult_config>(data_col, res_col, weights, biases);
+        for (int j = 0; j < CONFIG_T::n_filt; j++) {
+            //res[i * CONFIG_T::n_filt + j] = res_col[j];
+            res[j * CONFIG_T::out_width + i] = res_col[j]; // Transposed order
+        }
+    }
+}
+
+template<class data_T, typename CONFIG_T>
+void im2col_1d_cl(data_T data[CONFIG_T::in_width * CONFIG_T::n_chan], data_T data_col[CONFIG_T::filt_width * CONFIG_T::n_chan], const int col) {
+    int index = 0;
+    KernelLoop:
+    for (int kernel_col = 0; kernel_col < CONFIG_T::filt_width; kernel_col++) {
+        #pragma HLS UNROLL
+
+        ChannelLoop:
+        for (int channel = 0; channel < CONFIG_T::n_chan; channel++) {
+            int index_data = (col*CONFIG_T::stride_width+kernel_col-CONFIG_T::pad_left) * CONFIG_T::n_chan + channel;
+
+            if (index_data >= 0 && index_data < CONFIG_T::in_width*CONFIG_T::n_chan) {
+                data_col[index] = data[index_data];
+            } else {
+                data_col[index] = 0;
+            }
+            index++;
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_1d_resource_cl(
+    data_T data[CONFIG_T::in_width * CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt]
+)
+{
+    const int nin = CONFIG_T::n_chan * CONFIG_T::filt_width;
+    const int nout = CONFIG_T::n_filt;
+    const int rufactor = CONFIG_T::reuse_factor;
+    const int block_factor = DIV_ROUNDUP(nin*nout, rufactor);
+
+    //#pragma HLS function_instantiate variable=weights,biases
+    //#pragma HLS RESOURCE         variable=weights core=RAM_2P_BRAM Commenting out the deisgnation HLS seems to choose correctly
+    //#pragma HLS ARRAY_RESHAPE   variable=weights block factor=block_factor
+    //#pragma HLS ARRAY_PARTITION variable=biases complete
+
+    data_T data_col[CONFIG_T::filt_width * CONFIG_T::n_chan];
+    res_T res_col[CONFIG_T::n_filt];
+
+    #pragma HLS ARRAY_PARTITION variable=data_col complete
+    #pragma HLS ARRAY_PARTITION variable=res_col complete
+
+    ColLoop:
+    for (int i = 0; i < CONFIG_T::out_width; i++) {
+        #pragma HLS PIPELINE
+        im2col_1d_cl<data_T, CONFIG_T>(data, data_col, i);
+        dense_resource<data_T, res_T, typename CONFIG_T::mult_config>(data_col, res_col, weights, biases);
+        for (int j = 0; j < CONFIG_T::n_filt; j++) {
+            res[i * CONFIG_T::n_filt + j] = res_col[j];
+        }
+    }
+}
+
+}
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d_stream.h
new file mode 100644
index 0000000..e0f6f08
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv1d_stream.h
@@ -0,0 +1,96 @@
+#ifndef NNET_CONV1D_STREAM_H_
+#define NNET_CONV1D_STREAM_H_
+
+#include "nnet_common.h"
+#include "nnet_conv_stream.h"
+#include "hls_stream.h"
+
+namespace nnet {
+
+template<class data_T, typename CONFIG_T>
+void compute_scaled_indices_1d(
+    const unsigned w_idx,
+    ap_uint<CONFIG_T::filt_width> *pixel_idx
+) {
+    unsigned wp_idx = w_idx * (data_T::size / CONFIG_T::n_chan);
+
+    ComputeIndex: for (unsigned p = 0; p < data_T::size / CONFIG_T::n_chan; p++) {
+        #pragma HLS UNROLL
+
+        unsigned sw_idx = scale_index<CONFIG_T::filt_width, CONFIG_T::stride_width, CONFIG_T::in_width>(wp_idx + p);
+        pixel_idx[p] = CONFIG_T::pixels[sw_idx];
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_1d_encoded_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    assert(CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::filt_width * CONFIG_T::n_chan];
+    const int win_depth = CONFIG_T::out_width;
+    for (unsigned i_out = 0; i_out < CONFIG_T::filt_width * CONFIG_T::n_chan; i_out++) {
+        #pragma HLS STREAM variable=data_window[i_out] depth=win_depth
+    }
+
+    #pragma HLS ARRAY_PARTITION variable=CONFIG_T::pixels complete
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+    unsigned outputs_ready = 0;
+
+    ap_uint<CONFIG_T::filt_width> pixel_idx[data_T::size / CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=pixel_idx complete
+
+    ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width / (data_T::size / CONFIG_T::n_chan); i_iw++) {
+        #pragma HLS LOOP_FLATTEN
+        if (CONFIG_T::strategy == nnet::latency && data_T::size / CONFIG_T::n_chan == 1) {
+            #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+        }
+        compute_scaled_indices_1d<data_T, CONFIG_T>(i_iw, pixel_idx);
+        compute_output_encoded<data_T, res_T, CONFIG_T>(data.read(), data_window, res, res_pack, outputs_ready, weights, biases, pixel_idx);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_1d_buffer_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    assert(CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+
+    ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width; i_iw++) {
+        #pragma HLS LOOP_FLATTEN
+        if (CONFIG_T::strategy == nnet::latency) {
+            #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+        }
+        compute_output_buffer_1d<data_T, res_T, CONFIG_T>(data.read(), res, weights, biases);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_1d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    #pragma HLS inline region
+    switch(CONFIG_T::implementation){
+        case conv_implementation::linebuffer:
+            conv_1d_buffer_cl<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+            break;
+        case conv_implementation::encoded:
+            conv_1d_encoded_cl<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+            break;
+    }  
+}
+
+}
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d.h
new file mode 100644
index 0000000..5ecdc3a
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d.h
@@ -0,0 +1,91 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_CONV2D_H_
+#define NNET_CONV2D_H_
+
+#include "nnet_common.h"
+#include "nnet_conv2d_latency.h"
+#include "nnet_conv2d_resource.h"
+#include <cstdlib>
+
+namespace nnet {
+
+struct conv2d_config
+{
+    // Internal data type definitions
+    typedef float bias_t;
+    typedef float weight_t;
+    typedef float accum_t;
+
+    // Convolutional parameters
+    static const unsigned pad_top = 0;
+    static const unsigned pad_bottom = 0;
+    static const unsigned pad_left = 0;
+    static const unsigned pad_right = 0;
+    static const unsigned in_height = 10;
+    static const unsigned in_width = 10;
+    static const unsigned n_chan = 1;
+    static const unsigned filt_height = 1;
+    static const unsigned filt_width = 1;
+    static const unsigned kernel_size = filt_height * filt_width;
+    static const unsigned n_filt = 1;
+    static const unsigned stride_height = 1;
+    static const unsigned stride_width = 1;
+    static const unsigned out_height = 10;
+    static const unsigned out_width = 10;
+    static const unsigned dilation_height = 1;
+    static const unsigned dilation_width = 1;
+
+    static const unsigned reuse_factor = 1;
+    static const bool store_weights_in_bram = false;
+    static const unsigned n_zeros = 0; // not used yet
+};
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_2d_cf(
+    data_T data[CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    if (CONFIG_T::strategy == nnet::latency) {
+        conv_2d_latency_cf<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    } else {
+        conv_2d_resource_cf<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_2d_cl(
+    data_T data[CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    if (CONFIG_T::strategy == nnet::latency) {
+        conv_2d_latency_cl<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    } else {
+        conv_2d_resource_cl<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    }
+}
+
+}//end namespace
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d_latency.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d_latency.h
new file mode 100644
index 0000000..9cd91b8
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d_latency.h
@@ -0,0 +1,385 @@
+#ifndef NNET_CONV2D_LATENCY_H_
+#define NNET_CONV2D_LATENCY_H_
+
+#include "nnet_common.h"
+#include <cstdlib>
+
+namespace nnet {
+
+//Computes multiplier limit
+//This function should not be synthesized into firmware
+template<typename CONFIG_T>
+    int compute_multiplier_limit_conv2d(
+    typename CONFIG_T::weight_t  weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt]
+)
+{
+    int n_mult = 0;
+
+    for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            for(int ff = 0; ff < CONFIG_T::n_filt; ff++){
+                for(int cc = 0; cc < CONFIG_T::n_chan; cc++){
+                    for(int fh = 0; fh < CONFIG_T::filt_height; fh++){
+                        for(int fw = 0; fw < CONFIG_T::filt_width; fw++){
+
+                                int index_weight = fh*CONFIG_T::filt_width*CONFIG_T::n_chan*CONFIG_T::n_filt
+                                                 + fw*CONFIG_T::n_chan*CONFIG_T::n_filt
+                                                 + cc*CONFIG_T::n_filt
+                                                  + ff;
+
+                                if ((oh*CONFIG_T::stride_height+fh) < CONFIG_T::pad_top
+                                || (oh*CONFIG_T::stride_height+fh) >= (CONFIG_T::pad_top+CONFIG_T::in_height)
+                                || (ow*CONFIG_T::stride_width+fw) < CONFIG_T::pad_left
+                                || (ow*CONFIG_T::stride_width+fw) >= (CONFIG_T::pad_left+CONFIG_T::in_width)) {
+                                    //padded - do nothing
+                                    continue;
+                                } else {
+                                    if (weights[index_weight] > 1e-20 || weights[index_weight] < -1e-20) {
+                                          n_mult++;
+                                    }
+                                }
+
+                        }//end mult loop
+                    }//end channel loop
+                }//end filter width loop
+            }//end filter height loop
+        }//end output width loop
+    }//end output height loop
+
+    return ceil( float(n_mult) / float(CONFIG_T::reuse_factor) );
+
+}//end compute_n_mult
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_2d_latency_cf(
+    data_T data[CONFIG_T::in_height*CONFIG_T::in_width*CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_height*CONFIG_T::out_width*CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+
+    typename CONFIG_T::accum_t mult[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt * CONFIG_T::n_chan * CONFIG_T::filt_height * CONFIG_T::filt_width];
+    typename CONFIG_T::accum_t acc[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt];
+
+    #pragma HLS ARRAY_PARTITION variable=mult complete dim=0
+    #pragma HLS ARRAY_PARTITION variable=acc complete dim=0
+
+    // Use a function_instantiate in case it helps to explicitly optimize unchanging weights/biases
+    #pragma HLS function_instantiate variable=weights,biases
+
+    // Parallel mode
+    #pragma HLS PIPELINE
+    #pragma HLS ARRAY_PARTITION variable=biases complete dim=0
+
+    // Limit multipliers to control parallelization
+    const int multiplier_limit = compute_multiplier_limit_conv2d<CONFIG_T>(weights);
+    #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
+
+    // Convolve, saving all multiplication results to accumulate later
+    ConvOutHeight: for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        ConvOutWidth: for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            ConvFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++){
+                ConvChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++){
+                    ConvFiltHeight: for(int fh = 0; fh < CONFIG_T::filt_height; fh++){
+                        ConvFiltWidth: for(int fw = 0; fw < CONFIG_T::filt_width; fw++){
+
+                            int index_mult = oh*CONFIG_T::out_width*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + ow*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + ff*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + cc*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + fh*CONFIG_T::filt_width
+                                           + fw;
+
+                                int index_weight = fh*CONFIG_T::filt_width*CONFIG_T::n_chan*CONFIG_T::n_filt
+                                                 + fw*CONFIG_T::n_chan*CONFIG_T::n_filt
+                                                 + cc*CONFIG_T::n_filt
+                                                 + ff;
+
+                                if ((oh*CONFIG_T::stride_height+fh) < CONFIG_T::pad_top
+                                || (oh*CONFIG_T::stride_height+fh) >= (CONFIG_T::pad_top+CONFIG_T::in_height)
+                                || (ow*CONFIG_T::stride_width+fw) < CONFIG_T::pad_left
+                                || (ow*CONFIG_T::stride_width+fw) >= (CONFIG_T::pad_left+CONFIG_T::in_width)) {
+                                    mult[index_mult] = 0;
+                                } else {
+                                    int index_data = cc*CONFIG_T::in_height*CONFIG_T::in_width
+                                                   + (oh*CONFIG_T::stride_height+fh-CONFIG_T::pad_top)*CONFIG_T::in_width
+                                                   + (ow*CONFIG_T::stride_width+fw-CONFIG_T::pad_left);
+                                    mult[index_mult] = data[index_data] * weights[index_weight];
+                                }
+
+                        }//end mult loop
+                    }//end channel loop
+                  }//end filter width loop
+            }//end filter height loop
+        }//end output width loop
+    }//end output height loop
+
+
+    // Initialize accumulator with input biases
+    for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+                acc[oh*CONFIG_T::out_width*CONFIG_T::n_filt + ow*CONFIG_T::n_filt + ff]=biases[ff];
+            }
+        }
+    }
+
+
+    // Accumulate multiplication result
+    AccumOutHeight: for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        AccumOutWidth: for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            AccumFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+                //Do "dot product" sum within filter and sum over channels
+                AccumChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++){
+                    AccumDotHeight: for(int fh = 0; fh < CONFIG_T::filt_height; fh++){
+                        AccumDotWidth: for(int fw = 0; fw < CONFIG_T::filt_width; fw++){
+
+                            int index_mult = oh*CONFIG_T::out_width*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + ow*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + ff*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + cc*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + fh*CONFIG_T::filt_width
+                                           + fw;
+                            int index_acc = oh*CONFIG_T::out_width*CONFIG_T::n_filt
+                                          + ow*CONFIG_T::n_filt
+                                          + ff;
+
+                            acc[index_acc] += mult[index_mult];
+
+                        }//end dot product filter width loop
+                    }//end dot product filter height loop
+                }//end n channel loop
+            }//end n filter loop
+        }//end output width loop
+    }//end output height loop
+
+    // Cast to "res_t" type
+    for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+        for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+            for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+                int res_index = ff*CONFIG_T::out_height*CONFIG_T::out_width + oh*CONFIG_T::out_width + ow;
+                int acc_index = oh*CONFIG_T::out_width*CONFIG_T::n_filt + ow*CONFIG_T::n_filt + ff;
+                res[res_index] = acc[acc_index];
+            }
+        }
+    }
+
+}//end conv2d
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_2d_latency_cl(
+    data_T data[CONFIG_T::in_height*CONFIG_T::in_width*CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_height*CONFIG_T::out_width*CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+
+    typename CONFIG_T::accum_t mult[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt * CONFIG_T::n_chan * CONFIG_T::filt_height * CONFIG_T::filt_width];
+    typename CONFIG_T::accum_t acc[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt];
+
+    #pragma HLS ARRAY_PARTITION variable=mult complete dim=0
+    #pragma HLS ARRAY_PARTITION variable=acc complete dim=0
+
+    // Use a function_instantiate in case it helps to explicitly optimize unchanging weights/biases
+    #pragma HLS function_instantiate variable=weights,biases
+
+    // Parallel mode
+    #pragma HLS PIPELINE
+    #pragma HLS ARRAY_PARTITION variable=biases complete dim=0
+
+    // Limit multipliers to control parallelization
+    const int multiplier_limit = compute_multiplier_limit_conv2d<CONFIG_T>(weights);
+    #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
+
+    // Convolve, saving all multiplication results to accumulate later
+    ConvOutHeight: for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        ConvOutWidth: for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            ConvFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++){
+                ConvChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++){
+                    ConvFiltHeight: for(int fh = 0; fh < CONFIG_T::filt_height; fh++){
+                        ConvFiltWidth: for(int fw = 0; fw < CONFIG_T::filt_width; fw++){
+
+                            int index_mult = oh*CONFIG_T::out_width*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + ow*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + ff*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + cc*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + fh*CONFIG_T::filt_width
+                                           + fw;
+
+                                int index_weight = fh*CONFIG_T::filt_width*CONFIG_T::n_chan*CONFIG_T::n_filt
+                                                 + fw*CONFIG_T::n_chan*CONFIG_T::n_filt
+                                                 + cc*CONFIG_T::n_filt
+                                                 + ff;
+
+                                if ((oh*CONFIG_T::stride_height+fh) < CONFIG_T::pad_top
+                                || (oh*CONFIG_T::stride_height+fh) >= (CONFIG_T::pad_top+CONFIG_T::in_height)
+                                || (ow*CONFIG_T::stride_width+fw) < CONFIG_T::pad_left
+                                || (ow*CONFIG_T::stride_width+fw) >= (CONFIG_T::pad_left+CONFIG_T::in_width)) {
+                                    mult[index_mult] = 0;
+                                } else {
+                                    int index_data = (oh*CONFIG_T::stride_height+fh-CONFIG_T::pad_top)*CONFIG_T::in_width*CONFIG_T::n_chan
+                                                   + (ow*CONFIG_T::stride_width+fw-CONFIG_T::pad_left)*CONFIG_T::n_chan
+                                                   + cc;
+                                    mult[index_mult] = data[index_data] * weights[index_weight];
+                                }
+
+                        }//end mult loop
+                    }//end channel loop
+                  }//end filter width loop
+            }//end filter height loop
+        }//end output width loop
+    }//end output height loop
+
+
+    // Initialize accumulator with input biases
+    for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+                acc[oh*CONFIG_T::out_width*CONFIG_T::n_filt + ow*CONFIG_T::n_filt + ff]=biases[ff];
+            }
+        }
+    }
+
+
+    // Accumulate multiplication result
+    AccumOutHeight: for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        AccumOutWidth: for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            AccumFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+                //Do "dot product" sum within filter and sum over channels
+                AccumChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++){
+                    AccumDotHeight: for(int fh = 0; fh < CONFIG_T::filt_height; fh++){
+                        AccumDotWidth: for(int fw = 0; fw < CONFIG_T::filt_width; fw++){
+
+                            int index_mult = oh*CONFIG_T::out_width*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + ow*CONFIG_T::n_filt*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + ff*CONFIG_T::n_chan*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + cc*CONFIG_T::filt_height*CONFIG_T::filt_width
+                                           + fh*CONFIG_T::filt_width
+                                           + fw;
+                            int index_acc = oh*CONFIG_T::out_width*CONFIG_T::n_filt
+                                          + ow*CONFIG_T::n_filt
+                                          + ff;
+
+                            acc[index_acc] += mult[index_mult];
+
+                        }//end dot product filter width loop
+                    }//end dot product filter height loop
+                }//end n channel loop
+            }//end n filter loop
+        }//end output width loop
+    }//end output height loop
+
+    // Cast to "res_t" type
+    for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+              for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+                int index = oh*CONFIG_T::out_width*CONFIG_T::n_filt + ow*CONFIG_T::n_filt + ff;
+                res[index] = (res_T)(acc[index]);
+            }
+        }
+    }
+
+}//end conv2d
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pointwise_conv_2d_cl(
+    data_T data[CONFIG_T::in_height*CONFIG_T::in_width*CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_height*CONFIG_T::out_width*CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+
+    typename CONFIG_T::accum_t mult[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt * CONFIG_T::n_chan];
+    typename CONFIG_T::accum_t acc[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt];
+
+    #pragma HLS ARRAY_PARTITION variable=mult complete dim=0
+    #pragma HLS ARRAY_PARTITION variable=acc complete dim=0
+
+    // Use a function_instantiate in case it helps to explicitly optimize unchanging weights/biases
+    #pragma HLS function_instantiate variable=weights,biases
+
+    // Parallel mode
+    #pragma HLS PIPELINE
+    #pragma HLS ARRAY_PARTITION variable=biases complete dim=0
+
+    // Limit multipliers to control parallelization
+    const int multiplier_limit = compute_multiplier_limit_conv2d<CONFIG_T>(weights);
+    #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
+
+    // Convolve, saving all multiplication results to accumulate later
+    ConvOutHeight: for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        ConvOutWidth: for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            ConvFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+                ConvChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++) {
+
+                    int index_mult = oh*CONFIG_T::out_width*CONFIG_T::n_filt*CONFIG_T::n_chan
+                                   + ow*CONFIG_T::n_filt*CONFIG_T::n_chan
+                                   + ff*CONFIG_T::n_chan
+                                   + cc;
+
+                    int index_weight = cc*CONFIG_T::n_filt + ff;
+
+                    if ((oh*CONFIG_T::stride_height) < CONFIG_T::pad_top
+                    || (oh*CONFIG_T::stride_height) >= (CONFIG_T::pad_top+CONFIG_T::in_height)
+                    || (ow*CONFIG_T::stride_width) < CONFIG_T::pad_left
+                    || (ow*CONFIG_T::stride_width) >= (CONFIG_T::pad_left+CONFIG_T::in_width)) {
+                        mult[index_mult] = 0;
+                    } else {
+                        int index_data = (oh*CONFIG_T::stride_height-CONFIG_T::pad_top)*CONFIG_T::in_width*CONFIG_T::n_chan
+                                       + (ow*CONFIG_T::stride_width-CONFIG_T::pad_left)*CONFIG_T::n_chan
+                                       + cc;
+                        mult[index_mult] = data[index_data] * weights[index_weight];
+                    }
+
+                }
+            }
+        }
+    }
+
+
+    // Initialize accumulator with input biases
+    for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+                acc[oh*CONFIG_T::out_width*CONFIG_T::n_filt + ow*CONFIG_T::n_filt + ff]=biases[ff];
+            }
+        }
+    }
+
+
+    // Accumulate multiplication result
+    AccumOutHeight: for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        AccumOutWidth: for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+            AccumFilt: for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+                //Do "dot product" sum within filter and sum over channels
+                AccumChan: for(int cc = 0; cc < CONFIG_T::n_chan; cc++) {
+
+                    int index_mult = oh*CONFIG_T::out_width*CONFIG_T::n_filt*CONFIG_T::n_chan
+                                    + ow*CONFIG_T::n_filt*CONFIG_T::n_chan
+                                    + ff*CONFIG_T::n_chan
+                                    + cc;
+                    int index_acc = oh*CONFIG_T::out_width*CONFIG_T::n_filt
+                                    + ow*CONFIG_T::n_filt
+                                    + ff;
+
+                    acc[index_acc] += mult[index_mult];
+
+                }
+            }
+        }
+    }
+
+    // Cast to "res_t" type
+    for(int oh = 0; oh < CONFIG_T::out_height; oh++) {
+        for(int ow = 0; ow < CONFIG_T::out_width; ow++) {
+              for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+                int index = oh*CONFIG_T::out_width*CONFIG_T::n_filt + ow*CONFIG_T::n_filt + ff;
+                res[index] = (res_T)(acc[index]);
+            }
+        }
+    }
+
+}//end conv2d
+
+}
+#endif
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d_resource.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d_resource.h
new file mode 100644
index 0000000..a251051
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d_resource.h
@@ -0,0 +1,216 @@
+#ifndef NNET_CONV2D_RESOURCE_H_
+#define NNET_CONV2D_RESOURCE_H_
+
+#include "nnet_common.h"
+#include "nnet_dense.h"
+
+namespace nnet {
+
+template<class data_T, typename CONFIG_T>
+void im2col_2d(
+    data_T data[CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_chan],
+    data_T data_col[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::out_height * CONFIG_T::out_width])
+{
+    const int output_h = (CONFIG_T::in_height + CONFIG_T::pad_top + CONFIG_T::pad_bottom -
+        (CONFIG_T::dilation_height * (CONFIG_T::filt_height - 1) + 1)) / CONFIG_T::stride_height + 1;
+    const int output_w = (CONFIG_T::in_width + CONFIG_T::pad_left + CONFIG_T::pad_right -
+        (CONFIG_T::dilation_width * (CONFIG_T::filt_width - 1) + 1)) / CONFIG_T::stride_width + 1;
+    const int channel_size = CONFIG_T::in_height * CONFIG_T::in_width;
+
+    for (int channel = CONFIG_T::n_chan; channel--; data += channel_size) {
+        for (int kernel_row = 0; kernel_row < CONFIG_T::filt_height; kernel_row++) {
+            for (int kernel_col = 0; kernel_col < CONFIG_T::filt_width; kernel_col++) {
+                int input_row = -CONFIG_T::pad_top + kernel_row * CONFIG_T::dilation_height;
+                for (int output_rows = output_h; output_rows; output_rows--) {
+                    if (input_row < 0 || input_row > CONFIG_T::in_height) {
+                        for (int output_cols = output_w; output_cols; output_cols--) {
+                            *(data_col++) = 0;
+                        }
+                    } else {
+                        int input_col = -CONFIG_T::pad_left + kernel_col * CONFIG_T::dilation_width;
+                        for (int output_col = output_w; output_col; output_col--) {
+                            if (input_col >= 0 && input_col < CONFIG_T::in_width) {
+                                *(data_col++) = data[input_row * CONFIG_T::in_width + input_col];
+                            } else {
+                                *(data_col++) = 0;
+                            }
+                            input_col += CONFIG_T::stride_width;
+                        }
+                    }
+                    input_row += CONFIG_T::stride_height;
+                }
+            }
+        }
+    }
+}
+
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_2d_full(
+    data_T data[CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    data_T data_conv[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::out_height * CONFIG_T::out_width];
+    data_T data_col[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan];
+    res_T res_col[CONFIG_T::n_filt];
+
+    //#pragma HLS ARRAY_PARTITION variable=data_conv complete
+    #pragma HLS ARRAY_PARTITION variable=data_col complete
+    #pragma HLS ARRAY_PARTITION variable=res_col complete
+
+    im2col_2d<data_T, CONFIG_T>(data, data_conv);
+
+    for (int i = 0; i < CONFIG_T::out_height * CONFIG_T::out_width; i++) {
+        for (int j = 0; j < CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan; j++) {
+            data_col[j] = data[j * CONFIG_T::out_height * CONFIG_T::out_width + i];
+        }
+        dense<data_T, res_T, typename CONFIG_T::mult_config>(data_col, res_col, weights, biases);
+        for (int j = 0; j < CONFIG_T::n_filt; j++) {
+            //res[i * CONFIG_T::n_filt + j] = res_col[j];
+            res[j * CONFIG_T::out_height * CONFIG_T::out_width + i] = res_col[j]; // Transposed order
+        }
+    }
+}
+
+template<class data_T, typename CONFIG_T>
+void im2col_2d_cf(
+    data_T data[CONFIG_T::n_chan * CONFIG_T::in_height * CONFIG_T::in_width],
+    data_T data_col[CONFIG_T::n_chan * CONFIG_T::filt_height * CONFIG_T::filt_width],
+    const int row,
+    const int col)
+{
+    const int channel_size = CONFIG_T::in_height * CONFIG_T::in_width;
+    int index = 0;
+    for (int channel = CONFIG_T::n_chan; channel--; data += channel_size) {
+        #pragma HLS UNROLL
+        for (int kernel_row = 0; kernel_row < CONFIG_T::filt_height; kernel_row++) {
+            int input_row = -CONFIG_T::pad_top + kernel_row * CONFIG_T::dilation_height + row * CONFIG_T::stride_height;
+            for (int kernel_col = 0; kernel_col < CONFIG_T::filt_width; kernel_col++) {
+                if (input_row < 0 || input_row > CONFIG_T::in_height) {
+                    data_col[index++] = 0;
+                } else {
+                    int input_col = -CONFIG_T::pad_left + kernel_col * CONFIG_T::dilation_width + col * CONFIG_T::stride_width;
+                    if (input_col >= 0 && input_col < CONFIG_T::in_width) {
+                        //*(data_col++) = data[input_row * CONFIG_T::in_width + input_col];
+                        data_col[index++] = data[input_row * CONFIG_T::in_width + input_col];
+                    } else {
+                        //*(data_col++) = 0;
+                        data_col[index++] = 0;
+                    }
+                    input_col += CONFIG_T::stride_width;
+                }
+            }
+            input_row += CONFIG_T::stride_height;
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_2d_resource_cf(
+    data_T data[CONFIG_T::n_chan * CONFIG_T::in_height * CONFIG_T::in_width],
+    res_T  res[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    const int nin = CONFIG_T::n_chan * CONFIG_T::filt_width;
+    const int nout = CONFIG_T::n_filt;
+    const int rufactor = CONFIG_T::reuse_factor;
+    const int block_factor = DIV_ROUNDUP(nin*nout, rufactor);
+
+    //#pragma HLS function_instantiate variable=weights,biases
+    //#pragma HLS RESOURCE         variable=weights core=RAM_2P_BRAM Commenting out the deisgnation HLS seems to choose correctly
+    //#pragma HLS ARRAY_RESHAPE   variable=weights block factor=block_factor
+    //#pragma HLS ARRAY_PARTITION variable=biases complete
+
+    data_T data_col[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan];
+    res_T res_col[CONFIG_T::n_filt];
+
+    #pragma HLS ARRAY_PARTITION variable=data_col complete
+    #pragma HLS ARRAY_PARTITION variable=res_col complete
+    
+    HeightLoop:
+    for (int i = 0; i < CONFIG_T::out_height; i++) {
+        WidthLoop:
+        for (int j = 0; j < CONFIG_T::out_width; j++) {
+            #pragma HLS PIPELINE
+            im2col_2d_cf<data_T, CONFIG_T>(data, data_col, i, j);
+            dense<data_T, res_T, typename CONFIG_T::mult_config>(data_col, res_col, weights, biases);
+            FiltLoop:
+            for (int k = 0; k < CONFIG_T::n_filt; k++) {
+                //res[i * CONFIG_T::out_width * CONFIG_T::n_filt + j * CONFIG_T::n_filt + k] = res_col[k];
+                res[k * CONFIG_T::out_height * CONFIG_T::out_width + i * CONFIG_T::out_width + j] = res_col[k]; // Transposed order
+            }
+        }
+    }
+}
+
+template<class data_T, typename CONFIG_T>
+void im2col_2d_cl(
+    data_T data[CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_chan],
+    data_T data_col[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan],
+    const int row,
+    const int col)
+{
+    int index = 0;
+    for (int kernel_row = 0; kernel_row < CONFIG_T::filt_height; kernel_row++) {
+        #pragma HLS UNROLL
+        int input_row = -CONFIG_T::pad_top + kernel_row * CONFIG_T::dilation_height + row * CONFIG_T::stride_height;
+        for (int kernel_col = 0; kernel_col < CONFIG_T::filt_width; kernel_col++) {
+            for (int channel = 0; channel < CONFIG_T::n_chan; channel++) {
+                if (input_row < 0 || input_row >= CONFIG_T::in_height) {
+                    data_col[index++] = 0;
+                } else {
+                    int input_col = -CONFIG_T::pad_left + kernel_col * CONFIG_T::dilation_width + col * CONFIG_T::stride_width;
+                    if (input_col >= 0 && input_col < CONFIG_T::in_width) {
+                        data_col[index++] = data[input_row * CONFIG_T::in_width * CONFIG_T::n_chan + input_col * CONFIG_T::n_chan + channel];
+                    } else {
+                        data_col[index++] = 0;
+                    }
+                }
+            }
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_2d_resource_cl(
+    data_T data[CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_chan],
+    res_T  res[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt],
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    const int nin = CONFIG_T::n_chan * CONFIG_T::filt_width;
+    const int nout = CONFIG_T::n_filt;
+    const int rufactor = CONFIG_T::reuse_factor;
+    const int block_factor = DIV_ROUNDUP(nin*nout, rufactor);
+
+    //#pragma HLS function_instantiate variable=weights,biases
+    //#pragma HLS RESOURCE         variable=weights core=RAM_2P_BRAM Commenting out the deisgnation HLS seems to choose correctly
+    //#pragma HLS ARRAY_RESHAPE   variable=weights block factor=block_factor
+    //#pragma HLS ARRAY_PARTITION variable=biases complete
+
+    data_T data_col[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan];
+    res_T res_col[CONFIG_T::n_filt];
+
+    #pragma HLS ARRAY_PARTITION variable=data_col complete
+    #pragma HLS ARRAY_PARTITION variable=res_col complete
+    
+    HeightLoop:
+    for (int i = 0; i < CONFIG_T::out_height; i++) {
+        WidthLoop:
+        for (int j = 0; j < CONFIG_T::out_width; j++) {
+            #pragma HLS PIPELINE
+            im2col_2d_cl<data_T, CONFIG_T>(data, data_col, i, j);
+            dense<data_T, res_T, typename CONFIG_T::mult_config>(data_col, res_col, weights, biases);
+            FiltLoop:
+            for (int k = 0; k < CONFIG_T::n_filt; k++) {
+                res[i * CONFIG_T::out_width * CONFIG_T::n_filt + j * CONFIG_T::n_filt + k] = res_col[k];
+            }
+        }
+    }
+}
+
+}
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d_stream.h
new file mode 100644
index 0000000..5d1c7d1
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv2d_stream.h
@@ -0,0 +1,112 @@
+#ifndef NNET_CONV2D_STREAM_H_
+#define NNET_CONV2D_STREAM_H_
+
+#include "ap_shift_reg.h"
+#include "nnet_common.h"
+#include "nnet_conv_stream.h"
+#include "hls_stream.h"
+
+namespace nnet {
+
+template<class data_T, typename CONFIG_T>
+void compute_scaled_indices_2d(
+    const unsigned h_idx,
+    const unsigned w_idx,
+    ap_uint<CONFIG_T::filt_height * CONFIG_T::filt_width> *pixel_idx
+) {
+    const unsigned sh_idx = scale_index<CONFIG_T::filt_height, CONFIG_T::stride_height, CONFIG_T::in_height>(h_idx);
+    unsigned wp_idx = w_idx * (data_T::size / CONFIG_T::n_chan);
+
+    ComputeIndex: for (unsigned p = 0; p < data_T::size / CONFIG_T::n_chan; p++) {
+        #pragma HLS UNROLL
+
+        unsigned sw_idx = scale_index<CONFIG_T::filt_width, CONFIG_T::stride_width, CONFIG_T::in_width>(wp_idx + p);
+        pixel_idx[p] = CONFIG_T::pixels[sh_idx * CONFIG_T::min_width + sw_idx];
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void conv_2d_encoded_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    assert(CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    assert(CONFIG_T::filt_height == CONFIG_T::filt_width);
+
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan];
+    const int win_depth = CONFIG_T::filt_height * CONFIG_T::out_width;
+    for (unsigned i_out = 0; i_out < CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan; i_out++) {
+        #pragma HLS STREAM variable=data_window[i_out] depth=win_depth
+    }
+
+    #pragma HLS ARRAY_PARTITION variable=CONFIG_T::pixels complete
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+    unsigned outputs_ready = 0;
+
+    ap_uint<CONFIG_T::filt_height * CONFIG_T::filt_width> pixel_idx[data_T::size / CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=pixel_idx complete
+
+    ReadInputHeight: for (unsigned i_ih = 0; i_ih < CONFIG_T::in_height; i_ih++) {
+        ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width / (data_T::size / CONFIG_T::n_chan); i_iw++) {
+            #pragma HLS LOOP_FLATTEN
+            if (CONFIG_T::strategy == nnet::latency && data_T::size / CONFIG_T::n_chan == 1) {
+                #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+            }
+            compute_scaled_indices_2d<data_T, CONFIG_T>(i_ih, i_iw, pixel_idx);
+            compute_output_encoded<data_T, res_T, CONFIG_T>(data.read(), data_window, res, res_pack, outputs_ready, weights, biases, pixel_idx);
+        }
+    }
+}
+
+// Line Buffer
+template <class data_T, class res_T, typename CONFIG_T>
+void conv_2d_buffer_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    assert(CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+
+    static ap_shift_reg<typename data_T::value_type, CONFIG_T::in_width> line_buffer[MAX(CONFIG_T::filt_height - 1,1)][CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable = line_buffer complete dim = 2
+
+    ReadInputHeight: for (unsigned i_ih = 0; i_ih < CONFIG_T::in_height; i_ih++) {
+        ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width; i_iw++) {
+            #pragma HLS LOOP_FLATTEN
+            if(CONFIG_T::strategy == nnet::latency) {
+                #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+            }
+            if (CONFIG_T::filt_height > 1) {
+                compute_output_buffer_2d<data_T, res_T, CONFIG_T>(data.read(), line_buffer, res, weights, biases);
+            } else {
+                compute_output_buffer_1d<data_T, res_T, CONFIG_T>(data.read(), res, weights, biases);
+            }
+        }
+    }
+}
+
+template <class data_T, class res_T, typename CONFIG_T>
+void conv_2d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    #pragma HLS inline region
+    switch(CONFIG_T::implementation){
+        case conv_implementation::linebuffer:
+            conv_2d_buffer_cl<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+            break;
+        case conv_implementation::encoded:
+            conv_2d_encoded_cl<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+            break;
+    }  
+}
+
+}
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv_stream.h
new file mode 100644
index 0000000..862e836
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_conv_stream.h
@@ -0,0 +1,375 @@
+#ifndef NNET_CONV_STREAM_H_
+#define NNET_CONV_STREAM_H_
+
+#include "ap_shift_reg.h"
+#include "nnet_common.h"
+#include "hls_stream.h"
+#include "nnet_dense.h"
+
+namespace nnet {
+
+enum class conv_implementation { linebuffer=0, encoded=1};
+
+// *************************************************
+//       Encoded Implementation (Vlad's)
+// *************************************************
+template<unsigned K, unsigned S, unsigned W>
+unsigned scale_index_K_gte_S(const unsigned idx) {
+    #pragma HLS INLINE
+
+    if (idx < K - S) {
+        return idx;
+    }
+
+    constexpr unsigned nW = ((W - K) / S) * S + K; // Nearest W without unused pixels on the right
+    constexpr unsigned sW = (DIV_ROUNDUP(K, S) - 1) * S + K; // Scaled W that behaves like original W
+    if (idx >= nW) {
+        return sW;
+    }
+
+    const unsigned r = nW - idx;
+    if (r <= K - S) {
+        return sW - r;
+    }
+
+    return K - S + (idx - (K - S)) % S;
+}
+
+template<unsigned K, unsigned S, unsigned W>
+unsigned scale_index_K_lt_S(const unsigned idx) {
+    #pragma HLS INLINE
+
+    if (idx < S - K) {
+        return idx;
+    }
+
+    constexpr unsigned nW = ((W - K) / S) * S + K; // Nearest W without unused pixels on the right
+    constexpr unsigned sW = (DIV_ROUNDUP(S, K) - 1) * S + K; // Scaled W that behaves like original W
+    if (idx >= nW) {
+        return sW;
+    }
+
+    const unsigned r = nW - idx;
+    if (r <= S - K) {
+        return sW - r;
+    }
+
+    return S - K + (idx - (S - K)) % S;
+}
+
+template<unsigned K, unsigned S, unsigned W>
+unsigned scale_index(const unsigned idx) {
+    #pragma HLS INLINE
+    
+    if (K >= S) {
+        return scale_index_K_gte_S<K, S, W>(idx);
+    } else {
+        return scale_index_K_lt_S<K, S, W>(idx);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void mult_buffer(
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    res_T& res_pack,
+    hls::stream<res_T>& res_stream,
+    unsigned & outputs_ready,
+    typename CONFIG_T::weight_t weights[CONFIG_T::kernel_size * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_filt]
+) {
+    #pragma HLS INLINE
+
+    typename data_T::value_type data[CONFIG_T::kernel_size * CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=data complete
+    typename res_T::value_type res[CONFIG_T::n_filt];
+    #pragma HLS ARRAY_PARTITION variable=res complete
+
+    InitData: for (int id = 0; id < CONFIG_T::kernel_size * CONFIG_T::n_chan; id++) {
+        #pragma HLS UNROLL
+        data[id] = data_window[id].read();
+    }
+
+    #pragma HLS INLINE region
+    if (CONFIG_T::strategy == nnet::latency) {
+        dense_latency<typename data_T::value_type, typename res_T::value_type, typename CONFIG_T::mult_config>(data, res, weights, biases);
+    } else {
+        dense_resource<typename data_T::value_type, typename res_T::value_type, typename CONFIG_T::mult_config>(data, res, weights, biases);
+    }
+
+    CastLoop: for (unsigned jj = 0; jj < CONFIG_T::n_filt; jj++) {
+        #pragma HLS UNROLL
+        if (res_T::size / CONFIG_T::n_filt == 1) {
+            res_pack[jj] = res[jj];
+        } else {
+            res_pack[outputs_ready * CONFIG_T::n_filt + jj] = res[jj];
+        }
+    }
+
+    if (res_T::size / CONFIG_T::n_filt == 1) {
+        res_stream.write(res_pack);
+    } else {
+        if (outputs_ready == (res_T::size / CONFIG_T::n_filt) - 1) {
+            res_stream.write(res_pack);
+            outputs_ready = 0;
+        } else {
+            outputs_ready++;
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_output_encoded(
+    const data_T& in_elem,
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    hls::stream<res_T> &res,
+    res_T &res_pack,
+    unsigned &outputs_ready,
+    typename CONFIG_T::weight_t weights[CONFIG_T::kernel_size * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_filt],
+    ap_uint<CONFIG_T::kernel_size> *pixel_idx
+) {
+    #pragma HLS INLINE
+
+    MultLoop: for (unsigned p = 0; p < data_T::size / CONFIG_T::n_chan; p++) {
+        #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+        CopyDataFilt: for (unsigned f = 0; f < CONFIG_T::kernel_size; f++) {
+            #pragma HLS UNROLL
+            CopyDataChan: for (unsigned c = 0; c < CONFIG_T::n_chan; c++) {
+                #pragma HLS UNROLL
+                if (pixel_idx[p][f]) data_window[f * CONFIG_T::n_chan + c].write(in_elem[p * CONFIG_T::n_chan + c]);
+            }
+        }
+        if (pixel_idx[p][CONFIG_T::kernel_size - 1]) {
+            mult_buffer<data_T, res_T, CONFIG_T>(data_window, res_pack, res, outputs_ready, weights, biases);
+        }
+    }
+}
+
+
+
+// *************************************************
+//       Line Buffer Implementation (Phil's)
+// *************************************************
+template <class data_T, typename CONFIG_T>
+void kernel_shift_1d(
+    const data_T& in_elem,
+    typename data_T::value_type kernel_window[CONFIG_T::filt_width * CONFIG_T::n_chan]
+) {
+    #pragma HLS inline
+    #pragma HLS PIPELINE II = 1
+    
+    // Shift kernel_window by one step to the left (manual shift operation)
+    static const int filt_width = CONFIG_T::filt_width - 1;
+    KernelShiftWidth: for (int i_iw = 0; i_iw < filt_width; i_iw++) {
+        #pragma HLS UNROLL
+        KernelShiftChannel: for (unsigned i_ic = 0; i_ic < CONFIG_T::n_chan; i_ic++) {
+            // Shift every element in kernel_window to the left
+            kernel_window[i_iw * CONFIG_T::n_chan + i_ic] = kernel_window[(i_iw + 1) * CONFIG_T::n_chan + i_ic];
+        }
+    }
+
+    // Insert shift_buffer column into right-most column of kernel
+    static const int lastheight = (CONFIG_T::filt_width - 1) * CONFIG_T::n_chan;
+    KernelPushChannel: for (int i_ic = 0; i_ic < CONFIG_T::n_chan; i_ic++) {
+        #pragma HLS UNROLL
+        kernel_window[lastheight + i_ic] = in_elem[i_ic];
+    }
+}
+
+template <class data_T, typename CONFIG_T>
+void kernel_shift_2d(
+    typename data_T::value_type shift_buffer[CONFIG_T::filt_height][CONFIG_T::n_chan],
+    typename data_T::value_type kernel_window[CONFIG_T::filt_width * CONFIG_T::filt_height * CONFIG_T::n_chan]
+) {
+    #pragma HLS inline
+        
+    // Shift kernel_window by one step to the left (manual shift operation)
+    static const int filt_width = CONFIG_T::filt_width - 1;
+    KernelShiftWidth: for (int i_iw = 0; i_iw < filt_width; i_iw++) {
+        #pragma HLS PIPELINE II = 1
+        KernelShiftHeight: for (unsigned i_ih = 0; i_ih < CONFIG_T::filt_height; i_ih++) {
+            KernelShiftChannel: for (unsigned i_ic = 0; i_ic < CONFIG_T::n_chan; i_ic++) {
+            // Shift every element in kernel_window to the left
+                kernel_window[i_ih * CONFIG_T::filt_width * CONFIG_T::n_chan + i_iw * CONFIG_T::n_chan + i_ic] = kernel_window[i_ih * CONFIG_T::filt_width * CONFIG_T::n_chan + (i_iw + 1) * CONFIG_T::n_chan + i_ic];
+            }
+        }
+    }
+
+    // Insert shift_buffer column into right-most column of kernel
+    static const int lastheight = (CONFIG_T::filt_width - 1) * CONFIG_T::n_chan;
+    KernelPushHeight: for (int i_ih = 0; i_ih < CONFIG_T::filt_height; i_ih++) {
+        #pragma HLS UNROLL
+        KernelPushChannel: for (int i_ic = 0; i_ic < CONFIG_T::n_chan; i_ic++) {
+            kernel_window[lastheight + i_ih * CONFIG_T::filt_width * CONFIG_T::n_chan + i_ic] = shift_buffer[i_ih][i_ic];
+        }
+    }
+}
+
+template <class data_T, typename CONFIG_T>
+void shift_line_buffer(const data_T& in_elem, 
+                    ap_shift_reg<typename data_T::value_type, CONFIG_T::in_width> line_buffer[MAX(CONFIG_T::filt_height - 1,1)][CONFIG_T::n_chan],
+                    typename data_T::value_type kernel_window[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan]
+) {
+    
+    #pragma HLS PIPELINE
+
+    // Temporary buffer for popped (shifted) elements
+    typename data_T::value_type shift_buffer[CONFIG_T::filt_height][CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable = shift_buffer complete dim = 0
+
+    UpdateBuffer: for (int i_ic = 0; i_ic < CONFIG_T::n_chan; i_ic++) {
+        #pragma HLS UNROLL
+
+        // Insert pixel(s) at end of shift buffer
+        shift_buffer[CONFIG_T::filt_height - 1][i_ic] = in_elem[i_ic];
+    }
+
+    LineBufferDataIn: for (int i_ic = 0; i_ic < CONFIG_T::n_chan; i_ic++) {
+        // Shift the shift buffer into the line buffer
+        LineBufferShift: for (unsigned i_ih = 1; i_ih < CONFIG_T::filt_height; i_ih++) {
+            #pragma HLS UNROLL
+            typename data_T::value_type pop_elem = line_buffer[i_ih - 1][i_ic].shift(shift_buffer[CONFIG_T::filt_height - i_ih][i_ic]); // Shift the line buffer, return the popped pixel
+            shift_buffer[CONFIG_T::filt_height - i_ih - 1][i_ic] = pop_elem; // Popped element placed back into shift_buffer, one row up.
+        }
+    }
+    kernel_shift_2d<data_T, CONFIG_T>(shift_buffer, kernel_window);
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_output_buffer_2d(
+    const data_T& in_elem,
+    ap_shift_reg<typename data_T::value_type, CONFIG_T::in_width> line_buffer[MAX(CONFIG_T::filt_height - 1,1)][CONFIG_T::n_chan],
+    hls::stream<res_T> &res_stream,
+    typename CONFIG_T::weight_t weights[CONFIG_T::kernel_size * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_filt]
+) {
+    #pragma HLS INLINE
+
+    // Thresholds
+    const static int lShiftX = CONFIG_T::filt_width - 1;
+    const static int lShiftY = CONFIG_T::filt_height - 1;
+
+    // Counters
+    static int pX = 0; // Pixel X
+    static int pY = 0; // Pixel Y
+
+    static int sX = 0; // Stride X
+    static int sY = 0; // Stride Y
+
+    static typename data_T::value_type kernel_data[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=kernel_data complete
+
+    typename res_T::value_type res_out[CONFIG_T::n_filt];
+    #pragma HLS ARRAY_PARTITION variable=res_out complete dim = 0
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+
+    // Add pixel to buffer
+    nnet::shift_line_buffer<data_T, CONFIG_T>(in_elem, line_buffer, kernel_data);
+
+    // Check to see if we have a full kernel
+    if ( (sX - lShiftX) == 0 && (sY - lShiftY) == 0 && pY > lShiftY - 1 && pX > lShiftX - 1) {
+        
+        // Dense multiply
+        #pragma HLS INLINE region
+        if (CONFIG_T::strategy == nnet::latency) {
+            dense_latency<typename data_T::value_type, typename res_T::value_type, typename CONFIG_T::mult_config>(kernel_data, res_out, weights, biases);
+        } else {
+            dense_resource<typename data_T::value_type, typename res_T::value_type, typename CONFIG_T::mult_config>(kernel_data, res_out, weights, biases);
+        }
+
+        // Pack output
+        CastLoop: for (unsigned i_ic = 0; i_ic < CONFIG_T::n_filt; i_ic++) {
+            #pragma HLS UNROLL
+            res_pack[i_ic] = res_out[i_ic];
+        }
+
+        // Write output to stream when output ready
+        res_stream.write(res_pack);
+    }
+
+    // Counter Housekeeping
+    if (pX + 1 == CONFIG_T::in_width)  // Includes padding, end of line (padded)
+    {
+        pX = 0; 
+        sX = 0;
+        if (pY + 1 == CONFIG_T::in_height) {  // Reached bottom of image
+            pY = 0; 
+            sY = 0;
+        } else {
+            pY = pY + 1;
+            // Update stride (threshold) ? subtract stride : increment stride
+            sY = ((sY - lShiftY) == 0) ? sY - CONFIG_T::stride_height + 1 : sY + 1; 
+        }
+    } else {
+        pX = pX + 1;
+        // Update stride (threshold) ? subtract stride : increment stride
+        sX = ((sX - lShiftX) == 0) ? sX - CONFIG_T::stride_width + 1 : sX + 1; 
+    }
+}
+
+// Conv 1D compute output
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_output_buffer_1d(
+    const data_T& in_elem,
+    hls::stream<res_T> &res_stream,
+    typename CONFIG_T::weight_t weights[CONFIG_T::kernel_size * CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_filt]
+) {
+    #pragma HLS INLINE
+
+    // Thresholds
+    const static int lShiftX = CONFIG_T::filt_width - 1;
+
+    // Counters
+    static int pX = 0; // pixel counter
+    static int sX = 0; // stride counter
+
+    static typename data_T::value_type kernel_data[CONFIG_T::filt_width * CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=kernel_data complete
+
+    typename res_T::value_type res_out[CONFIG_T::n_filt];
+    #pragma HLS ARRAY_PARTITION variable=res_out complete dim = 0
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+
+    // Add pixel to buffer
+    nnet::kernel_shift_1d<data_T, CONFIG_T>(in_elem, kernel_data);
+
+    // Check to see if we have a full kernel
+    if ( (sX - lShiftX) == 0 && pX > lShiftX - 1 ) {
+        
+        // Dense multiply
+        #pragma HLS INLINE region
+        if (CONFIG_T::strategy == nnet::latency) {
+            dense_latency<typename data_T::value_type, typename res_T::value_type, typename CONFIG_T::mult_config>(kernel_data, res_out, weights, biases);
+        } else {
+            dense_resource<typename data_T::value_type, typename res_T::value_type, typename CONFIG_T::mult_config>(kernel_data, res_out, weights, biases);
+        }
+
+        // Pack output
+        CastLoop: for (unsigned i_ic = 0; i_ic < CONFIG_T::n_filt; i_ic++) {
+            #pragma HLS UNROLL
+            res_pack[i_ic] = res_out[i_ic];
+        }
+
+        // Write output to stream when output ready
+        res_stream.write(res_pack);
+    }
+
+    // Counter Housekeeping
+    if (pX + 1 == CONFIG_T::in_width)  // Includes padding, end of line (padded)
+    {
+        pX = 0;
+        sX = 0;
+    } else {
+        pX = pX + 1;
+        // Update stride (threshold) ? subtract stride : increment stride
+        sX = ((sX - lShiftX) == 0) ? sX - CONFIG_T::stride_width + 1 : sX + 1; 
+    }
+}
+
+}
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense.h
new file mode 100644
index 0000000..deb1c04
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense.h
@@ -0,0 +1,54 @@
+#ifndef NNET_DENSE_H_
+#define NNET_DENSE_H_
+
+#include "nnet_common.h"
+#include "nnet_mult.h"
+#include "nnet_dense_latency.h"
+#include "nnet_dense_resource.h"
+#include "nnet_helpers.h"
+#include "hls_stream.h"
+#include <math.h>
+
+namespace nnet {
+
+struct dense_config
+{
+    // Internal data type definitions
+    typedef float bias_t;
+    typedef float weight_t;
+    typedef float accum_t;
+
+    // Layer Sizes
+    static const unsigned n_in = 10;
+    static const unsigned n_out = 10;
+
+    // Resource reuse info
+    static const unsigned io_type = io_parallel;
+    static const unsigned strategy = latency; 
+    static const unsigned reuse_factor = 1;
+    static const bool store_weights_in_bram = false;
+    static const unsigned n_zeros = 0;
+    // partitioning arrays cyclically to go with roll factors?
+    // Product function to use
+    template<class x_T, class y_T, class res_T>
+    using product = nnet::product::mult<x_T, y_T, res_T>;
+};
+
+template<class data_T, class res_T, typename CONFIG_T>
+void dense(
+    data_T    data[CONFIG_T::n_in],
+    res_T     res[CONFIG_T::n_out],
+    typename CONFIG_T::weight_t  weights[CONFIG_T::n_in*CONFIG_T::n_out],
+    typename CONFIG_T::bias_t    biases[CONFIG_T::n_out])
+{
+    #pragma HLS inline
+    if (CONFIG_T::strategy == nnet::latency) {
+        dense_latency<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    } else {
+        dense_resource<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_compressed.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_compressed.h
new file mode 100644
index 0000000..adfaa0e
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_compressed.h
@@ -0,0 +1,109 @@
+//
+//    hls4ml: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2018 Giuseppe Di Guglielmo
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_COMPRESSED_LAYER_H_
+#define NNET_COMPRESSED_LAYER_H_
+
+#include "nnet_common.h"
+#include "nnet_dense.h"
+#include "hls_stream.h"
+#include <math.h>
+
+namespace nnet {
+
+template<typename CONFIG_T>
+void fill_mult(typename CONFIG_T::index_t index,
+        typename CONFIG_T::accum_t mult[CONFIG_T::n_out],
+        typename CONFIG_T::accum_t weight) {
+    for(unsigned  k = 0; k < CONFIG_T::n_out; k++) {
+        #pragma HLS UNROLL
+        if (k == index) mult[k] += weight;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void dense_compressed(
+        data_T    data[CONFIG_T::n_in],
+        res_T     res[CONFIG_T::n_out],
+        typename CONFIG_T::weight_t  weights[CONFIG_T::n_nonzeros],
+        typename CONFIG_T::bias_t    biases[CONFIG_T::n_out])
+{
+
+    const int multiplier_limit = DIV_ROUNDUP(CONFIG_T::n_nonzeros, CONFIG_T::reuse_factor);
+
+    typename CONFIG_T::accum_t acc [CONFIG_T::n_out];
+    #pragma HLS ARRAY_PARTITION variable=acc    complete
+    #pragma HLS ARRAY_PARTITION variable=biases complete
+    #pragma HLS ARRAY_RESHAPE   variable=weights block factor=multiplier_limit
+    //if (CONFIG_T::store_weights_in_bram){
+    //#pragma HLS RESOURCE variable=weights core=ROM_1P_BRAM
+    #pragma HLS data_pack variable=weights struct_level
+    //}
+
+    InitAccum:
+    for(unsigned i = 0; i < CONFIG_T::n_out; i++) {
+        #pragma HLS UNROLL
+        acc[i] = (typename CONFIG_T::accum_t) (biases[i]);
+    }
+
+    // Do the compressed matrix-multiply
+    const int rufactor = CONFIG_T::reuse_factor;
+    ReuseLoop:
+    for(unsigned ir = 0; ir < rufactor; ir++) {
+        #pragma HLS PIPELINE  II=1 rewind
+
+        typename CONFIG_T::accum_t mult[CONFIG_T::n_out];
+        #pragma HLS ARRAY_PARTITION variable=mult complete
+
+        ResetMult:
+        for(int imult = 0; imult < CONFIG_T::n_out; imult++) {
+            #pragma HLS UNROLL
+            mult[imult] = 0;
+        }
+
+        CompressedMultLoop:
+        for(unsigned im = 0; im < multiplier_limit; im++) {
+            #pragma HLS UNROLL
+            unsigned w = im * rufactor + ir;
+            auto row = weights[w].row_index;
+            auto col = weights[w].col_index;
+            auto weight_cache = weights[w].weight;
+            data_T  data_cache = data[row];
+            //mult[col] += weight_cache * data_cache;
+            typename CONFIG_T::accum_t prod = CONFIG_T::template product<data_T, typename CONFIG_T::weight_t, typename CONFIG_T::accum_t>::product(data_cache, weight_cache);
+            fill_mult<CONFIG_T>(col, mult, prod);
+        }
+
+        for (int im = 0; im < CONFIG_T::n_out; im++){
+            acc[im] += mult[im];
+        }
+    }
+
+    // Cast to "res_t" type
+    ResultLoop:
+    for(unsigned i = 0; i < CONFIG_T::n_out; i++){
+        #pragma HLS UNROLL
+        //res[i] = (res_T) (acc[i]);
+        res[i] = cast<data_T, res_T, CONFIG_T>(acc[i]);
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_latency.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_latency.h
new file mode 100644
index 0000000..4a04671
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_latency.h
@@ -0,0 +1,131 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_DENSE_LATENCY_H_
+#define NNET_DENSE_LATENCY_H_
+
+#include "nnet_common.h"
+#include "nnet_mult.h"
+#include "nnet_helpers.h"
+#include "hls_stream.h"
+#include <math.h>
+
+namespace nnet {
+
+template<class data_T, class res_T, typename CONFIG_T>
+void dense_latency(
+    data_T    data[CONFIG_T::n_in],
+    res_T     res[CONFIG_T::n_out],
+    typename CONFIG_T::weight_t  weights[CONFIG_T::n_in*CONFIG_T::n_out],
+    typename CONFIG_T::bias_t    biases[CONFIG_T::n_out])
+{
+    data_T cache;
+    typename CONFIG_T::accum_t mult[CONFIG_T::n_in*CONFIG_T::n_out];
+    typename CONFIG_T::accum_t acc[CONFIG_T::n_out];
+
+    // Use a function_instantiate in case it helps to explicitly optimize unchanging weights/biases
+    #pragma HLS function_instantiate variable=weights,biases
+
+    if (CONFIG_T::io_type == io_parallel || CONFIG_T::io_type == io_stream){
+        // For parallel inputs:
+        //   - completely partition arrays -- target fabric
+        //   - if we have an unroll factor, limit number of multipliers
+        #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+
+        // #pragma HLS ARRAY_PARTITION variable=weights complete // remove this line for now, it breaks compression sometimes
+        #pragma HLS ARRAY_PARTITION variable=biases complete
+        #pragma HLS ARRAY_PARTITION variable=mult complete
+        #pragma HLS ARRAY_PARTITION variable=acc complete
+
+        int multiplier_limit  = ceil(float(CONFIG_T::n_in*CONFIG_T::n_out) / float(CONFIG_T::reuse_factor)) - floor(float(CONFIG_T::n_zeros) / float(CONFIG_T::reuse_factor));
+        CONFIG_T::template product<data_T, typename CONFIG_T::weight_t, typename CONFIG_T::accum_t>::limit(multiplier_limit);
+
+    } else if (CONFIG_T::io_type == io_serial){
+        // Only reduce cycle_factor if n_out is evenly divisible by reuse_factor
+        // Otherwise, HLS wont be happy
+        int cycle_factor = CONFIG_T::n_out / CONFIG_T::reuse_factor;
+        int reused_cycle = DIV_ROUNDUP(CONFIG_T::n_out, CONFIG_T::reuse_factor);
+        if (cycle_factor != reused_cycle) {
+            cycle_factor = CONFIG_T::n_out;
+        }
+        /*int cycle_factor = CONFIG_T::n_out;
+        float reused_cycle = CONFIG_T::n_out / CONFIG_T::reuse_factor;
+        if (reused_cycle == ceil(reused_cycle)){
+            // Dont use "ceil" here; as of 2018.2, HLS crashes mysteriously
+            cycle_factor = cycle_factor / CONFIG_T::reuse_factor;
+        }*/
+        #pragma HLS ARRAY_PARTITION variable=weights cyclic factor=cycle_factor
+        #pragma HLS ARRAY_PARTITION variable=mult cyclic factor=cycle_factor
+        #pragma HLS ARRAY_PARTITION variable=acc complete
+        #pragma HLS DATAFLOW
+        #pragma HLS STREAM variable=mult depth=1
+        #pragma HLS STREAM variable=acc depth=1
+        if (CONFIG_T::store_weights_in_bram){
+            #pragma HLS RESOURCE variable=weights core=ROM_2P_BRAM
+        }
+    }
+
+    // Do the matrix-multiply
+    Product1: for(int ii = 0; ii < CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        cache = data[ii];
+        Product2: for(int jj = 0; jj < CONFIG_T::n_out; jj++) {
+            if (CONFIG_T::io_type == io_serial) {
+                int multiplier_limit  = ceil(float(CONFIG_T::n_out) / float(CONFIG_T::reuse_factor));
+                CONFIG_T::template product<data_T, typename CONFIG_T::weight_t, typename CONFIG_T::accum_t>::limit(multiplier_limit);
+            }
+        int index = ii*CONFIG_T::n_out+jj;
+        mult[index] = CONFIG_T::template product<data_T, typename CONFIG_T::weight_t, typename CONFIG_T::accum_t>::product(cache, weights[index]);
+        }
+    }
+
+    // Initialize accumulator with input biases
+    ResetAccum: for(int iacc = 0; iacc < CONFIG_T::n_out; iacc++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS UNROLL
+        }
+        acc[iacc] = (typename CONFIG_T::accum_t) biases[iacc];
+    }
+
+    // Accumulate multiplication result
+    Accum1: for(int ii = 0; ii < CONFIG_T::n_in; ii++) {
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS PIPELINE
+        }
+        Accum2: for(int jj = 0; jj < CONFIG_T::n_out; jj++) {
+        int index = ii*CONFIG_T::n_out+jj;
+        acc[jj] += mult[index];
+        }
+    }
+
+    // Cast to "res_t" type
+    Result: for(int ires = 0; ires < CONFIG_T::n_out; ires++){
+        if (CONFIG_T::io_type == io_serial){
+            #pragma HLS UNROLL
+        }
+        //res[ires] = (res_T) (acc[ires]);
+        res[ires] = cast<data_T, res_T, CONFIG_T>(acc[ires]);
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_resource.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_resource.h
new file mode 100644
index 0000000..756a627
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_resource.h
@@ -0,0 +1,284 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_DENSE_RESOURCE_H_
+#define NNET_DENSE_RESOURCE_H_
+
+#include "nnet_common.h"
+#include "nnet_mult.h"
+#include "hls_stream.h"
+#include <math.h>
+#include <assert.h>
+
+namespace nnet {
+
+template<class data_T, class res_T, typename CONFIG_T>
+void dense_resource_rf_leq_nin(
+    data_T data[CONFIG_T::n_in],
+    res_T  res[CONFIG_T::n_out],
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_in*CONFIG_T::n_out],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_out]) {
+
+    const int rufactor = CONFIG_T::reuse_factor;
+    const int multfactor = MIN(CONFIG_T::n_in,CONFIG_T::reuse_factor);
+    const int multiplier_limit = DIV_ROUNDUP(CONFIG_T::n_in*CONFIG_T::n_out, multfactor);
+    const int block_factor = DIV_ROUNDUP(CONFIG_T::n_in*CONFIG_T::n_out, CONFIG_T::reuse_factor);
+    const int multscale = multiplier_limit/CONFIG_T::n_out;
+    const int nin = CONFIG_T::n_in;
+    const int nout = CONFIG_T::n_out;
+
+    assert((multiplier_limit % nout == 0 || rufactor >= nin) && "The current Reuse Factor is not allowed");
+    assert((multiplier_limit == block_factor) && "This function is correct only for RF <= N_IN");
+
+    #pragma HLS function_instantiate variable=weights,biases
+    //#pragma HLS RESOURCE variable=weights core=RAM_2P_BRAM Commenting out the deisgnation HLS seems to choose correctly
+    #pragma HLS ARRAY_RESHAPE   variable=weights block factor=block_factor
+    #pragma HLS ARRAY_PARTITION variable=biases complete
+
+    typename CONFIG_T::accum_t acc[CONFIG_T::n_out];
+    #pragma HLS ARRAY_PARTITION variable=acc complete
+
+    InitAccum:
+    for (int iacc = 0; iacc < nout; iacc++) {
+        #pragma HLS UNROLL
+        acc[iacc] = (typename CONFIG_T::accum_t) biases[iacc];
+    }
+
+    ReuseLoop:
+    for (int ir = 0; ir < rufactor; ir++) {
+        #pragma HLS PIPELINE II=1 rewind
+
+        int w_index = ir;
+        int in_index = ir;
+        int out_index = 0;
+        int acc_step = 0;
+
+        MultLoop:
+        for (int im = 0; im < block_factor; im++) {
+            #pragma HLS UNROLL
+
+            acc[out_index] += CONFIG_T::template product<data_T, typename CONFIG_T::weight_t, typename CONFIG_T::accum_t>::product(data[in_index], weights[w_index]);
+
+            // Increment w_index
+            w_index += rufactor;
+            // Increment in_index
+            in_index += rufactor;
+            if (in_index >= nin) {
+                in_index = ir;
+            }
+            // Increment out_index
+            if (acc_step + 1 >= multscale) {
+                acc_step = 0;
+                out_index++;
+            } else {
+                acc_step++;
+            }
+        }
+    }
+
+    // Cast to "res_t" type
+    Result:
+    for (int ires = 0; ires < CONFIG_T::n_out; ires++) {
+        #pragma HLS UNROLL
+        res[ires] = cast<data_T, res_T, CONFIG_T>(acc[ires]);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void dense_resource_rf_gt_nin_rem0(
+    data_T data[CONFIG_T::n_in],
+    res_T  res[CONFIG_T::n_out],
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_in*CONFIG_T::n_out],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_out]) {
+
+    const int rufactor = MIN(CONFIG_T::reuse_factor, CONFIG_T::n_in * CONFIG_T::n_out);
+    const int multfactor = MIN(CONFIG_T::n_in,CONFIG_T::reuse_factor);
+    const int multiplier_limit = DIV_ROUNDUP(CONFIG_T::n_in*CONFIG_T::n_out, multfactor);
+    const int block_factor = DIV_ROUNDUP(CONFIG_T::n_in*CONFIG_T::n_out, CONFIG_T::reuse_factor);
+    const int multscale = multiplier_limit/CONFIG_T::n_out;
+    const int nin = CONFIG_T::n_in;
+    const int nout = CONFIG_T::n_out;
+
+    assert((multiplier_limit % nout == 0 || rufactor >= nin) && "The current Reuse Factor is not allowed");
+    assert((rufactor > nin && rufactor % nin == 0) && "This function is correct only for RF > N_IN && RF % N_IN == 0");
+
+    #pragma HLS function_instantiate variable=weights,biases
+    //#pragma HLS RESOURCE variable=weights core=RAM_2P_BRAM Commenting out the deisgnation HLS seems to choose correctly
+    #pragma HLS ARRAY_RESHAPE   variable=weights block factor=block_factor
+    #pragma HLS ARRAY_PARTITION variable=biases complete
+
+    typename CONFIG_T::accum_t acc[CONFIG_T::n_out];
+    #pragma HLS ARRAY_PARTITION variable=acc complete
+
+    InitAccum:
+    for (int iacc = 0; iacc < nout; iacc++) {
+        #pragma HLS UNROLL
+        acc[iacc] = (typename CONFIG_T::accum_t) biases[iacc];
+    }
+
+    int w_index;
+    int in_index = 0;
+    int out_index;
+    int outstep = 0;
+    const int outscale = rufactor / nin;
+
+    int outidx[rufactor];
+    IndexLoop:
+    for (int ir = 0; ir < rufactor; ir++) {
+        outidx[ir] = outstep;
+        if ((ir + 1) % nin == 0) {
+            outstep++;
+        }
+    }
+
+    ReuseLoop:
+    for (int ir = 0; ir < rufactor; ir++) {
+        #pragma HLS PIPELINE II=1 rewind
+
+        w_index = ir;
+        out_index = outidx[ir]/*outstep*/;
+
+        MultLoop:
+        for (int im = 0; im < block_factor; im++) {
+            #pragma HLS UNROLL
+            acc[out_index] += CONFIG_T::template product<data_T, typename CONFIG_T::weight_t, typename CONFIG_T::accum_t>::product(data[in_index], weights[w_index]);
+
+            w_index += rufactor;
+            if (w_index >= CONFIG_T::n_in * CONFIG_T::n_out) break; // check out of bounds
+            out_index += outscale;
+        }
+
+        in_index++;
+        if (in_index >= nin) {
+            in_index = 0;
+            //outstep++; // This causes a huge increase in scheduling and RTL generation times, hence the above workaround.
+        }
+    }
+
+    // Cast to "res_t" type
+    Result:
+    for (int ires = 0; ires < CONFIG_T::n_out; ires++) {
+        #pragma HLS UNROLL
+        res[ires] = cast<data_T, res_T, CONFIG_T>(acc[ires]);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void dense_resource_rf_gt_nin(
+    data_T data[CONFIG_T::n_in],
+    res_T  res[CONFIG_T::n_out],
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_in*CONFIG_T::n_out],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_out]) {
+
+    const int rufactor = CONFIG_T::reuse_factor;
+    const int multfactor = MIN(CONFIG_T::n_in,CONFIG_T::reuse_factor);
+    const int multiplier_limit = DIV_ROUNDUP(CONFIG_T::n_in*CONFIG_T::n_out, multfactor);
+    const int block_factor = DIV_ROUNDUP(CONFIG_T::n_in*CONFIG_T::n_out, CONFIG_T::reuse_factor);
+    const int multscale = multiplier_limit/CONFIG_T::n_out;
+    const int nin = CONFIG_T::n_in;
+    const int nout = CONFIG_T::n_out;
+
+    assert((multiplier_limit % nout == 0 || rufactor >= nin) && "The current Reuse Factor is not allowed");
+    assert((rufactor > nin) && "This function is correct only for RF > N_IN");
+
+    #pragma HLS function_instantiate variable=weights,biases
+    //#pragma HLS RESOURCE variable=weights core=RAM_2P_BRAM Commenting out the deisgnation HLS seems to choose correctly
+    #pragma HLS ARRAY_RESHAPE   variable=weights block factor=block_factor
+    #pragma HLS ARRAY_PARTITION variable=biases complete
+
+    typename CONFIG_T::accum_t acc[CONFIG_T::n_out];
+    #pragma HLS ARRAY_PARTITION variable=acc complete
+
+    InitAccum:
+    for (int iacc = 0; iacc < nout; iacc++) {
+        #pragma HLS UNROLL
+        acc[iacc] = (typename CONFIG_T::accum_t) biases[iacc];
+    }
+
+    ReuseLoop:
+    for (int ir = 0; ir < rufactor; ir++) {
+        #pragma HLS PIPELINE II=1 rewind
+        typename CONFIG_T::accum_t tmpmult[block_factor];
+        #pragma HLS ARRAY_PARTITION variable=tmpmult complete
+
+        MultLoop:
+        for (int im = 0; im < block_factor; im++) {
+            #pragma HLS UNROLL
+            int w_index = ir + rufactor * im;
+            int in_index = w_index % nin;
+            if (w_index >= CONFIG_T::n_in*CONFIG_T::n_out) continue; // check out of bounds
+            tmpmult[im] = CONFIG_T::template product<data_T, typename CONFIG_T::weight_t, typename CONFIG_T::accum_t>::product(data[in_index], weights[w_index]);
+        }
+
+        typename CONFIG_T::accum_t mult[multiplier_limit];
+        #pragma HLS ARRAY_PARTITION variable=mult complete
+
+        ResetMult:
+        for (int imult = 0; imult < multiplier_limit; imult++) {
+            #pragma HLS UNROLL
+            mult[imult] = 0;
+        }
+
+        AccumLoop1:
+        for (int im = 0; im < block_factor; im++) {
+            #pragma HLS UNROLL
+            int w_index = ir + rufactor * im;
+            int out_index = w_index / multfactor;
+            if (out_index >= multiplier_limit) continue; // check out of bounds
+            mult[out_index] += tmpmult[im];
+        }
+
+        AccumLoop2:
+        for (int im = 0; im < multiplier_limit; im++) {
+            #pragma HLS UNROLL
+            //int out_index = im/multscale; // This is the general case
+            //acc[out_index] += mult[im];
+            acc[im] += mult[im]; // If RF > N_IN then multiplier_limit == n_out
+        }
+    }
+
+    // Cast to "res_t" type
+    Result:
+    for (int ires = 0; ires < CONFIG_T::n_out; ires++) {
+        #pragma HLS UNROLL
+        res[ires] = cast<data_T, res_T, CONFIG_T>(acc[ires]);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void dense_resource(
+    data_T data[CONFIG_T::n_in],
+    res_T  res[CONFIG_T::n_out],
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_in*CONFIG_T::n_out],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_out]) {
+
+    #pragma HLS INLINE region
+
+    if (CONFIG_T::reuse_factor <= CONFIG_T::n_in) {
+        dense_resource_rf_leq_nin<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    } else if (CONFIG_T::reuse_factor % CONFIG_T::n_in == 0) {
+        dense_resource_rf_gt_nin_rem0<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    } else {
+        dense_resource_rf_gt_nin<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_stream.h
new file mode 100644
index 0000000..52c96c5
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_dense_stream.h
@@ -0,0 +1,70 @@
+#ifndef NNET_DENSE_STREAM_H_
+#define NNET_DENSE_STREAM_H_
+
+#include "nnet_common.h"
+#include "nnet_types.h"
+#include "hls_stream.h"
+#include <math.h>
+#include <assert.h>
+
+namespace nnet {
+
+template<class data_T, class res_T, typename CONFIG_T>
+void dense_wrapper(
+    data_T data[CONFIG_T::n_in],
+    res_T  res[CONFIG_T::n_out],
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_in*CONFIG_T::n_out],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_out]
+) {
+    #pragma HLS INLINE region
+    if (CONFIG_T::strategy == nnet::latency) {
+        #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+        dense_latency<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    } else {
+        dense_resource<data_T, res_T, CONFIG_T>(data, res, weights, biases);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void dense(
+    hls::stream<data_T> &data_stream,
+    hls::stream<res_T>  &res_stream,
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_in*CONFIG_T::n_out],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_out])
+{
+    typename data_T::value_type data[CONFIG_T::n_in];
+    #pragma HLS ARRAY_PARTITION variable=data complete
+
+    typename res_T::value_type res[CONFIG_T::n_out];
+    #pragma HLS ARRAY_PARTITION variable=res complete
+
+    DataPrepare: for(int i_in = 0; i_in < CONFIG_T::n_in / data_T::size; i_in++) {
+        if (CONFIG_T::n_in / data_T::size > 1) {
+            #pragma HLS PIPELINE
+        }
+        data_T data_pack = data_stream.read();
+        DataPack: for (int i_pack = 0; i_pack < data_T::size; i_pack++) {
+            #pragma HLS UNROLL
+            data[i_in * data_T::size + i_pack] = data_pack[i_pack];
+        }
+    }
+
+    dense_wrapper<typename data_T::value_type, typename res_T::value_type, CONFIG_T>(data, res, weights, biases);
+
+    ResWrite: for(unsigned i_out = 0; i_out < CONFIG_T::n_out / res_T::size; i_out++) {
+        if (CONFIG_T::n_out / res_T::size > 1) {
+            #pragma HLS PIPELINE
+        }
+        res_T res_pack;
+        #pragma HLS DATA_PACK variable=res_pack
+        ResPack: for (int i_pack = 0; i_pack < res_T::size; i_pack++) {
+            #pragma HLS UNROLL
+            res_pack[i_pack] = res[i_out * res_T::size + i_pack];
+        }
+        res_stream.write(res_pack);
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_garnet.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_garnet.h
new file mode 100644
index 0000000..dfd4ced
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_garnet.h
@@ -0,0 +1,983 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_GARNET_H_
+#define NNET_GARNET_H_
+
+#include "nnet_common.h"
+#include "hls_stream.h"
+#include "hls_math.h"
+
+namespace nnet {
+  namespace garnet_utils {
+
+    template<class CONFIG_T>
+    inline
+    typename std::enable_if<std::is_class<typename CONFIG_T::distance_t>::value>::type
+    initialize_edge_weights_table(typename CONFIG_T::edge_weight_t edge_weights_table[])
+    {
+      typedef ap_uint<CONFIG_T::distance_width> index_t;
+
+      unsigned const table_size = (1 << CONFIG_T::distance_width);
+
+      index_t index;
+      typename CONFIG_T::distance_t distance;
+
+      // edge_weight_t is ap_ufixed with 0 iwidth -> let index 0 be a saturated version of 1
+      edge_weights_table[0] = ap_ufixed<CONFIG_T::edge_weight_t::width, 0, AP_TRN, AP_SAT>(1.);
+
+      for (unsigned iw = 1; iw < table_size; ++iw) {
+        index = iw;
+        distance.range(CONFIG_T::distance_width - 1, 0) = index.range(CONFIG_T::distance_width - 1, 0);
+        edge_weights_table[iw] = hls::exp(-distance * distance);
+      }
+    }
+
+    template<class CONFIG_T>
+    inline
+    typename std::enable_if<not std::is_class<typename CONFIG_T::distance_t>::value>::type
+    initialize_edge_weights_table(typename CONFIG_T::edge_weight_t edge_weights_table[])
+    {
+      unsigned const table_size = (1 << CONFIG_T::distance_width);
+      double const step = 64. / table_size;
+
+      typename CONFIG_T::distance_t v = -32.;
+      for (unsigned iw = 0; iw < table_size; ++iw) {
+        edge_weights_table[iw] = std::exp(-v * v);
+        v += step;
+      }
+    }
+
+    template<class CONFIG_T>
+    inline
+    typename std::enable_if<std::is_class<typename CONFIG_T::distance_t>::value, typename CONFIG_T::edge_weight_t>::type
+    get_edge_weight(typename CONFIG_T::distance_t distance, typename CONFIG_T::edge_weight_t edge_weights_table[])
+    {
+      typedef ap_uint<CONFIG_T::distance_width> index_t;
+
+      index_t index(distance.range(CONFIG_T::distance_width - 1, 0));
+
+      return edge_weights_table[index];
+    }
+
+    template<class CONFIG_T>
+    inline
+    typename std::enable_if<not std::is_class<typename CONFIG_T::distance_t>::value, typename CONFIG_T::edge_weight_t>::type
+    get_edge_weight(typename CONFIG_T::distance_t distance, typename CONFIG_T::edge_weight_t edge_weights_table[])
+    {
+      unsigned const table_size = (1 << CONFIG_T::distance_width);
+      double const step = 64. / table_size;
+
+      int index = (distance + 32.) / step;
+      if (index < 0)
+        index = 0;
+      else if (index >= table_size)
+        index = table_size - 1;
+
+      return edge_weights_table[index];
+    }
+
+    template<class CONFIG_T>
+    typename CONFIG_T::edge_weight_t
+    compute_edge_weight(typename CONFIG_T::distance_t distance)
+    {
+      if (CONFIG_T::is_stack) {
+        #pragma HLS INLINE OFF
+      }
+#ifdef __SYNTHESIS__
+      typename CONFIG_T::edge_weight_t edge_weights_table[1 << CONFIG_T::distance_width];
+      // unsigned const reshape_factor = CONFIG_T::n_aggregators * CONFIG_T::n_in_features * (CONFIG_T::n_vertices / CONFIG_T::reuse_factor);
+      // #pragma HLS ARRAY_RESHAPE variable=edge_weights_table cyclic factor=reshape_factor dim=1
+      bool initialized = false;
+#else
+      static typename CONFIG_T::edge_weight_t edge_weights_table[1 << CONFIG_T::distance_width];
+      static bool initialized = false;
+#endif
+      if (not initialized) {
+        initialize_edge_weights_table<CONFIG_T>(edge_weights_table);
+        initialized = true;
+      }
+
+      return get_edge_weight<CONFIG_T>(distance, edge_weights_table);
+    }
+
+    template<class dividend_T, class exponent_T>
+    inline
+    typename std::enable_if<std::is_class<dividend_T>::value, dividend_T>::type
+    normalize_log2(dividend_T dividend, exponent_T exponent)
+    {
+      #pragma HLS INLINE
+      return dividend >> exponent;
+    }
+
+    template<class dividend_T, class exponent_T>
+    inline
+    typename std::enable_if<not std::is_class<dividend_T>::value, dividend_T>::type
+    normalize_log2(dividend_T dividend, exponent_T exponent)
+    {
+      #pragma HLS INLINE
+      return dividend / std::pow(2., exponent);
+    }
+
+    template<class CONFIG_T, class E = typename CONFIG_T::edge_weight_t>
+    struct Means {
+      typedef E edge_weight_t;
+      
+      edge_weight_t edge_weight_mean[CONFIG_T::n_aggregators];
+      typename CONFIG_T::aggr_t weighted_feature_mean[CONFIG_T::n_aggregators * CONFIG_T::n_in_features];
+
+      Means() {
+        #pragma HLS INLINE
+        #pragma HLS ARRAY_PARTITION variable=edge_weight_mean complete
+        #pragma HLS ARRAY_PARTITION variable=weighted_feature_mean complete
+        #pragma HLS UNROLL region
+
+       Aggregators:
+        for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+          edge_weight_mean[ia] = 0.;
+
+         InFeatures:
+          for (unsigned ix = 0; ix < CONFIG_T::n_in_features; ++ix) {
+            unsigned const iax = ia * CONFIG_T::n_in_features + ix;
+            weighted_feature_mean[iax] = 0.;
+          }
+        }
+      }
+
+      void set_weight(unsigned, edge_weight_t const&) {
+        #pragma HLS INLINE
+      }
+
+      void add_means_normalized(Means<CONFIG_T, edge_weight_t> const& local) {
+        #pragma HLS INLINE
+        // Always called within a pipelined region - no UNROLL needed
+
+        unsigned const log2_unroll_factor = CONFIG_T::n_vertices_width - CONFIG_T::log2_reuse_factor;
+        
+       Aggregators:
+        for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+          edge_weight_mean[ia] += normalize_log2(local.edge_weight_mean[ia], log2_unroll_factor);
+
+         InFeatures:
+          for (unsigned ix = 0; ix < CONFIG_T::n_in_features; ++ix) {
+            unsigned const iax = ia * CONFIG_T::n_in_features + ix;
+            weighted_feature_mean[iax] += normalize_log2(local.weighted_feature_mean[iax], log2_unroll_factor);
+          }
+        }
+      }
+
+      template<class nvtx_T, class arrays_T, class T = CONFIG_T>
+      typename std::enable_if<T::mean_by_nvert>::type set_means_normalized(nvtx_T const nvtx, arrays_T const& accum) {
+        #pragma HLS INLINE
+        #pragma HLS UNROLL region
+
+        // accum comes divided by unroll factor
+        typename T::norm_t nvtx_norm = (T::n_vertices / T::reuse_factor) / nvtx;
+
+       Aggregators:
+        for (unsigned ia = 0; ia < T::n_aggregators; ++ia) {
+          edge_weight_mean[ia] = accum.edge_weight_mean[ia] * nvtx_norm;
+
+         InFeatures:
+          for (unsigned ix = 0; ix < T::n_in_features; ++ix) {
+            unsigned const iax = ia * T::n_in_features + ix;
+
+            weighted_feature_mean[iax] = accum.weighted_feature_mean[iax] * nvtx_norm;
+          }
+        }
+      }
+
+      template<class nvtx_T, class arrays_T, class T = CONFIG_T>
+      typename std::enable_if<not T::mean_by_nvert>::type set_means_normalized(nvtx_T const nvtx, arrays_T const& accum) {
+        #pragma HLS INLINE
+        #pragma HLS UNROLL region
+
+       Aggregators:
+        for (unsigned ia = 0; ia < T::n_aggregators; ++ia) {
+  
+          edge_weight_mean[ia] = normalize_log2(accum.edge_weight_mean[ia], T::log2_reuse_factor);
+  
+         InFeatures:
+          for (unsigned ix = 0; ix < T::n_in_features; ++ix) {
+            unsigned const iax = ia * T::n_in_features + ix;
+  
+            weighted_feature_mean[iax] = normalize_log2(accum.weighted_feature_mean[iax], T::log2_reuse_factor);
+          }
+        }
+      }
+    };
+
+    template<class CONFIG_T, class E = typename CONFIG_T::edge_weight_t>
+    struct WeightsAndMeans : public Means<CONFIG_T, E> {
+      typedef E edge_weight_t;
+
+      edge_weight_t edge_weights[CONFIG_T::n_vertices * CONFIG_T::n_aggregators];
+
+      WeightsAndMeans() : Means<CONFIG_T, E>() {
+        #pragma HLS INLINE
+        unsigned const reshape_factor = CONFIG_T::n_aggregators * (CONFIG_T::n_vertices / CONFIG_T::reuse_factor);
+        #pragma HLS ARRAY_PARTITION variable=edge_weights cyclic factor=reshape_factor
+      }
+
+      void set_weight(unsigned iva, edge_weight_t const& weight) {
+        #pragma HLS INLINE
+        edge_weights[iva] = weight;
+      }
+    };
+
+
+    template<class CONFIG_T, class nvtx_T, class Enable = void>
+    struct OutputBiasNormalizer;
+
+    template<class CONFIG_T, class nvtx_T>
+    struct OutputBiasNormalizer<CONFIG_T, nvtx_T, typename std::enable_if<CONFIG_T::mean_by_nvert>::type> {
+      typedef typename CONFIG_T::output_transform_biases_t biases_t;
+
+      biases_t const (&output_biases)[CONFIG_T::n_out_features];
+
+      OutputBiasNormalizer(nvtx_T const) : output_biases{CONFIG_T::output_transform_biases} {
+        #pragma HLS INLINE
+      }
+    };
+
+    template<class CONFIG_T, class nvtx_T>
+    struct OutputBiasNormalizer<CONFIG_T, nvtx_T, typename std::enable_if<not CONFIG_T::mean_by_nvert>::type> {
+      typedef typename CONFIG_T::output_transform_biases_t biases_t;
+
+      biases_t output_biases[CONFIG_T::n_out_features];
+      
+      OutputBiasNormalizer(nvtx_T const nvtx) {
+        #pragma HLS ARRAY_PARTITION variable=output_biases complete
+        #pragma HLS UNROLL region
+        
+        // Cannot add a loop label here due to a Vivado HLS bug, apparently
+        for (unsigned io = 0; io < CONFIG_T::n_out_features; ++io) {
+          typename CONFIG_T::aggr_t bias = CONFIG_T::output_transform_biases[io];
+          bias *= nvtx;
+          output_biases[io] = normalize_log2(bias, CONFIG_T::n_vertices_width);
+        }
+      }
+    };
+
+    template<class CONFIG_T, class data_T>
+    struct InputDataGetter {
+      typedef data_T data_t;
+
+      data_T const* dataref;
+
+      InputDataGetter(data_T const* d) : dataref{d} {
+        #pragma HLS INLINE
+      }
+      data_T const& get(unsigned iv, unsigned ix) const {
+        #pragma HLS INLINE
+        unsigned const ivx = iv * CONFIG_T::n_in_features + ix;
+        return dataref[ivx];
+      }
+    };
+
+    template<class CONFIG_T, class data_T>
+    struct SingleVertexDataGetter {
+      typedef data_T data_t;
+
+      data_T const (&dataref)[CONFIG_T::n_in_features];
+
+      SingleVertexDataGetter(data_T const (&d)[CONFIG_T::n_in_features]) : dataref{d} {
+        #pragma HLS INLINE
+      }
+      data_T const& get(unsigned, unsigned ix) const {
+        #pragma HLS INLINE
+        return dataref[ix];
+      }
+    };
+
+    template<class CONFIG_T, class res_T>
+    struct OutputResSetter {
+      typedef res_T res_t;
+
+      res_T* resref;
+
+      OutputResSetter(res_T* r) : resref{r} {
+        #pragma HLS INLINE
+      }
+      void set(unsigned iv, unsigned io, res_T const& acc) {
+        #pragma HLS INLINE
+        unsigned const ivo = iv * CONFIG_T::n_out_features + io;    
+        resref[ivo] = acc;
+      }
+    };
+
+    template<class CONFIG_T, class res_T>
+    struct SingleVertexResSetter {
+      typedef res_T res_t;
+
+      res_T (&resref)[CONFIG_T::n_out_features];
+
+      SingleVertexResSetter(res_T (&r)[CONFIG_T::n_out_features]) : resref{r} {
+        #pragma HLS INLINE
+      }
+      void set(unsigned, unsigned io, res_T const& acc) {
+        #pragma HLS INLINE
+        resref[io] = acc;
+      }
+    };
+
+    template<class CONFIG_T, class data_getter_T, class arrays_local_T, class arrays_T>
+    inline
+    void
+    compute_weights_aggregates(
+      data_getter_T const& data_getter,
+      unsigned iv,
+      arrays_local_T& arrays_local,
+      arrays_T& arrays
+    )
+    {
+      #pragma HLS INLINE
+
+     Aggregators:
+      for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+        typename CONFIG_T::distance_t distance = CONFIG_T::aggregator_distance_biases[ia];
+
+       InFeatures1:
+        for (unsigned ix = 0; ix < CONFIG_T::n_in_features; ++ix) {
+          unsigned const iax = ia * CONFIG_T::n_in_features + ix;
+
+          typename CONFIG_T::distance_t incr = data_getter.get(iv, ix) * CONFIG_T::aggregator_distance_weights[iax];
+
+          distance += incr;
+        }
+
+        typename CONFIG_T::edge_weight_t edge_weight = garnet_utils::compute_edge_weight<typename CONFIG_T::base_t>(distance);
+
+        arrays_local.edge_weight_mean[ia] += edge_weight;
+
+       InFeatures2:
+        for (unsigned ix = 0; ix < CONFIG_T::n_in_features; ++ix) {
+          unsigned const iax = ia * CONFIG_T::n_in_features + ix;
+
+          typename data_getter_T::data_t incr = data_getter.get(iv, ix) * edge_weight;
+
+          arrays_local.weighted_feature_mean[iax] += incr;
+        }
+
+        unsigned const iva = iv * CONFIG_T::n_aggregators + ia;
+        arrays.set_weight(iva, edge_weight);
+      }
+    }
+
+    template<class CONFIG_T, class arrays_T>
+    inline
+    typename CONFIG_T::aggr_t
+    compute_output_base_core(
+      arrays_T const& arrays,
+      unsigned io,
+      unsigned ia
+    )
+    {
+      #pragma HLS INLINE
+      #pragma HLS UNROLL region
+
+      unsigned const ioa = io * CONFIG_T::n_aggregators + ia;
+      typename CONFIG_T::aggr_t aggr = arrays.edge_weight_mean[ia] * CONFIG_T::input_transform_biases[ioa];
+
+     InFeatures:
+      for (unsigned ix = 0; ix < CONFIG_T::n_in_features; ++ix) {
+        unsigned const ioax = ioa * CONFIG_T::n_in_features + ix;
+        unsigned const iax = ia * CONFIG_T::n_in_features + ix;
+
+        aggr += arrays.weighted_feature_mean[iax] * CONFIG_T::input_transform_weights[ioax];
+      }
+
+      return aggr;
+    }
+
+    template<class CONFIG_T, class arrays_T>
+    inline
+    void
+    compute_output_base(
+      arrays_T const& arrays,
+      typename CONFIG_T::aggr_t output_base[CONFIG_T::n_out_features * CONFIG_T::n_aggregators]
+    )
+    {
+      #pragma HLS INLINE
+      #pragma HLS UNROLL region
+
+     OutFeatures:
+      for (unsigned io = 0; io < CONFIG_T::n_out_features; ++io) {
+       Aggregators:
+        for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+          unsigned const ioa = io * CONFIG_T::n_aggregators + ia;
+
+          output_base[ioa] = compute_output_base_core<CONFIG_T>(arrays, io, ia);
+        }
+      }
+    }
+
+    template<class CONFIG_T, class arrays_T, class res_setter_T>
+    inline
+    void
+    compute_vertex_output(
+      arrays_T const& arrays,
+      unsigned iv,
+      typename CONFIG_T::aggr_t const output_base[CONFIG_T::n_out_features * CONFIG_T::n_aggregators],
+      res_setter_T& res_setter
+    )
+    {
+      #pragma HLS INLINE
+
+      typename arrays_T::edge_weight_t edge_weights[CONFIG_T::n_aggregators];
+      #pragma HLS ARRAY_PARTITION variable=edge_weights complete
+
+     Aggregators1:
+      for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+        unsigned const iva = iv * CONFIG_T::n_aggregators + ia;
+
+        edge_weights[ia] = arrays.edge_weights[iva];
+      }
+      
+     OutFeatures:
+      for (unsigned io = 0; io < CONFIG_T::n_out_features; ++io) {
+        typename res_setter_T::res_t acc = CONFIG_T::output_transform_biases[io];
+    
+       Aggregators2:
+        for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+          unsigned const ioa = io * CONFIG_T::n_aggregators + ia;
+
+          typename res_setter_T::res_t incr = edge_weights[ia] * output_base[ioa];
+          acc += incr;
+        }
+
+        res_setter.set(iv, io, acc);
+      }
+    }
+
+    template<class CONFIG_T, class data_T, class nvtx_T, class arrays_T>
+    void
+    aggregate(
+      data_T const data[CONFIG_T::n_vertices * CONFIG_T::n_in_features],
+      nvtx_T const nvtx,
+      arrays_T& arrays
+    )
+    {
+      InputDataGetter<CONFIG_T, data_T> data_getter(data);
+
+      unsigned const unroll_factor = CONFIG_T::n_vertices >> CONFIG_T::log2_reuse_factor;
+
+      Means<CONFIG_T, typename CONFIG_T::edge_weight_aggr_t> means_accum;
+      
+     VerticesOuter:
+      for (unsigned ivv = 0; ivv < CONFIG_T::reuse_factor; ++ivv) {
+        #pragma HLS PIPELINE
+
+        if (ivv * unroll_factor >= nvtx)
+          break;
+
+        Means<CONFIG_T, typename CONFIG_T::edge_weight_aggr_t> means_local;
+
+       VerticesInner:
+        for (unsigned ir = 0; ir < unroll_factor; ++ir) {
+          unsigned iv = ivv * unroll_factor + ir;
+
+          if (iv == nvtx)
+            break;
+    
+          compute_weights_aggregates<CONFIG_T>(data_getter, iv, means_local, arrays);
+        }
+
+        means_accum.add_means_normalized(means_local);
+      }
+
+      arrays.set_means_normalized(nvtx, means_accum);
+    }
+
+    template<class CONFIG_T, class nvtx_T, class arrays_T, class res_T>
+    void
+    distribute(
+      nvtx_T const nvtx,
+      arrays_T const& arrays,
+      res_T res[CONFIG_T::n_vertices * CONFIG_T::n_out_features]
+    )
+    {
+      OutputResSetter<CONFIG_T, res_T> res_setter(res);
+
+      typename CONFIG_T::aggr_t output_base[CONFIG_T::n_out_features * CONFIG_T::n_aggregators];
+      #pragma HLS ARRAY_PARTITION variable=output_base complete      
+
+      compute_output_base<CONFIG_T>(arrays, output_base);
+
+      unsigned const unroll_factor = CONFIG_T::n_vertices >> CONFIG_T::log2_reuse_factor;
+
+     VerticesOuter:
+      for (unsigned ivv = 0; ivv < CONFIG_T::reuse_factor; ++ivv) {
+        #pragma HLS PIPELINE
+
+        if (ivv * unroll_factor >= nvtx)
+          break;
+
+       VerticesInner:
+        for (unsigned ir = 0; ir < unroll_factor; ++ir) {
+          unsigned iv = ivv * unroll_factor + ir;
+
+          if (iv == nvtx)
+            break;
+    
+          compute_vertex_output<CONFIG_T>(arrays, iv, output_base, res_setter);
+        }
+      }
+    }
+    
+    template<class CONFIG_T, class output_biases_T, class arrays_T, class res_T>
+    void
+    set_output(
+      output_biases_T const& output_transform_biases,
+      arrays_T const& arrays,
+      res_T res[CONFIG_T::n_out_features]
+    )
+    {
+      #pragma HLS PIPELINE
+    
+     OutFeatures:
+      for (unsigned io = 0; io < CONFIG_T::n_out_features; ++io) {
+        res_T acc = output_transform_biases.output_biases[io];
+
+       Aggregators:
+        for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+          typename CONFIG_T::aggr_t aggr = compute_output_base_core<CONFIG_T>(arrays, io, ia);
+
+          acc += arrays.edge_weight_mean[ia] * aggr;
+        }
+    
+        res[io] = acc;
+      }
+    }
+
+    template<class prev_layer_t, class current_layer_t, class nvtx_T, class prev_arrays_T, class current_arrays_T>
+    void
+    distribute_aggregate(
+      nvtx_T const nvtx,
+      prev_arrays_T const& prev_arrays,
+      current_arrays_T& current_arrays
+    )
+    {
+      typedef typename prev_layer_t::output_t data_T;
+
+      typename prev_layer_t::aggr_t prev_output_base[prev_layer_t::n_out_features * prev_layer_t::n_aggregators];
+      #pragma HLS ARRAY_PARTITION variable=prev_output_base complete
+
+      compute_output_base<prev_layer_t>(prev_arrays, prev_output_base);
+
+      unsigned const unroll_factor = current_layer_t::n_vertices >> current_layer_t::log2_reuse_factor;
+
+      Means<current_layer_t, typename current_layer_t::edge_weight_aggr_t> means_accum;
+      
+     VerticesOuter:
+      for (unsigned ivv = 0; ivv < current_layer_t::reuse_factor; ++ivv) {
+        #pragma HLS PIPELINE
+
+        if (ivv * unroll_factor >= nvtx)
+          break;
+
+        Means<current_layer_t, typename current_layer_t::edge_weight_aggr_t> means_local;
+
+       VerticesInner:
+        for (unsigned ir = 0; ir < unroll_factor; ++ir) {
+          unsigned iv = ivv * unroll_factor + ir;
+
+          if (iv == nvtx)
+            break;
+    
+          data_T data[prev_layer_t::n_out_features];
+          #pragma HLS ARRAY_PARTITION variable=data complete
+
+          SingleVertexResSetter<prev_layer_t, data_T> res_setter(data);
+
+          compute_vertex_output<prev_layer_t>(prev_arrays, iv, prev_output_base, res_setter);
+
+          SingleVertexDataGetter<current_layer_t, data_T> data_getter(data);
+          
+          compute_weights_aggregates<current_layer_t>(data_getter, iv, means_local, current_arrays);
+        }
+
+        means_accum.add_means_normalized(means_local);
+      }
+
+      current_arrays.set_means_normalized(nvtx, means_accum);
+    }
+
+    template<class prev_layer_t, class current_layer_t, class last_layer_t, class nvtx_T, class prev_arrays_T, class last_arrays_T>
+    inline
+    typename std::enable_if<std::is_same<current_layer_t, last_layer_t>::value>::type
+    sublayer(
+      nvtx_T const nvtx,
+      prev_arrays_T const& prev_arrays,
+      last_arrays_T& last_arrays
+    )
+    {
+      #pragma HLS INLINE
+
+      distribute_aggregate<prev_layer_t, current_layer_t>(nvtx, prev_arrays, last_arrays);
+    }
+      
+    template<class prev_layer_t, class current_layer_t, class last_layer_t, class nvtx_T, class prev_arrays_T, class last_arrays_T>
+    inline
+    typename std::enable_if<not std::is_same<current_layer_t, last_layer_t>::value>::type
+    sublayer(
+      nvtx_T const nvtx,
+      prev_arrays_T const& prev_arrays,
+      last_arrays_T& last_arrays
+    )
+    {
+      #pragma HLS INLINE
+
+      WeightsAndMeans<current_layer_t> current_arrays;
+
+      distribute_aggregate<prev_layer_t, current_layer_t>(nvtx, prev_arrays, current_arrays);
+
+      sublayer<current_layer_t, typename current_layer_t::next_layer_t, last_layer_t>(nvtx, current_arrays, last_arrays);
+    }
+  }
+ 
+  struct garnet_config
+  {
+    // Layer specs
+    static const unsigned n_vertices_width = 8;
+    static const unsigned n_vertices = (1 << n_vertices_width);
+    static const unsigned n_in_features = 4;
+    static const unsigned n_propagate = 4;
+    static const unsigned n_aggregators = 4;
+    static const unsigned n_out_features = 4;
+    static const unsigned distance_width = 12;
+
+    // Internal data type definitions
+    typedef float input_transform_weights_t;
+    typedef float input_transform_biases_t;
+    typedef float output_transform_weights_t;
+    typedef float output_transform_biases_t;
+    typedef float aggregator_distance_weights_t;
+    typedef float aggregator_distance_biases_t;
+  
+    typedef float norm_t;
+    typedef float distance_t;
+    typedef float edge_weight_t;
+    typedef float edge_weight_aggr_t;
+    typedef float aggr_t;
+    typedef float output_t;
+
+    /* static const input_transform_weights_t (&input_transform_weights)[n_out_features * n_aggregators * n_in_features]; */
+    /* static const input_transform_biases_t (&input_transform_biases)[n_out_features * n_aggregators]; */
+    /* static const aggregator_distance_weights_t (&aggregator_distance_weights)[n_aggregators * n_in_features]; */
+    /* static const aggregator_distance_biases_t (&aggregator_distance_biases)[n_aggregators]; */
+    /* static const output_transform_biases_t (&output_transform_biases)[n_out_features]; */
+
+    enum OutputCollapse {
+      no_collapse,
+      collapse_mean,
+      collapse_max
+    };
+  
+    static const unsigned output_collapse = no_collapse;
+
+    static const bool mean_by_nvert = false;
+    static const bool is_stack = false;
+ 
+    // Optimization specs
+    static const unsigned reuse_factor = 64;
+    static const unsigned log2_reuse_factor = 6;
+  };
+
+  // vertices -> vertices
+  template<class data_T, class nvtx_T, class res_T, typename CONFIG_T>
+  typename std::enable_if<CONFIG_T::output_collapse == CONFIG_T::no_collapse>::type
+  garnet(
+    data_T const data[CONFIG_T::n_vertices * CONFIG_T::n_in_features],
+    nvtx_T const nvtx[1],
+    res_T res[CONFIG_T::n_vertices * CONFIG_T::n_out_features]
+  )
+  {
+    #pragma HLS DATAFLOW
+
+    garnet_utils::WeightsAndMeans<CONFIG_T> arrays;
+
+    garnet_utils::aggregate<CONFIG_T>(
+      data,
+      nvtx[0],
+      arrays
+    );
+  
+    garnet_utils::distribute<CONFIG_T>(
+      nvtx[0],
+      arrays,
+      res
+    );
+  }
+
+  // vertices -> out features
+  template<class data_T, class nvtx_T, class res_T, class CONFIG_T>
+  typename std::enable_if<CONFIG_T::output_collapse == CONFIG_T::collapse_mean>::type
+  garnet(
+    data_T const data[CONFIG_T::n_vertices * CONFIG_T::n_in_features],
+    nvtx_T const nvtx[1],
+    res_T res[CONFIG_T::n_out_features]
+  )
+  {
+    #pragma HLS DATAFLOW
+
+    garnet_utils::Means<CONFIG_T> arrays;
+  
+    garnet_utils::aggregate<CONFIG_T>(
+      data,
+      nvtx[0],
+      arrays
+    );
+
+    garnet_utils::OutputBiasNormalizer<CONFIG_T, nvtx_T> normalize_bias(nvtx[0]);
+
+    garnet_utils::set_output<CONFIG_T>(
+      normalize_bias,
+      arrays,
+      res
+    );
+  }
+
+  // vertices -> vertices
+  template<class data_T, class nvtx_T, class res_T, class CONFIG_T>
+  typename std::enable_if<CONFIG_T::output_collapse == CONFIG_T::no_collapse>::type
+  garnet_stack(
+    data_T const data[CONFIG_T::n_vertices * CONFIG_T::n_in_features],
+    nvtx_T const nvtx[1],
+    res_T res[CONFIG_T::n_vertices * CONFIG_T::n_out_features]
+  )
+  {
+    #pragma HLS DATAFLOW
+
+    typedef typename CONFIG_T::template sublayer_t<0> first_layer_t;
+    unsigned const ilast = CONFIG_T::n_sublayers - 1;
+    typedef typename CONFIG_T::template sublayer_t<ilast> last_layer_t;
+
+    garnet_utils::WeightsAndMeans<first_layer_t> arrays_first;
+    garnet_utils::Means<last_layer_t> arrays_last;
+
+    garnet_utils::aggregate<first_layer_t>(
+      data,
+      nvtx[0],
+      arrays_first
+    );
+
+    garnet_utils::sublayer<first_layer_t, typename first_layer_t::next_layer_t, last_layer_t>(
+      nvtx[0],
+      arrays_first,
+      arrays_last
+    );
+
+    garnet_utils::distribute<last_layer_t>(
+      nvtx[0],
+      arrays_last,
+      res
+    );
+  }
+
+  // vertices -> out features
+  template<class data_T, class nvtx_T, class res_T, class CONFIG_T>
+  typename std::enable_if<CONFIG_T::output_collapse == CONFIG_T::collapse_mean>::type
+  garnet_stack(
+    data_T const data[CONFIG_T::n_vertices * CONFIG_T::n_in_features],
+    nvtx_T const nvtx[1],
+    res_T res[CONFIG_T::n_out_features]
+  )
+  {
+    #pragma HLS DATAFLOW
+
+    typedef typename CONFIG_T::template sublayer_t<0> first_layer_t;
+    unsigned const ilast = CONFIG_T::n_sublayers - 1;
+    typedef typename CONFIG_T::template sublayer_t<ilast> last_layer_t;
+
+    garnet_utils::WeightsAndMeans<first_layer_t> arrays_first;
+    garnet_utils::Means<last_layer_t> arrays_last;
+
+    garnet_utils::aggregate<first_layer_t>(
+      data,
+      nvtx[0],
+      arrays_first
+    );
+
+    garnet_utils::sublayer<first_layer_t, typename first_layer_t::next_layer_t, last_layer_t>(
+      nvtx[0],
+      arrays_first,
+      arrays_last
+    );
+
+    garnet_utils::OutputBiasNormalizer<last_layer_t, nvtx_T> normalize_bias(nvtx[0]);
+
+    garnet_utils::set_output<last_layer_t>(
+      normalize_bias,
+      arrays_last,
+      res
+    );
+  }
+
+  /* Reference (dumb) implementation returning (Vertices, Features) */
+  template<class data_T, class nvtx_T, class res_T, typename CONFIG_T>
+  typename std::enable_if<CONFIG_T::output_collapse == CONFIG_T::no_collapse>::type
+  garnet_ref(
+    data_T const data[CONFIG_T::n_vertices * CONFIG_T::n_in_features],
+    nvtx_T const nvtx[1],
+    res_T res[CONFIG_T::n_vertices * CONFIG_T::n_out_features]
+  )
+  {
+    typename CONFIG_T::edge_weight_t edge_weights[CONFIG_T::n_vertices * CONFIG_T::n_aggregators];
+    typename CONFIG_T::aggr_t propagated_features[CONFIG_T::n_vertices * CONFIG_T::n_propagate];
+  
+    for (unsigned iv = 0; iv < CONFIG_T::n_vertices; ++iv) {
+      if (iv == nvtx[0])
+        break;
+  
+      for (unsigned ip = 0; ip < CONFIG_T::n_propagate; ++ip) {
+        unsigned const ivp = iv * CONFIG_T::n_propagate + ip;
+  
+        propagated_features[ivp] = CONFIG_T::input_transform_biases[ip];
+  
+        for (unsigned ix = 0; ix < CONFIG_T::n_in_features; ++ix) {
+          unsigned const ivx = iv * CONFIG_T::n_in_features + ix;
+          unsigned const ipx = ip * CONFIG_T::n_in_features + ix;
+  
+          propagated_features[ivp] += data[ivx] * CONFIG_T::input_transform_weights[ipx];
+        }
+      }
+  
+      for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+        unsigned const iva = iv * CONFIG_T::n_aggregators + ia;
+  
+        typename CONFIG_T::aggr_t distance = CONFIG_T::aggregator_distance_biases[ia];
+  
+        for (unsigned ix = 0; ix < CONFIG_T::n_in_features; ++ix) {
+          unsigned const ivx = iv * CONFIG_T::n_in_features + ix;
+          unsigned const iax = ia * CONFIG_T::n_in_features + ix;
+  
+          distance += data[ivx] * CONFIG_T::aggregator_distance_weights[iax];
+        }
+  
+        edge_weights[iva] = garnet_utils::compute_edge_weight<CONFIG_T>(distance);
+      }
+    }
+  
+    typename CONFIG_T::aggr_t aggregated_features[CONFIG_T::n_aggregators * CONFIG_T::n_propagate];
+  
+    for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+      for (unsigned ip = 0; ip < CONFIG_T::n_propagate; ++ip) {
+        unsigned const iap = ia * CONFIG_T::n_propagate + ip;
+  
+        aggregated_features[iap] = 0.;
+  
+        for (unsigned iv = 0; iv < CONFIG_T::n_vertices; ++iv) {
+          if (iv == nvtx[0])
+            break;
+  
+          unsigned const iva = iv * CONFIG_T::n_aggregators + ia;
+          unsigned const ivp = iv * CONFIG_T::n_propagate + ip;
+  
+          aggregated_features[iap] += edge_weights[iva] * propagated_features[ivp];
+        }
+      }
+    }
+  
+    for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+      for (unsigned ip = 0; ip < CONFIG_T::n_propagate; ++ip) {
+        unsigned const iap = ia * CONFIG_T::n_propagate + ip;
+  
+        if (CONFIG_T::mean_by_nvert)
+          aggregated_features[iap] /= nvtx[0];
+        else {
+          // Not using right shift in case aggr_t is float or double
+          aggregated_features[iap] /= CONFIG_T::n_vertices;
+        }
+      }
+    }
+  
+    for (unsigned iv = 0; iv < CONFIG_T::n_vertices; ++iv) {
+      if (iv == nvtx[0])
+        break;
+  
+      for (unsigned io = 0; io < CONFIG_T::n_out_features; ++io) {
+        unsigned const ivo = iv * CONFIG_T::n_out_features + io;
+  
+        typename CONFIG_T::aggr_t acc = CONFIG_T::output_transform_biases[io];
+  
+        for (unsigned ia = 0; ia < CONFIG_T::n_aggregators; ++ia) {
+          unsigned const iva = iv * CONFIG_T::n_aggregators + ia;
+          unsigned const ioa = io * CONFIG_T::n_aggregators + ia;
+  
+          typename CONFIG_T::aggr_t aggr = 0.;
+  
+          for (unsigned ip = 0; ip < CONFIG_T::n_propagate; ++ip) {
+            unsigned const iap = ia * CONFIG_T::n_propagate + ip;
+            unsigned const ioap = ioa * CONFIG_T::n_propagate + ip;
+  
+            aggr += CONFIG_T::output_transform_weights[ioap] * aggregated_features[iap];
+          }
+  
+          acc += edge_weights[iva] * aggr;
+        }
+  
+        res[ivo] = acc;
+      }
+    }
+  }
+
+  /* Reference (dumb) implementation returning (Features) - output averaged over vertices already */
+  template<class data_T, class nvtx_T, class res_T, typename CONFIG_T>
+  typename std::enable_if<CONFIG_T::output_collapse == CONFIG_T::collapse_mean>::type
+  garnet_ref(
+    data_T const data[CONFIG_T::n_vertices * CONFIG_T::n_in_features],
+    nvtx_T const nvtx[1],
+    res_T res[CONFIG_T::n_out_features]
+  )
+  {
+    typename CONFIG_T::aggr_t vertex_res[CONFIG_T::n_vertices * CONFIG_T::n_out_features];
+
+    garnet_ref<CONFIG_T>(
+      data,
+      nvtx,
+      vertex_res
+    );
+  
+    for (unsigned io = 0; io < CONFIG_T::n_out_features; ++io) {
+      typename CONFIG_T::aggr_t acc = 0.;
+  
+      for (unsigned iv = 0; iv < CONFIG_T::n_vertices; ++iv) {
+        if (iv == nvtx[0])
+          break;
+  
+        unsigned const ivo = iv * CONFIG_T::n_out_features + io;
+  
+        acc += vertex_res[ivo];
+      }
+  
+      if (CONFIG_T::mean_by_nvert)
+        acc /= nvtx[0];
+      else {
+        // Not using right shift in case aggr_t is float or double
+        acc /= CONFIG_T::n_vertices;
+      }
+  
+      res[io] = acc;
+    }
+  }
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_helpers.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_helpers.h
new file mode 100644
index 0000000..eed64fc
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_helpers.h
@@ -0,0 +1,433 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_HELPERS_H
+#define NNET_HELPERS_H
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <fstream>
+#include <algorithm>
+#include <vector>
+#include <map>
+#include <iostream>
+#include "hls_stream.h"
+
+namespace nnet {
+
+#ifndef __SYNTHESIS__
+
+#ifndef WEIGHTS_DIR
+#define WEIGHTS_DIR "weights"
+#endif
+
+template<class T, size_t SIZE>
+void load_weights_from_txt(T *w, const char* fname) {
+
+    std::string full_path = std::string(WEIGHTS_DIR) + "/" + std::string(fname);
+    std::ifstream infile(full_path.c_str(), std::ios::binary);
+
+    if (infile.fail()) {
+        std::cerr << "ERROR: file " << std::string(fname) << " does not exist" << std::endl;
+        exit(1);
+    }
+
+    std::string line;
+    if (std::getline(infile, line)) {
+        std::istringstream iss(line);
+        std::string token;
+
+        size_t i = 0;
+        while(std::getline(iss, token, ',')) {
+            std::istringstream(token) >> w[i];
+            i++;
+        }
+
+        if (SIZE != i) {
+            std::cerr << "ERROR: Expected " << SIZE << " values";
+            std::cerr << " but read only " << i << " values" << std::endl;
+        }
+    }
+}
+
+template<class T, size_t SIZE>
+void load_compressed_weights_from_txt(T *w, const char* fname) {
+
+    std::string full_path = std::string(WEIGHTS_DIR) + "/" + std::string(fname);
+    std::ifstream infile(full_path.c_str(), std::ios::binary);
+
+    if (infile.fail()) {
+        std::cerr << "ERROR: file " << std::string(fname) << " does not exist" << std::endl;
+        exit(1);
+    }
+
+    std::string line;
+    if (std::getline(infile, line)) {
+        std::istringstream iss(line);
+        std::string token;
+        std::string extra_chars = "} ";
+
+        size_t i = 0;
+        while(std::getline(iss, token, '{')) {
+            if (token.length() == 0) {
+                continue;
+            }
+            for (char c: extra_chars) {
+                token.erase(std::remove(token.begin(), token.end(), c), token.end());
+            }
+            if (token.back() == ',') {
+                token.erase(token.end() - 1);
+            }
+
+            std::replace(token.begin(), token.end(), ',', ' ');
+            std::istringstream structss(token);
+
+            if(!(structss >> w[i].row_index >> w[i].col_index >> w[i].weight)) {
+                std::cerr << "ERROR: Unable to parse file " << std::string(fname);
+                exit(1);
+            }
+            i++;
+        }
+
+        if (SIZE != i) {
+            std::cerr << "ERROR: Expected " << SIZE << " values";
+            std::cerr << " but read only " << i << " values" << std::endl;
+        }
+    }
+}
+
+template<class T, size_t SIZE>
+void load_exponent_weights_from_txt(T *w, const char* fname) {
+
+    std::string full_path = std::string(WEIGHTS_DIR) + "/" + std::string(fname);
+    std::ifstream infile(full_path.c_str(), std::ios::binary);
+
+    if (infile.fail()) {
+        std::cerr << "ERROR: file " << std::string(fname) << " does not exist" << std::endl;
+        exit(1);
+    }
+
+    std::string line;
+    if (std::getline(infile, line)) {
+        std::istringstream iss(line);
+        std::string token;
+        std::string extra_chars = "} ";
+
+        size_t i = 0;
+        while(std::getline(iss, token, '{')) {
+            if (token.length() == 0) {
+                continue;
+            }
+            for (char c: extra_chars) {
+                token.erase(std::remove(token.begin(), token.end(), c), token.end());
+            }
+            if (token.back() == ',') {
+                token.erase(token.end() - 1);
+            }
+
+            std::replace(token.begin(), token.end(), ',', ' ');
+            std::istringstream structss(token);
+
+            if(!(structss >> w[i].sign >> w[i].weight)) {
+                std::cerr << "ERROR: Unable to parse file " << std::string(fname);
+                exit(1);
+            }
+            i++;
+        }
+
+        if (SIZE != i) {
+            std::cerr << "ERROR: Expected " << SIZE << " values";
+            std::cerr << " but read only " << i << " values" << std::endl;
+        }
+    }
+}
+template<class srcType, class dstType, size_t SIZE>
+void convert_data(srcType *src, dstType *dst) {
+    for (size_t i = 0; i < SIZE; i++) {
+        dst[i] = dstType(src[i]);
+    }
+}
+
+template<class srcType, class dstType, size_t SIZE>
+void convert_data(srcType *src, hls::stream<dstType> &dst) {
+    for (size_t i = 0; i < SIZE / dstType::size; i++) {
+        dstType ctype;
+        for (size_t j = 0; j < dstType::size; j++) {
+            ctype[j] = typename dstType::value_type(src[i * dstType::size + j]);
+        }
+        dst.write(ctype);
+    }
+}
+
+template<class srcType, class dstType, size_t SIZE>
+void convert_data(hls::stream<srcType> &src, dstType *dst) {
+    for (size_t i = 0; i < SIZE / srcType::size; i++) {
+        srcType ctype = src.read();
+        for (size_t j = 0; j < srcType::size; j++) {
+            dst[i * srcType::size + j] = dstType(ctype[j]);
+        }
+    }
+}
+
+extern bool trace_enabled;
+extern std::map<std::string, void *> *trace_outputs;
+extern size_t trace_type_size;
+
+template<class data_T, class save_T>
+void save_output_array(data_T *data, save_T *ptr, size_t layer_size) {
+    for(int i = 0; i < layer_size; i++) {
+        ptr[i] = save_T(data[i]);
+    }
+}
+
+template<class data_T, class save_T>
+void save_output_array(hls::stream<data_T> &data, save_T *ptr, size_t layer_size) {
+    for (size_t i = 0; i < layer_size / data_T::size; i++) {
+        data_T ctype = data.read();
+        for (size_t j = 0; j < data_T::size; j++) {
+            ptr[i * data_T::size + j] = save_T(ctype[j]);
+        }
+        data.write(ctype);
+    }
+}
+
+// We don't want to include save_T in this function because it will be inserted into myproject.cpp
+// so a workaround with element size is used
+template<class data_T>
+void save_layer_output(data_T *data, const char *layer_name, size_t layer_size) {
+    if (!trace_enabled) return;
+    
+    if (trace_outputs) {
+        if (trace_outputs->count(layer_name) > 0) {
+            if (trace_type_size == 4) {
+                save_output_array<data_T, float>(data, (float *) (*trace_outputs)[layer_name], layer_size);
+            } else if (trace_type_size == 8) {
+                save_output_array<data_T, double>(data, (double *) (*trace_outputs)[layer_name], layer_size);
+            } else {
+                std::cout << "Unknown trace type!" << std::endl;
+            }
+        } else {
+            std::cout << "Layer name: " << layer_name << " not found in debug storage!" << std::endl;
+        }
+    } else {
+        std::ostringstream filename;
+        filename << "./tb_data/" << layer_name << "_output.log"; //TODO if run as a shared lib, path should be ../tb_data
+        std::fstream out;
+        out.open(filename.str(), std::ios::app);
+        assert(out.is_open());
+        for(int i = 0; i < layer_size; i++) {
+            out << float(data[i]) << " "; // We don't care about precision in text files
+        }
+        out << std::endl;
+        out.close();
+    }
+}
+
+template<class data_T>
+void save_layer_output(hls::stream<data_T> &data, const char *layer_name, size_t layer_size) {
+    if (!trace_enabled) return;
+    
+    if (trace_outputs) {
+        if (trace_outputs->count(layer_name) > 0) {
+            if (trace_type_size == 4) {
+                save_output_array<data_T, float>(data, (float *) (*trace_outputs)[layer_name], layer_size);
+            } else if (trace_type_size == 8) {
+                save_output_array<data_T, double>(data, (double *) (*trace_outputs)[layer_name], layer_size);
+            } else {
+                std::cout << "Unknown trace type!" << std::endl;
+            }
+        } else {
+            std::cout << "Layer name: " << layer_name << " not found in debug storage!" << std::endl;
+        }
+    } else {
+        std::ostringstream filename;
+        filename << "./tb_data/" << layer_name << "_output.log"; //TODO if run as a shared lib, path should be ../tb_data
+        std::fstream out;
+        out.open(filename.str(), std::ios::app);
+        assert(out.is_open());
+        for (size_t i = 0; i < layer_size / data_T::size; i++) {
+            data_T ctype = data.read();
+            for (size_t j = 0; j < data_T::size; j++) {
+                out << float(ctype[j]) << " "; // We don't care about precision in text files
+            }
+            data.write(ctype);
+        }
+        out << std::endl;
+        out.close();
+    }
+}
+
+
+#endif
+
+template<class src_T, class dst_T, size_t OFFSET, size_t SIZE>
+void copy_data(std::vector<src_T> src, dst_T dst[SIZE]) {
+    typename std::vector<src_T>::const_iterator in_begin = src.cbegin() + OFFSET;
+    typename std::vector<src_T>::const_iterator in_end = in_begin + SIZE;
+    std::copy(in_begin, in_end, dst);
+}
+
+template<class src_T, class dst_T, size_t OFFSET, size_t SIZE>
+void copy_data(std::vector<src_T> src, hls::stream<dst_T> &dst) {
+    typename std::vector<src_T>::const_iterator in_begin = src.cbegin() + OFFSET;
+    typename std::vector<src_T>::const_iterator in_end = in_begin + SIZE;
+
+    size_t i_pack = 0;
+    dst_T dst_pack;
+    for (typename std::vector<src_T>::const_iterator i = in_begin; i != in_end; ++i) {
+        dst_pack[i_pack++] = typename dst_T::value_type(*i);
+        if (i_pack == dst_T::size) {
+            i_pack = 0;
+            dst.write(dst_pack);
+        }
+    }
+}
+
+template<class src_T, class dst_T, size_t OFFSET, size_t SIZE>
+void copy_data_axi(std::vector<src_T> src, dst_T dst[SIZE]) {
+    for(auto i = 0; i < SIZE; i++)
+    	if(i == SIZE - 1)
+    	{
+    		dst[i].data = src[i];
+    		dst[i].last = 1;
+    	}
+    	else
+    	{
+    		dst[i].data = src[i];
+    		dst[i].last = 0;
+    	}
+}
+
+template<class res_T, size_t SIZE>
+void print_result(res_T result[SIZE], std::ostream &out, bool keep = false) {
+    for(int i = 0; i < SIZE; i++) {
+        out << result[i] << " ";
+    }
+    out << std::endl;
+}
+
+template<class res_T, size_t SIZE>
+void print_result(hls::stream<res_T> &result, std::ostream &out, bool keep = false) {
+    for(int i = 0; i < SIZE / res_T::size; i++) {
+        res_T res_pack = result.read();
+        for(int j = 0; j < res_T::size; j++) {
+            out << res_pack[j] << " ";
+        }
+        if (keep) result.write(res_pack);
+    }
+    out << std::endl;
+}
+
+template<class data_T, size_t SIZE>
+void fill_zero(data_T data[SIZE]) {
+    std::fill_n(data, SIZE, 0.);
+}
+
+template<class data_T, size_t SIZE>
+void fill_zero(hls::stream<data_T> &data) {
+    for(int i = 0; i < SIZE / data_T::size; i++) {
+        data_T data_pack;
+        for(int j = 0; j < data_T::size; j++) {
+            data_pack[j] = 0.;
+        }
+        data.write(data_pack);
+    }
+}
+
+template <class dataType, unsigned int nrows>
+int read_file_1D(const char * filename, dataType data[nrows])
+{
+  FILE *fp;
+  fp = fopen(filename, "r");
+  if (fp == 0) {
+    return -1;
+  }
+  // Read data from file
+  float newval;
+  for (int ii = 0; ii < nrows; ii++){
+    if (fscanf(fp, "%f\n", &newval) != 0){
+      data[ii] = newval;
+    } else {
+      return -2;
+    }
+  }
+  fclose(fp);
+  return 0;
+}
+
+template <class dataType, unsigned int nrows, unsigned int ncols>
+int read_file_2D(const char * filename, dataType data[nrows][ncols])
+{
+  FILE *fp;
+  fp = fopen(filename, "r");
+  if (fp == 0) {
+    return -1;
+  }
+  // Read data from file
+  float newval;
+  for (int ii = 0; ii < nrows; ii++) {
+    for (int jj = 0; jj < ncols; jj++){
+      if (fscanf(fp, "%f\n", &newval) != 0){
+        data[ii][jj] = newval;
+      } else {
+        return -2;
+      }
+    }
+  }
+  fclose(fp);
+  return 0;
+}
+
+template<class in_T, class out_T, int N_IN>
+void change_type(hls::stream<in_T> &in, hls::stream<out_T> &out)
+{
+    in_T datareg;
+    hls::stream<out_T> input_trunc;
+    for (int ii=0; ii<N_IN; ii++) {
+        out << (out_T) in.read();
+    }
+}
+
+template<class data_T, int N_IN>
+void  hls_stream_debug(hls::stream<data_T> &data, hls::stream<data_T> &res)
+{
+    data_T datareg;
+    for (int ii=0; ii<N_IN; ii++) {
+        datareg = data.read();
+        std::cout << "[" << ii << "]: " << datareg << std::endl;
+        res << datareg;
+    }
+}
+
+constexpr int ceillog2(int x){
+  return (x <= 2) ? 1 : 1 + ceillog2((x+1) / 2);
+}
+
+constexpr int floorlog2(int x){
+  return (x < 2) ? 0 : 1 + floorlog2(x / 2);
+}
+
+constexpr int pow2(int x){
+  return x == 0 ? 1 : 2 * pow2(x - 1);
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_image.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_image.h
new file mode 100644
index 0000000..e17c7a7
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_image.h
@@ -0,0 +1,42 @@
+#ifndef NNET_IMAGE_H_
+#define NNET_IMAGE_H_
+
+#include "nnet_common.h"
+#include "hls_stream.h"
+#include <math.h>
+
+namespace nnet {
+
+struct resize_config {
+    static const unsigned height = 10;
+    static const unsigned width = 10;
+    static const unsigned n_chan = 10;
+    static const unsigned new_height = 10;
+    static const unsigned new_width = 10;
+};
+
+template<class data_T, typename CONFIG_T>
+void resize_nearest(
+    data_T image[CONFIG_T::height * CONFIG_T::width * CONFIG_T::n_chan],
+    data_T resized[CONFIG_T::new_height * CONFIG_T::new_width * CONFIG_T::n_chan]
+) {
+    int y_ratio = (int)((CONFIG_T::height << 16) / CONFIG_T::new_height) + 1;
+    int x_ratio = (int)((CONFIG_T::width << 16) / CONFIG_T::new_width) + 1;
+    int x2, y2;
+
+    #pragma HLS PIPELINE
+
+    for (int i = 0; i < CONFIG_T::new_height; i++) {
+        for (int j = 0; j < CONFIG_T::new_width; j++) {
+            x2 = ((j * x_ratio) >> 16);
+            y2 = ((i * y_ratio) >> 16);
+            for (int k = 0; k < CONFIG_T::n_chan; k++) {
+                resized[(i * CONFIG_T::new_width * CONFIG_T::n_chan) + j * CONFIG_T::n_chan + k] = image[(y2 * CONFIG_T::width * CONFIG_T::n_chan) + x2 * CONFIG_T::n_chan + k];
+            }
+        }
+    }  
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_image_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_image_stream.h
new file mode 100644
index 0000000..e25f5fe
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_image_stream.h
@@ -0,0 +1,43 @@
+#ifndef NNET_IMAGE_STREAM_H_
+#define NNET_IMAGE_STREAM_H_
+
+#include "nnet_common.h"
+#include "hls_stream.h"
+
+namespace nnet {
+
+template<class data_T, typename CONFIG_T>
+void resize_nearest(
+    hls::stream<data_T> &image,
+    hls::stream<data_T> &resized
+) {
+    assert(CONFIG_T::new_height % CONFIG_T::height == 0);
+    assert(CONFIG_T::new_width % CONFIG_T::width == 0);
+    constexpr unsigned ratio_height = CONFIG_T::new_height / CONFIG_T::height;
+    constexpr unsigned ratio_width = CONFIG_T::new_width / CONFIG_T::width;
+    constexpr unsigned ii = ratio_height * ratio_width;
+
+    ResizeImage: for (unsigned i = 0; i < CONFIG_T::height * CONFIG_T::width; i++) {
+        #pragma HLS PIPELINE II=ii
+        
+        data_T  in_data = image.read();
+
+        ResizeNew: for (unsigned j = 0; j < ratio_height * ratio_width; j++) {
+            #pragma HLS UNROLL
+
+            data_T out_data;
+            #pragma HLS DATA_PACK variable=out_data
+            
+            ResizeChan: for (unsigned k = 0; k < CONFIG_T::n_chan; k++) {
+                #pragma HLS UNROLL
+                out_data[k] = in_data[k];
+            }
+
+            resized.write(out_data);
+        }
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_merge.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_merge.h
new file mode 100644
index 0000000..48a5e17
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_merge.h
@@ -0,0 +1,302 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_MERGE_H_
+#define NNET_MERGE_H_
+
+#include "nnet_common.h"
+#include "nnet_mult.h"
+#include "hls_stream.h"
+#include <math.h>
+
+namespace nnet {
+
+struct merge_config
+{
+    static const unsigned n_elem = 10;
+};
+
+struct dot_config {
+    static const unsigned n_in = 10;
+    static const unsigned n_out = 1;
+    static const unsigned reuse_factor = 1;
+    typedef float accum_t;
+    // Product function to use
+    template<class x_T, class y_T, class res_T>
+    using product = nnet::product::mult<x_T, y_T, res_T>;
+};
+
+struct concat_config {
+    static const unsigned n_elem1_0 = 10;
+    static const unsigned n_elem1_1 = 10;
+    static const unsigned n_elem1_2 = 10;
+    static const unsigned n_elem2_0 = 10;
+    static const unsigned n_elem2_1 = 10;
+    static const unsigned n_elem2_2 = 10;
+
+    static const unsigned axis = -1;
+};
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void add(
+    input1_T data1[CONFIG_T::n_elem],
+    input2_T data2[CONFIG_T::n_elem],
+    res_T res[CONFIG_T::n_elem])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem; ii++) {
+        res[ii] = data1[ii] + data2[ii];
+    }
+}
+
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void subtract(
+    input1_T data1[CONFIG_T::n_elem],
+    input2_T data2[CONFIG_T::n_elem],
+    res_T res[CONFIG_T::n_elem])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem; ii++) {
+        res[ii] = data1[ii] - data2[ii];
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void multiply(
+    input1_T data1[CONFIG_T::n_elem],
+    input2_T data2[CONFIG_T::n_elem],
+    res_T res[CONFIG_T::n_elem])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem; ii++) {
+        res[ii] = data1[ii] * data2[ii];
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void average(
+    input1_T data1[CONFIG_T::n_elem],
+    input2_T data2[CONFIG_T::n_elem],
+    res_T res[CONFIG_T::n_elem])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem; ii++) {
+        res[ii] = (data1[ii] + data2[ii]) / (res_T) 2;
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void maximum(
+    input1_T data1[CONFIG_T::n_elem],
+    input2_T data2[CONFIG_T::n_elem],
+    res_T res[CONFIG_T::n_elem])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem; ii++) {
+        res[ii] = (data1[ii] > data2[ii]) ? data1[ii] : data2[ii];
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void minimum(
+    input1_T data1[CONFIG_T::n_elem],
+    input2_T data2[CONFIG_T::n_elem],
+    res_T res[CONFIG_T::n_elem])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem; ii++) {
+        res[ii] = (data1[ii] < data2[ii]) ? data1[ii] : data2[ii];
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void dot1d(
+    input1_T data1[CONFIG_T::n_in],
+    input2_T data2[CONFIG_T::n_in],
+    res_T res[CONFIG_T::n_out])
+{
+    #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+
+    constexpr unsigned multiplier_limit = DIV_ROUNDUP(CONFIG_T::n_in, CONFIG_T::reuse_factor);
+    CONFIG_T::template product<input1_T, input2_T, typename CONFIG_T::accum_t>::limit(multiplier_limit);
+
+    typename CONFIG_T::accum_t mult[CONFIG_T::n_in];
+    #pragma HLS ARRAY_PARTITION variable=mult complete
+    typename CONFIG_T::accum_t acc = 0;
+
+    Product: for(int i_mult=0; i_mult < CONFIG_T::n_in; i_mult++) {
+        #pragma HLS UNROLL
+        mult[i_mult] = CONFIG_T::template product<input1_T, input2_T, typename CONFIG_T::accum_t>::product(data1[i_mult], data2[i_mult]);
+    }
+
+    Accum: for(int i_acc = 0; i_acc < CONFIG_T::n_in; i_acc++) {
+        #pragma HLS UNROLL
+        acc += mult[i_acc];
+    }
+
+    res[0] = cast<input1_T, res_T, CONFIG_T>(acc);
+}
+
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate1d(
+    input1_T data1[CONFIG_T::n_elem1_0],
+    input2_T data2[CONFIG_T::n_elem2_0],
+    res_T res[CONFIG_T::n_elem1_0 + CONFIG_T::n_elem2_0])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem1_0; ii++) {
+        res[ii] = data1[ii];
+    }
+    for (int ii=0; ii<CONFIG_T::n_elem2_0; ii++) {
+        res[CONFIG_T::n_elem1_0 + ii] = data2[ii];
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate2d_0(
+    input1_T data1[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1],
+    input2_T data2[CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1],
+    res_T res[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 + CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1; ii++) {
+        res[ii] = data1[ii];
+    }
+    for (int ii=0; ii<CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1; ii++) {
+        res[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 + ii] = data2[ii];
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate2d_1(
+    input1_T data1[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1],
+    input2_T data2[CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1],
+    res_T res[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 + CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem1_0; ii++) {
+        for (int jj=0; jj<CONFIG_T::n_elem1_1; jj++) {
+            res[ii * (CONFIG_T::n_elem1_1 + CONFIG_T::n_elem2_1) + jj] = data1[ii * CONFIG_T::n_elem1_1 + jj];
+        }
+        for (int jj=0; jj<CONFIG_T::n_elem2_1; jj++) {
+            res[ii * (CONFIG_T::n_elem1_1 + CONFIG_T::n_elem2_1) + CONFIG_T::n_elem1_1 + jj] = data2[ii * CONFIG_T::n_elem2_1 + jj];
+        }
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate2d(
+    input1_T data1[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1],
+    input2_T data2[CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1],
+    res_T res[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 + CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1])
+{
+    if (CONFIG_T::axis == 2 || CONFIG_T::axis == -1) {
+        concatenate2d_1<input1_T, input2_T, res_T, CONFIG_T>(data1, data2, res);
+    } else {
+        concatenate2d_0<input1_T, input2_T, res_T, CONFIG_T>(data1, data2, res);
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate3d_0(
+input1_T data1[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2],
+    input2_T data2[CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2],
+    res_T res[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2 + CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2; ii++) {
+        res[ii] = data1[ii];
+    }
+    for (int ii=0; ii<CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2; ii++) {
+        res[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2 + ii] = data2[ii];
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate3d_1(
+input1_T data1[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2],
+    input2_T data2[CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2],
+    res_T res[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2 + CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem1_0; ii++) {
+        for (int jj=0; jj<CONFIG_T::n_elem1_1; jj++) {
+            for (int kk=0; kk<CONFIG_T::n_elem1_2; kk++) {
+                int res_idx = ii * (CONFIG_T::n_elem1_1 + CONFIG_T::n_elem2_1) * CONFIG_T::n_elem1_2
+                            + jj * CONFIG_T::n_elem1_2
+                            + kk;
+                int data_idx = ii * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2
+                             + jj * CONFIG_T::n_elem1_2
+                             + kk;
+                res[res_idx] = data1[data_idx];
+            }
+        }
+        for (int jj=0; jj<CONFIG_T::n_elem2_1; jj++) {
+            for (int kk=0; kk<CONFIG_T::n_elem2_2; kk++) {
+                int res_idx = ii * (CONFIG_T::n_elem1_1 + CONFIG_T::n_elem2_1) * CONFIG_T::n_elem1_2
+                            + (jj + CONFIG_T::n_elem1_1) * CONFIG_T::n_elem1_2
+                            + kk;
+                int data_idx = ii * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2
+                             + jj * CONFIG_T::n_elem2_2
+                             + kk;
+                res[res_idx] = data2[data_idx];
+            }
+        }
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate3d_2(
+    input1_T data1[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2],
+    input2_T data2[CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2],
+    res_T res[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2 + CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2])
+{
+    for (int ii=0; ii<CONFIG_T::n_elem1_0; ii++) {
+        for (int jj=0; jj<CONFIG_T::n_elem1_1; jj++) {
+            for (int kk=0; kk<CONFIG_T::n_elem1_2; kk++) {
+                int res_idx = ii * CONFIG_T::n_elem1_1 * (CONFIG_T::n_elem1_2 + CONFIG_T::n_elem2_2)
+                            + jj * (CONFIG_T::n_elem1_2 + CONFIG_T::n_elem2_2)
+                            + kk;
+                int data_idx = ii * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2
+                             + jj * CONFIG_T::n_elem1_2
+                             + kk;
+                res[res_idx] = data1[data_idx];
+            }
+            for (int kk=0; kk<CONFIG_T::n_elem1_2; kk++) {
+                int res_idx = ii * CONFIG_T::n_elem1_1 * (CONFIG_T::n_elem1_2 + CONFIG_T::n_elem2_2)
+                            + jj * (CONFIG_T::n_elem1_2 + CONFIG_T::n_elem2_2)
+                            + kk + CONFIG_T::n_elem1_2;
+                int data_idx = ii * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2
+                             + jj * CONFIG_T::n_elem2_2
+                             + kk;
+                res[res_idx] = data2[data_idx];
+            }
+        }
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate3d(
+    input1_T data1[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2],
+    input2_T data2[CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2],
+    res_T res[CONFIG_T::n_elem1_0 * CONFIG_T::n_elem1_1 * CONFIG_T::n_elem1_2 + CONFIG_T::n_elem2_0 * CONFIG_T::n_elem2_1 * CONFIG_T::n_elem2_2])
+{
+    if (CONFIG_T::axis == 3 || CONFIG_T::axis == -1) {
+        concatenate3d_2<input1_T, input2_T, res_T, CONFIG_T>(data1, data2, res);
+    } else if (CONFIG_T::axis == 2 || CONFIG_T::axis == -2) {
+        concatenate3d_1<input1_T, input2_T, res_T, CONFIG_T>(data1, data2, res);
+    } else {
+        concatenate3d_0<input1_T, input2_T, res_T, CONFIG_T>(data1, data2, res);
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_merge_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_merge_stream.h
new file mode 100644
index 0000000..e7a02ce
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_merge_stream.h
@@ -0,0 +1,304 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+
+#ifndef NNET_MERGE_STREAM_H_
+#define NNET_MERGE_STREAM_H_
+
+#include "nnet_common.h"
+#include "hls_stream.h"
+#include <math.h>
+
+namespace nnet {
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void add(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    assert(input1_T::size == input2_T::size && input1_T::size == res_T::size);
+
+    AddLoop: for (int i = 0; i < CONFIG_T::n_elem / input1_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        input1_T in_data1 = data1.read();
+        input2_T in_data2 = data2.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        AddPack: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            out_data[j] = in_data1[j] + in_data2[j];
+        }
+
+        res.write(out_data);
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void subtract(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    assert(input1_T::size == input2_T::size && input1_T::size == res_T::size);
+
+    SubtractLoop: for (int i = 0; i < CONFIG_T::n_elem / input1_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        input1_T in_data1 = data1.read();
+        input2_T in_data2 = data2.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        SubtractPack: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            out_data[j] = in_data1[j] - in_data2[j];
+        }
+
+        res.write(out_data);
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void multiply(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    assert(input1_T::size == input2_T::size && input1_T::size == res_T::size);
+
+    MultiplyLoop: for (int i = 0; i < CONFIG_T::n_elem / input1_T::size; i++) {
+        #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+
+        input1_T in_data1 = data1.read();
+        input2_T in_data2 = data2.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        MultiplyPack: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            out_data[j] = in_data1[j] * in_data2[j];
+        }
+
+        res.write(out_data);
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void average(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    assert(input1_T::size == input2_T::size && input1_T::size == res_T::size);
+
+    AverageLoop: for (int i = 0; i < CONFIG_T::n_elem / input1_T::size; i++) {
+        #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+
+        input1_T in_data1 = data1.read();
+        input2_T in_data2 = data2.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        AveragePack: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            out_data[j] = (in_data1[j] + in_data2[j]) / (typename res_T::value_type) 2;
+        }
+
+        res.write(out_data);
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void maximum(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    assert(input1_T::size == input2_T::size && input1_T::size == res_T::size);
+
+    MaximumLoop: for (int i = 0; i < CONFIG_T::n_elem / input1_T::size; i++) {
+        #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+
+        input1_T in_data1 = data1.read();
+        input2_T in_data2 = data2.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        MaximumPack: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            out_data[j] = (in_data1[j] > in_data2[j]) ? in_data1[j] : in_data2[j];
+        }
+
+        res.write(out_data);
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void minimum(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    assert(input1_T::size == input2_T::size && input1_T::size == res_T::size);
+
+    MinimumLoop: for (int i = 0; i < CONFIG_T::n_elem / input1_T::size; i++) {
+        #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+
+        input1_T in_data1 = data1.read();
+        input2_T in_data2 = data2.read();
+        res_T out_data;
+        #pragma HLS DATA_PACK variable=out_data
+
+        MinimumPack: for (int j = 0; j < res_T::size; j++) {
+            #pragma HLS UNROLL
+            out_data[j] = (in_data1[j] < in_data2[j]) ? in_data1[j] : in_data2[j];
+        }
+
+        res.write(out_data);
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate3d_0(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    ConcatLoopHeight1: for (int i = 0; i < CONFIG_T::n_elem1_0; i++) {
+        ConcatLoopWidth1: for (int j = 0; j < CONFIG_T::n_elem1_1; j++) {
+            #pragma HLS PIPELINE II=1
+
+            input1_T in_data1 = data1.read();
+            res_T out_data;
+            #pragma HLS DATA_PACK variable=out_data
+
+            ConcatPackInput1: for (int k = 0; k < input1_T::size; k++) {
+                #pragma HLS UNROLL
+                out_data[k] = in_data1[k];
+            }
+
+            res.write(out_data);
+        }
+    }
+    ConcatLoopHeight2: for (int i = 0; i < CONFIG_T::n_elem2_0; i++) {
+        ConcatLoopWidth2: for (int j = 0; j < CONFIG_T::n_elem2_1; j++) {
+            #pragma HLS PIPELINE II=1
+
+            input2_T in_data2 = data2.read();
+            res_T out_data;
+            #pragma HLS DATA_PACK variable=out_data
+
+            ConcatPackInput2: for (int k = 0; k < input2_T::size; k++) {
+                #pragma HLS UNROLL
+                out_data[k] = in_data2[k];
+            }
+
+            res.write(out_data);
+        }
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate3d_1(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    ConcatLoopHeight: for (int i = 0; i < CONFIG_T::n_elem1_0; i++) {
+        ConcatLoopWidth1: for (int j = 0; j < CONFIG_T::n_elem1_1; j++) {
+            #pragma HLS PIPELINE II=1
+
+            input1_T in_data1 = data1.read();
+            res_T out_data;
+            #pragma HLS DATA_PACK variable=out_data
+
+            ConcatPackInput1: for (int k = 0; k < input1_T::size; k++) {
+                #pragma HLS UNROLL
+                out_data[k] = in_data1[k];
+            }
+
+            res.write(out_data);
+        }
+        ConcatLoopWidth2: for (int j = 0; j < CONFIG_T::n_elem2_1; j++) {
+            #pragma HLS PIPELINE II=1
+
+            input2_T in_data2 = data2.read();
+            res_T out_data;
+            #pragma HLS DATA_PACK variable=out_data
+
+            ConcatPackInput2: for (int k = 0; k < input2_T::size; k++) {
+                #pragma HLS UNROLL
+                out_data[k] = in_data2[k];
+            }
+
+            res.write(out_data);
+        }
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate3d_2(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    ConcatLoopHeight: for (int i = 0; i < CONFIG_T::n_elem1_0; i++) {
+        ConcatLoopWidth: for (int j = 0; j < CONFIG_T::n_elem1_1; j++) {
+            #pragma HLS PIPELINE II=1
+
+            input1_T in_data1 = data1.read();
+            input2_T in_data2 = data2.read();
+            res_T out_data;
+            #pragma HLS DATA_PACK variable=out_data
+
+            ConcatPackInput1: for (int k = 0; k < input1_T::size; k++) {
+                #pragma HLS UNROLL
+                out_data[k] = in_data1[k];
+            }
+            
+            ConcatPackInput2: for (int k = 0; k < input2_T::size; k++) {
+                #pragma HLS UNROLL
+                out_data[input1_T::size + k] = in_data2[k];
+            }
+
+            res.write(out_data);
+        }
+    }
+}
+
+template<class input1_T, class input2_T, class res_T, typename CONFIG_T>
+void concatenate3d(
+    hls::stream<input1_T> &data1,
+    hls::stream<input2_T> &data2,
+    hls::stream<res_T> &res)
+{
+    if (CONFIG_T::axis == 3 || CONFIG_T::axis == -1) {
+        concatenate3d_2<input1_T, input2_T, res_T, CONFIG_T>(data1, data2, res);
+    } else if (CONFIG_T::axis == 2 || CONFIG_T::axis == -2) {
+        concatenate3d_1<input1_T, input2_T, res_T, CONFIG_T>(data1, data2, res);
+    } else {
+        concatenate3d_0<input1_T, input2_T, res_T, CONFIG_T>(data1, data2, res);
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_mult.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_mult.h
new file mode 100644
index 0000000..3a597f0
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_mult.h
@@ -0,0 +1,122 @@
+#ifndef NNET_MULT_H_
+#define NNET_MULT_H_
+
+#include "nnet_common.h"
+#include "nnet_helpers.h"
+#include "hls_stream.h"
+#include <math.h>
+
+namespace nnet {
+
+namespace product{
+
+/* ---
+ * 5 different methods to perform the product of input and weight, depending on the
+ * types of each. 
+ * --- */
+
+template<class x_T, class w_T, class y_T>
+class Product{
+    public:
+    static y_T product(x_T a, w_T w){
+        // 'Normal' product
+        #pragma HLS INLINE
+        return a * w;
+    }
+    static void limit(unsigned multiplier_limit) {} // Nothing to do here
+};
+
+template<class x_T, class w_T, class y_T>
+class both_binary : public Product<x_T, w_T, y_T>{
+    public:
+    static y_T product(x_T a, w_T w){
+        // specialisation for 1-bit weights and incoming data
+        #pragma HLS INLINE
+        return a == w;
+    }
+};
+
+template<class x_T, class w_T, class y_T>
+class weight_binary : public Product<x_T, w_T, y_T>{
+    public:
+    static y_T product(x_T a, w_T w){
+        // Specialisation for 1-bit weights, arbitrary data
+        #pragma HLS INLINE
+        return w == 0 ? (x_T) -a : a;
+    }
+};
+
+template<class x_T, class w_T, class y_T>
+class data_binary : public Product<x_T, w_T, y_T>{
+    public:
+    static y_T product(x_T a, w_T w){
+        // Specialisation for 1-bit data, arbitrary weight
+        #pragma HLS INLINE
+        return a == 0 ? (w_T) -w : w;
+    }
+};
+
+template<class x_T, class w_T, class y_T>
+class weight_ternary : public Product<x_T, w_T, y_T>{
+    public:
+    static y_T product(x_T a, w_T w){
+        // Specialisation for 2-bit weights, arbitrary data
+        #pragma HLS INLINE
+        if (w == 0) return (x_T) 0;
+        else if(w == -1) return (x_T) -a;
+        else return (x_T) a; // if(w == 1)
+    }
+};
+
+template<class x_T, class w_T, class y_T>
+class mult : public Product<x_T, w_T, y_T>{
+    public:
+    static y_T product(x_T a, w_T w){
+        // 'Normal' product
+        #pragma HLS INLINE
+        return a * w;
+    }
+    static void limit(unsigned multiplier_limit){
+        #pragma HLS INLINE
+        #pragma HLS ALLOCATION instances=mul limit=multiplier_limit operation
+    }
+};
+
+template<class x_T, class w_T, class y_T>
+class weight_exponential : public Product<x_T, w_T, y_T>{
+    public:
+    static y_T product(x_T a, w_T w){
+        // Shift product for exponential weights
+        #pragma HLS INLINE
+        // shift by the exponent. Negative weights shift right
+        y_T y = a << w.weight;
+        // negate or not depending on weight sign
+        return w.sign == 1 ? (y_T) y : (y_T) -y;
+    }
+};
+
+} // namespace product_type
+
+template<class data_T, class res_T, typename CONFIG_T>
+inline typename std::enable_if<std::is_same<data_T, ap_uint<1>>::value
+        && std::is_same<typename CONFIG_T::weight_t, ap_uint<1>>::value, ap_int<nnet::ceillog2(CONFIG_T::n_in) + 2>>::type
+cast(typename CONFIG_T::accum_t x){
+  return (ap_int<nnet::ceillog2(CONFIG_T::n_in) + 2>) (x - CONFIG_T::n_in / 2) * 2;
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+inline typename std::enable_if<std::is_same<data_T, ap_uint<1>>::value
+        && ! std::is_same<typename CONFIG_T::weight_t, ap_uint<1>>::value, res_T>::type
+cast(typename CONFIG_T::accum_t x){
+  return (res_T) x;
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+inline typename std::enable_if<(! std::is_same<data_T, ap_uint<1>>::value), res_T>::type
+cast(typename CONFIG_T::accum_t x){
+  return (res_T) x;
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_padding.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_padding.h
new file mode 100644
index 0000000..6a2c713
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_padding.h
@@ -0,0 +1,148 @@
+#ifndef NNET_PADDING_H_
+#define NNET_PADDING_H_
+
+#include <math.h>
+
+namespace nnet {
+
+struct padding1d_config {
+    static const unsigned n_chan = 10;
+    static const unsigned in_width = 10;
+    static const unsigned out_width = 10;
+    static const unsigned pad_left = 0;
+    static const unsigned pad_right = 0;
+};
+
+template<class data_T, class res_T, typename CONFIG_T>
+void zeropad1d_cf(
+    data_T data[CONFIG_T::n_chan * CONFIG_T::in_width],
+    data_T res[CONFIG_T::n_chan * CONFIG_T::out_width]
+) {
+    for(int j = 0; j < CONFIG_T::n_chan; j++) {
+        for (int i = 0; i < CONFIG_T::pad_left; i++) {
+            *(res++) = 0;
+        }
+
+        for (int i = 0; i < CONFIG_T::in_width; i++) {
+            *(res++) = (res_T) *(data++);
+        }
+
+        for (int i = 0; i < CONFIG_T::pad_right; i++) {
+            *(res++) = 0;
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void zeropad1d_cl(
+    data_T data[CONFIG_T::n_chan * CONFIG_T::in_width],
+    res_T res[CONFIG_T::n_chan * CONFIG_T::out_width]
+) {
+    for (int i = 0; i < CONFIG_T::pad_left; i++) {
+        for(int j = 0; j < CONFIG_T::n_chan; j++) {
+            *(res++) = 0;
+        }
+    }
+
+    for (int i = 0; i < CONFIG_T::in_width; i++) {
+        for(int j = 0; j < CONFIG_T::n_chan; j++) {
+            *(res++) = (res_T) *(data++);
+        }
+    }
+
+    for (int i = 0; i < CONFIG_T::pad_right; i++) {
+        for(int j = 0; j < CONFIG_T::n_chan; j++) {
+            *(res++) = 0;
+        }
+    }
+}
+
+
+struct padding2d_config {
+    static const unsigned n_chan = 10;
+    static const unsigned in_height = 10;
+    static const unsigned in_width = 10;
+    static const unsigned out_height = 10;
+    static const unsigned out_width = 10;
+    static const unsigned pad_top = 0;
+    static const unsigned pad_bottom = 0;
+    static const unsigned pad_left = 0;
+    static const unsigned pad_right = 0;
+};
+
+template<class data_T, class res_T, typename CONFIG_T>
+void zeropad2d_cf(
+    data_T data[CONFIG_T::n_chan * CONFIG_T::in_height * CONFIG_T::in_width],
+    data_T res[CONFIG_T::n_chan * CONFIG_T::out_height * CONFIG_T::out_width]
+) {
+    for(int k = 0; k < CONFIG_T::n_chan; k++) {
+
+        for (int i = 0; i < CONFIG_T::pad_top; i++) {
+            for (int j = 0; j < CONFIG_T::out_width; j++) {
+                *(res++) = 0;
+            }
+        }
+
+        for (int i = 0; i < CONFIG_T::in_height; i++) {
+            for (int j = 0; j < CONFIG_T::pad_left; j++) {
+                *(res++) = 0;
+            }
+            for (int j = 0; j < CONFIG_T::in_width; j++) {
+                *(res++) = (res_T) *(data++);
+            }
+            for (int j = 0; j < CONFIG_T::pad_right; j++) {
+                *(res++) = 0;
+            }
+        }
+
+        for (int i = 0; i < CONFIG_T::pad_bottom; i++) {
+            for (int j = 0; j < CONFIG_T::out_width; j++) {
+                *(res++) = 0;
+            }
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void zeropad2d_cl(
+    data_T data[CONFIG_T::n_chan * CONFIG_T::in_height * CONFIG_T::in_width],
+    res_T res[CONFIG_T::n_chan * CONFIG_T::out_height * CONFIG_T::out_width]
+) {
+    for (int i = 0; i < CONFIG_T::pad_top; i++) {
+        for (int j = 0; j < CONFIG_T::out_width; j++) {
+            for(int k = 0; k < CONFIG_T::n_chan; k++) {
+                *(res++) = 0;
+            }
+        }
+    }
+
+    for (int i = 0; i < CONFIG_T::in_height; i++) {
+        for (int j = 0; j < CONFIG_T::pad_left; j++) {
+            for(int k = 0; k < CONFIG_T::n_chan; k++) {
+                *(res++) = 0;
+            }
+        }
+        for (int j = 0; j < CONFIG_T::in_width; j++) {
+            for(int k = 0; k < CONFIG_T::n_chan; k++) {
+                *(res++) = (res_T) *(data++);
+            }
+        }
+        for (int j = 0; j < CONFIG_T::pad_right; j++) {
+            for(int k = 0; k < CONFIG_T::n_chan; k++) {
+                *(res++) = 0;
+            }
+        }
+    }
+
+    for (int i = 0; i < CONFIG_T::pad_bottom; i++) {
+        for (int j = 0; j < CONFIG_T::out_width; j++) {
+            for(int k = 0; k < CONFIG_T::n_chan; k++) {
+                *(res++) = 0;
+            }
+        }
+    }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_padding_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_padding_stream.h
new file mode 100644
index 0000000..0bbd42f
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_padding_stream.h
@@ -0,0 +1,82 @@
+#ifndef NNET_PADDING_STREAM_H_
+#define NNET_PADDING_STREAM_H_
+
+#include <math.h>
+
+namespace nnet {
+
+template<class res_T, typename CONFIG_T>
+void fill_zero(hls::stream<res_T> &res) {
+    #pragma HLS INLINE
+    res_T res_part;
+	for (int c = 0; c < CONFIG_T::n_chan; c++) {
+        #pragma HLS UNROLL
+	    res_part[c] = 0;
+    }
+    res.write(res_part);
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void fill_data(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    #pragma HLS INLINE
+    data_T data_part = data.read();
+    res_T res_part;
+    for (int c = 0; c < CONFIG_T::n_chan; c++) {
+        #pragma HLS UNROLL
+        res_part[c] = data_part[c];
+    }
+    res.write(res_part);
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void zeropad1d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res
+) {
+    PadLeft: for (int i = 0; i < CONFIG_T::pad_left; i++) {
+        fill_zero<res_T, CONFIG_T>(res);
+    }
+
+    CopyMain: for (int i = 0; i < CONFIG_T::in_width; i++) {
+        fill_data<data_T, res_T, CONFIG_T>(data, res);
+    }
+
+    PadRight: for (int i = 0; i < CONFIG_T::pad_right; i++) {
+        fill_zero<res_T, CONFIG_T>(res);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void zeropad2d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res
+) {
+
+    PadTop: for (int i = 0; i < CONFIG_T::pad_top; i++) {
+        PadTopWidth: for (int j = 0; j < CONFIG_T::out_width; j++) {
+            fill_zero<res_T, CONFIG_T>(res);
+        }
+    }
+
+    PadMain: for (int i = 0; i < CONFIG_T::in_height; i++) {
+        PadLeft: for (int j = 0; j < CONFIG_T::pad_left; j++) {
+            fill_zero<res_T, CONFIG_T>(res);
+        }
+        CopyMain: for (int j = 0; j < CONFIG_T::in_width; j++) {
+            fill_data<data_T, res_T, CONFIG_T>(data, res);
+        }
+        PadRight: for (int j = 0; j < CONFIG_T::pad_right; j++) {
+            fill_zero<res_T, CONFIG_T>(res);
+        }
+    }
+
+    PadBottom: for (int i = 0; i < CONFIG_T::pad_bottom; i++) {
+        PadBottomWidth: for (int j = 0; j < CONFIG_T::out_width; j++) {
+            fill_zero<res_T, CONFIG_T>(res);
+        }
+    }
+}
+
+}
+
+#endif
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_pooling.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_pooling.h
new file mode 100644
index 0000000..545ae55
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_pooling.h
@@ -0,0 +1,298 @@
+#ifndef NNET_POOLING_H_
+#define NNET_POOLING_H_
+
+#include <iostream>
+#include "nnet_helpers.h"
+
+namespace nnet{
+
+// Return the maximum value from an array
+template<typename T, int N>
+T max(T x[N]){
+  T y = x[0];
+  for(int i = 1; i < N; i++){
+    y = x[i] > y ? x[i] : y;
+  }
+  return y;
+}
+
+template<int W, int N>
+ap_int<W> avg(ap_int<W> (&x)[N]){
+  // Use a wider accumulator than the input to avoid overflow
+  ap_int<W + ceillog2(N)> tmp = 0;
+  for(int i = 0; i < N; i++){
+    tmp += x[i];
+  }
+  tmp /= N;
+  // Now cast back to original type
+  ap_int<W> y = tmp;
+  return tmp;
+}
+
+template<int W, int I, int N>
+ap_fixed<W, I> avg(ap_fixed<W, I> (&x)[N]){
+  // Use a wider accumulator than the input to avoid overflow
+  ap_fixed<W + ceillog2(N), I + ceillog2(N)> tmp = 0;
+  for(int i = 0; i < N; i++){
+    tmp += x[i];
+  }
+  tmp /= N;
+  // Now cast back to original type
+  ap_fixed<W, I> y = tmp;
+  return y;
+}
+
+// Return the mean value of an array
+template<typename T, int N>
+T avg(T (&x)[N]){
+  T y = 0;
+  for(int i = 0; i < N; i++){
+    y += x[i];
+  }
+  y /= N;
+  return y;
+}
+
+// Enumeration for pooling operation (max, avg, l2norm pooling)
+enum Pool_Op { Max, Average }; // L2Norm };
+template<typename T, int N, Pool_Op op>
+T pool_op(T (&x)[N]){
+	switch(op){
+	case Max: return max<T, N>(x);
+	case Average: return avg(x);
+	// case L2Norm: return l2norm<T, N>(x);
+	}
+}
+
+template<typename T, Pool_Op op>
+T pad_val(){
+  /*---
+  *- In Tensorflow, pooling ignores the value in the padded cells
+  *- For Avg pooling, return 0 (the divisior is modified to the
+  *- area overlapping the unpadded image.
+  *- For max pooling, return the most negative value for the type.
+  *- TODO this is not really generic, it assumes fixed point or integer T
+  ---*/
+  switch(op){
+    case Max:{ 
+      T x = 0;
+      x[x.width - 1] = 1;
+      return x;
+      break;}
+    case Average: return 0;
+  }
+}
+
+struct pooling1d_config{
+  // IO size
+  static const unsigned n_in = 10;
+  static const unsigned pool_width = 2;
+  static const unsigned stride_width = 2;
+  static const unsigned n_out = (n_in - pool_width) / stride_width + 1;
+  static const unsigned pad_left = 0;
+  static const unsigned pad_right = 0;
+  // Pooling function
+  static const Pool_Op pool_op = Max;
+};
+
+template<typename CONFIG_T>
+constexpr int pool_op_limit_1d() {
+  return CONFIG_T::n_in * CONFIG_T::n_filt / CONFIG_T::reuse;
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pooling1d_cl(data_T data[CONFIG_T::n_in * CONFIG_T::n_filt], res_T res[CONFIG_T::n_out * CONFIG_T::n_filt]) {
+
+  // TODO partition the arrays according to the reuse factor
+  const int limit = pool_op_limit_1d<CONFIG_T>();
+  #pragma HLS ALLOCATION instances=pool_op limit=limit function
+  // Add any necessary padding
+  unsigned padded_width = CONFIG_T::n_in + CONFIG_T::pad_left + CONFIG_T::pad_right;
+  if (CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0) {
+    padded_width -= padded_width - (padded_width / CONFIG_T::stride_width * CONFIG_T::stride_width);
+  }
+
+  for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+    // Loop over input image x in steps of stride
+    for(int ii = 0; ii < padded_width; ii += CONFIG_T::stride_width) {
+      data_T pool[CONFIG_T::pool_width];
+      // Keep track of number of pixels in image vs padding region
+      unsigned img_overlap = 0;
+      // Loop over pool window x
+      for(int jj = 0; jj < CONFIG_T::stride_width; jj++) {
+        if(ii+jj < CONFIG_T::pad_left || ii+jj >= (padded_width - CONFIG_T::pad_right)) {
+          // Add padding
+          pool[jj] = pad_val<data_T, CONFIG_T::pool_op>();
+        }else{
+          pool[jj] = data[(ii + jj) * CONFIG_T::n_filt + ff];
+          img_overlap++;
+        }
+      }
+      // do the pooling
+      // TODO in the case of average pooling, need to reduce width to area of pool window
+      // not overlapping padding region
+      res[(ii/CONFIG_T::stride_width)* CONFIG_T::n_filt + ff] =
+          pool_op<data_T, CONFIG_T::pool_width, CONFIG_T::pool_op>(pool);
+      // If the pool op is Average, the zero-padding needs to be removed from the results
+      if(CONFIG_T::pool_op == Average) {
+        data_T rescale = CONFIG_T::pool_width / img_overlap;
+        res[(ii/CONFIG_T::stride_width)* CONFIG_T::n_filt + ff] *= rescale;
+      }
+	  }
+  }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void global_pooling1d_cl(data_T data[CONFIG_T::n_in * CONFIG_T::n_filt], res_T res[CONFIG_T::n_filt]) {
+  assert(CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+  assert(CONFIG_T::pool_width == CONFIG_T::stride_width);
+
+  // TODO partition the arrays according to the reuse factor
+  const int limit = pool_op_limit_1d<CONFIG_T>();
+  #pragma HLS ALLOCATION instances=pool_op limit=limit function
+
+  for(int ff = 0; ff < CONFIG_T::n_filt; ff++) {
+    data_T pool[CONFIG_T::n_in];
+    for(int jj = 0; jj < CONFIG_T::n_in; jj++) {
+      pool[jj] = data[jj * CONFIG_T::n_filt + ff];
+    }
+  // do the pooling
+  res[ff] = pool_op<data_T, CONFIG_T::n_in, CONFIG_T::pool_op>(pool);
+  }
+}
+
+struct pooling2d_config{
+  // IO size
+  static const unsigned in_height = 10;
+  static const unsigned in_width = 10;
+  static const unsigned n_filt = 4;
+  static const unsigned stride_height = 2;
+  static const unsigned stride_width = 2;
+  static const unsigned pool_height = 2;
+  static const unsigned pool_width = 2;
+  static const unsigned out_height = (in_height - pool_height) / stride_height + 1;
+  static const unsigned out_width = (in_width - pool_width) / stride_width + 1;
+  // Padding
+  static const unsigned pad_top = 0;
+  static const unsigned pad_bottom = 0;
+  static const unsigned pad_left = 0;
+  static const unsigned pad_right = 0;
+  // Pooling function
+  static const Pool_Op pool_op = Max;
+  // Reuse
+  static const unsigned reuse = 1;
+  
+  // Internal data type definitions
+  typedef float accum_t;
+};
+
+template<typename CONFIG_T>
+constexpr int pool_op_limit(){
+  return (CONFIG_T::out_height * CONFIG_T::out_width) * CONFIG_T::n_filt / CONFIG_T::reuse;
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pooling2d_cl(data_T data[CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_filt],
+               res_T res[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt]){
+
+  // TODO partition the arrays according to the reuse factor
+  const int limit = pool_op_limit<CONFIG_T>();
+  #pragma HLS ALLOCATION instances=pool_op limit=limit function
+  // Add any necessary padding
+  unsigned padded_height = CONFIG_T::in_height + CONFIG_T::pad_top + CONFIG_T::pad_bottom;
+  unsigned padded_width = CONFIG_T::in_width + CONFIG_T::pad_left + CONFIG_T::pad_right;
+  if (CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0) {
+    padded_height -= padded_height - (padded_height / CONFIG_T::stride_height * CONFIG_T::stride_height);
+    padded_width -= padded_width - (padded_width / CONFIG_T::stride_width * CONFIG_T::stride_width);
+  }
+
+  for(int ff = 0; ff < CONFIG_T::n_filt; ff++){
+	  // Loop over input image y in steps of stride
+	  for(int ii = 0; ii < padded_height; ii += CONFIG_T::stride_height){
+		  // Loop over input image x in steps of stride
+		  for(int jj = 0; jj < padded_width; jj += CONFIG_T::stride_width){
+			  data_T pool[CONFIG_T::pool_height * CONFIG_T::pool_width];
+        // Keep track of number of pixels in image vs padding region
+        unsigned img_overlap = 0;
+			  // Loop over pool window y
+			  for(int kk = 0; kk < CONFIG_T::stride_height; kk++){
+				  // Loop over pool window x
+				  for(int ll = 0; ll < CONFIG_T::stride_width; ll++){
+            if(ii+kk < CONFIG_T::pad_top || ii+kk >= (padded_height - CONFIG_T::pad_bottom) || jj+ll < CONFIG_T::pad_left || jj+ll >= (padded_width - CONFIG_T::pad_right)){
+              // Add padding
+              pool[kk * CONFIG_T::stride_width + ll] = pad_val<data_T, CONFIG_T::pool_op>();
+            }else{
+  					  pool[kk * CONFIG_T::stride_width + ll] = data[(ii + kk) * CONFIG_T::in_width * CONFIG_T::n_filt + (jj + ll) * CONFIG_T::n_filt + ff];
+              img_overlap++;
+            }
+				  }
+			  }
+			  // do the pooling
+        // TODO in the case of average pooling, need to reduce height * width to area of pool window
+        // not overlapping padding region
+			  res[(ii/CONFIG_T::stride_height) * CONFIG_T::out_width * CONFIG_T::n_filt + (jj/CONFIG_T::stride_width)* CONFIG_T::n_filt + ff] =
+					  pool_op<data_T, CONFIG_T::pool_height*CONFIG_T::pool_width, CONFIG_T::pool_op>(pool);
+        // If the pool op is Average, the zero-padding needs to be removed from the results
+        if(CONFIG_T::pool_op == Average){
+          data_T rescale = CONFIG_T::pool_height * CONFIG_T::pool_width / img_overlap;
+          res[(ii/CONFIG_T::stride_height) * CONFIG_T::out_width * CONFIG_T::n_filt + (jj/CONFIG_T::stride_width)* CONFIG_T::n_filt + ff] *= rescale;
+        }
+		  }
+	  }
+  }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pooling2d_cf(data_T data[CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_filt],
+               res_T res[CONFIG_T::out_height * CONFIG_T::out_width * CONFIG_T::n_filt]){
+
+  // TODO partition the arrays according to the reuse factor
+  const int limit = pool_op_limit<CONFIG_T>();
+  #pragma HLS ALLOCATION instances=pool_op limit=limit function
+  // Add any necessary padding
+  unsigned padded_height = CONFIG_T::in_height + CONFIG_T::pad_top + CONFIG_T::pad_bottom;
+  unsigned padded_width = CONFIG_T::in_width + CONFIG_T::pad_left + CONFIG_T::pad_right;
+  if (CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0) {
+    padded_height -= padded_height - (padded_height / CONFIG_T::stride_height * CONFIG_T::stride_height);
+    padded_width -= padded_width - (padded_width / CONFIG_T::stride_width * CONFIG_T::stride_width);
+  }
+
+  for(int ff = 0; ff < CONFIG_T::n_filt; ff++){
+	  // Loop over input image y in steps of stride
+	  for(int ii = 0; ii < padded_height; ii += CONFIG_T::stride_height){
+		  // Loop over input image x in steps of stride
+		  for(int jj = 0; jj < padded_width; jj += CONFIG_T::stride_width){
+			  data_T pool[CONFIG_T::pool_height * CONFIG_T::pool_width];
+        // Keep track of number of pixels in image vs padding region
+        unsigned img_overlap = 0;
+			  // Loop over pool window y
+			  for(int kk = 0; kk < CONFIG_T::stride_height; kk++){
+				  // Loop over pool window x
+				  for(int ll = 0; ll < CONFIG_T::stride_width; ll++){
+            if(ii+kk < CONFIG_T::pad_top || ii+kk >= (padded_height - CONFIG_T::pad_bottom) || jj+ll < CONFIG_T::pad_left || jj+ll >= (padded_width - CONFIG_T::pad_right)){
+              // Add padding
+              pool[kk * CONFIG_T::stride_width + ll] = pad_val<data_T, CONFIG_T::pool_op>();
+            }else{
+  					  pool[kk * CONFIG_T::stride_width + ll] = data[(ii + kk) * CONFIG_T::in_width + ff * CONFIG_T::in_width*CONFIG_T::in_height + ll + jj];
+              img_overlap++;
+            }
+				  }
+			  }
+			  // do the pooling
+        // TODO in the case of average pooling, need to reduce height * width to area of pool window
+        // not overlapping padding region
+			  res[(ii/CONFIG_T::stride_height) * CONFIG_T::out_width + (jj/CONFIG_T::stride_width) + ff* CONFIG_T::out_height* CONFIG_T::out_width] =
+					  pool_op<data_T, CONFIG_T::pool_height*CONFIG_T::pool_width, CONFIG_T::pool_op>(pool);
+        // If the pool op is Average, the zero-padding needs to be removed from the results
+        if(CONFIG_T::pool_op == Average){
+          data_T rescale = CONFIG_T::pool_height * CONFIG_T::pool_width / img_overlap;
+          res[(ii/CONFIG_T::stride_height) * CONFIG_T::out_width + (jj/CONFIG_T::stride_width) + ff* CONFIG_T::out_height* CONFIG_T::out_width] *= rescale;
+        }
+		  }
+	  }
+  }
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_pooling_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_pooling_stream.h
new file mode 100644
index 0000000..80fa1d2
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_pooling_stream.h
@@ -0,0 +1,605 @@
+#ifndef NNET_POOLING_STREAM_H_
+#define NNET_POOLING_STREAM_H_
+
+#include "utils/x_hls_utils.h"
+#include "ap_shift_reg.h"
+#include "nnet_common.h"
+#include "nnet_pooling.h"
+#include "nnet_conv_stream.h"
+#include "hls_stream.h"
+
+namespace nnet {
+
+// *************************************************
+//       Max/average pooling
+// *************************************************
+
+template <class T, int N, class CONFIG_T>
+T reduce_pool(T x[N]) {
+    #pragma HLS INLINE
+    if (CONFIG_T::pool_op == Max) {
+        Op_max<T> op_max;
+        return reduce<T, N, Op_max<T>>(x, op_max);
+    } else {
+        Op_add<T> op_add;
+        T sum = reduce<T, N, Op_add<T>>(x, op_add);
+        return sum / N;
+    }
+}
+
+template<unsigned TABLE_SIZE, unsigned POOL_SIZE>
+void init_pool_table(
+    unsigned table[TABLE_SIZE]
+) {
+    for (unsigned ii = 0; ii < TABLE_SIZE; ii++) {
+        table[ii] = ii % POOL_SIZE;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_pool_encoded_2d(
+    const unsigned h_idx,
+    const unsigned w_idx,
+    const data_T& in_elem,
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::pool_height * CONFIG_T::pool_width * CONFIG_T::n_filt],
+    hls::stream<res_T> &res,
+    res_T &res_pack,
+    unsigned &outputs_ready
+) {
+    // Nearest H without unused pixels on the right
+    constexpr unsigned nH = ((CONFIG_T::in_height - CONFIG_T::pool_height) / CONFIG_T::stride_height) * CONFIG_T::stride_height + CONFIG_T::pool_height;
+    // Scaled H that behaves like original H
+    constexpr unsigned sH = (DIV_ROUNDUP(CONFIG_T::pool_height, CONFIG_T::stride_height) - 1) * CONFIG_T::stride_height + CONFIG_T::pool_height;
+    // Nearest W without unused pixels on the right
+    constexpr unsigned nW = ((CONFIG_T::in_width - CONFIG_T::pool_width) / CONFIG_T::stride_width) * CONFIG_T::stride_width + CONFIG_T::pool_width;
+    // Scaled W that behaves like original W
+    constexpr unsigned sW = (DIV_ROUNDUP(CONFIG_T::pool_width, CONFIG_T::stride_width) - 1) * CONFIG_T::stride_width + CONFIG_T::pool_width;
+
+#ifdef __SYNTHESIS__
+    bool initialized = false;
+    unsigned pool_table_height[CONFIG_T::in_height];
+    unsigned pool_table_width[CONFIG_T::in_width];
+#else
+    static bool initialized = false;
+    static unsigned pool_table_height[CONFIG_T::in_height];
+    static unsigned pool_table_width[CONFIG_T::in_width];
+#endif
+    if (!initialized) {
+        init_pool_table<CONFIG_T::in_height, CONFIG_T::pool_height>(pool_table_height);
+        init_pool_table<CONFIG_T::in_width, CONFIG_T::pool_width>(pool_table_width);
+        initialized = true;
+    }
+
+    #pragma HLS INLINE
+
+    if (data_T::size / CONFIG_T::n_filt > 1) {
+        #pragma HLS ARRAY_PARTITION variable=pool_table_height complete
+        #pragma HLS ARRAY_PARTITION variable=pool_table_width complete
+    }
+
+    typename CONFIG_T::accum_t pool_window[CONFIG_T::pool_height * CONFIG_T::pool_width];
+    #pragma HLS ARRAY_PARTITION variable=pool_window complete
+
+    const unsigned sh_idx = pool_table_height[h_idx] * CONFIG_T::pool_width;
+    const unsigned wp_idx = w_idx * (data_T::size / CONFIG_T::n_filt);
+
+    PixelLoop: for (unsigned p = 0; p < data_T::size / CONFIG_T::n_filt; p++) {
+        #pragma HLS PIPELINE
+
+        ap_uint<CONFIG_T::pool_height * CONFIG_T::pool_width> filt_mask = 0;
+        if ((h_idx < nH) && (wp_idx + p < nW)) {
+            filt_mask = sh_idx + pool_table_width[wp_idx + p] + 1;
+        }
+
+        CopyDataFilt: for (unsigned c = 0; c < CONFIG_T::n_filt; c++) {
+            if (filt_mask > 0) data_window[c * CONFIG_T::pool_height * CONFIG_T::pool_width + filt_mask.to_uint() - 1].write(in_elem[p * CONFIG_T::n_filt + c]);
+        }
+
+        if (filt_mask == CONFIG_T::pool_height * CONFIG_T::pool_width) {
+            FiltLoop: for(unsigned c = 0; c < CONFIG_T::n_filt; c++) {
+                PoolLoop: for(unsigned f = 0; f < CONFIG_T::pool_height * CONFIG_T::pool_width; f++) {
+                    pool_window[f] = data_window[c * CONFIG_T::pool_height * CONFIG_T::pool_width + f].read();
+                }
+                if (res_T::size / CONFIG_T::n_filt == 1) { // Saves resources if we don't pack output, compiler will remove the else branch
+                    res_pack[c] = reduce_pool<typename CONFIG_T::accum_t, CONFIG_T::pool_height * CONFIG_T::pool_width, CONFIG_T>(pool_window);
+                } else {
+                    res_pack[outputs_ready * CONFIG_T::n_filt + c] = reduce_pool<typename CONFIG_T::accum_t, CONFIG_T::pool_height * CONFIG_T::pool_width, CONFIG_T>(pool_window);
+                }
+
+            }
+            if (res_T::size / CONFIG_T::n_filt == 1) { // Saves resources if we don't pack output, compiler will remove the else branch
+                res.write(res_pack);
+            } else {
+                if (outputs_ready == (res_T::size / CONFIG_T::n_filt) - 1) {
+                    res.write(res_pack);
+                    outputs_ready = 0;
+                } else {
+                    outputs_ready++;
+                }
+            }
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pooling2d_encoded_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T> &res
+) {
+    assert(CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    assert(CONFIG_T::pool_height == CONFIG_T::stride_height && CONFIG_T::pool_width == CONFIG_T::stride_width);
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+    unsigned outputs_ready = 0;
+
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::pool_height * CONFIG_T::pool_width * CONFIG_T::n_filt];
+    constexpr int win_depth = CONFIG_T::pool_height * CONFIG_T::out_width;
+    for (unsigned i_out = 0; i_out < CONFIG_T::pool_height * CONFIG_T::pool_width * CONFIG_T::n_filt; i_out++) {
+        #pragma HLS STREAM variable=data_window[i_out] depth=win_depth
+    }
+
+    constexpr int pack_factor = data_T::size / CONFIG_T::n_filt;
+
+    ReadInputHeight: for (unsigned i_ih = 0; i_ih < CONFIG_T::in_height; i_ih++) {
+        ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width / (pack_factor); i_iw++) {
+            #pragma HLS LOOP_FLATTEN
+            if (res_T::size / CONFIG_T::n_filt == 1) {
+                #pragma HLS PIPELINE II=pack_factor
+            }
+            compute_pool_encoded_2d<data_T, res_T, CONFIG_T>(i_ih, i_iw, data.read(), data_window, res, res_pack, outputs_ready);
+        }
+    }
+}
+
+// *************************************************
+//       Line Buffer Implementation (Phil's)
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_pool_buffer_2d(
+    const data_T& in_elem,
+    ap_shift_reg<typename data_T::value_type, CONFIG_T::in_width> line_buffer[MAX(CONFIG_T::pool_height - 1,1)][CONFIG_T::n_filt],
+    hls::stream<res_T> &res
+) {
+    #pragma HLS INLINE
+    const static int lShiftX = CONFIG_T::pool_width - 1;
+    const static int lShiftY = CONFIG_T::pool_height - 1;
+    static int pX = 0; // pixel X 
+    static int pY = 0; // pixel Y
+    static int sX = 0; // stride X
+    static int sY = 0; // stride Y
+
+    typename data_T::value_type pool_window[CONFIG_T::pool_height * CONFIG_T::pool_width];
+    #pragma HLS ARRAY_PARTITION variable=pool_window complete
+
+    static typename data_T::value_type kernel_data[CONFIG_T::pool_height * CONFIG_T::pool_width * CONFIG_T::n_filt];
+    #pragma HLS ARRAY_PARTITION variable = kernel_data complete dim = 0
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+
+    // Add pixel into line buffer, return pooling kernels
+    nnet::shift_line_buffer<data_T, CONFIG_T>(in_elem, line_buffer, kernel_data);
+
+    // Can compute pooling output
+    if ((sX - lShiftX) == 0 && (sY - lShiftY) == 0 && pY > lShiftY - 1 && pX > lShiftX - 1) {
+        FiltLoop: for(unsigned i_ic = 0; i_ic < CONFIG_T::n_filt; i_ic++) {
+            #pragma HLS PIPELINE
+
+            // Retrieve data for current channel
+            PoolLoop: for(unsigned i_ihw = 0; i_ihw < CONFIG_T::pool_height * CONFIG_T::pool_width; i_ihw++) {
+                pool_window[i_ihw] = kernel_data[i_ihw * CONFIG_T::n_filt + i_ic]; 
+            }
+
+            // Compute Pooling
+            res_pack[i_ic] = reduce_pool<typename data_T::value_type, CONFIG_T::pool_height * CONFIG_T::pool_width, CONFIG_T>(pool_window);
+        }
+
+        // Write to output
+        res.write(res_pack);
+    }
+
+    // Counter Housekeeping
+    if (pX + 1 == CONFIG_T::in_width)  // Includes padding, end of line (padded)
+    {
+        pX = 0;
+        sX = 0;
+        if (pY + 1 == CONFIG_T::in_height) {  // Reached bottom of image
+            pY = 0;
+            sY = 0;
+        } else { // Next line
+            pY = pY + 1;
+            // Update stride (threshold) ? subtract stride : increment stride
+            sY = ((sY - lShiftY) == 0) ? sY - CONFIG_T::stride_height + 1 : sY + 1; 
+        }
+    } else {
+        pX = pX + 1;
+        // Update stride (threshold) ? subtract stride : increment stride
+        sX = ((sX - lShiftX) == 0) ? sX - CONFIG_T::stride_width + 1 : sX + 1; 
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pooling2d_buffer_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T> &res
+) {
+    assert(CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    assert(CONFIG_T::pool_height == CONFIG_T::stride_height && CONFIG_T::pool_width == CONFIG_T::stride_width);
+
+    static ap_shift_reg<typename data_T::value_type, CONFIG_T::in_width> line_buffer[MAX(CONFIG_T::pool_height - 1,1)][CONFIG_T::n_filt];
+    #pragma HLS ARRAY_PARTITION variable = line_buffer complete dim = 2
+
+    ReadInputHeight: for (unsigned i_ih = 0; i_ih < CONFIG_T::in_height; i_ih++) {
+        ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width; i_iw++) {
+            #pragma HLS LOOP_FLATTEN
+            #pragma HLS PIPELINE
+
+            compute_pool_buffer_2d<data_T, res_T, CONFIG_T>(data.read(), line_buffer, res);
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pooling2d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T> &res
+) {
+    #pragma HLS inline region
+    switch(CONFIG_T::implementation){
+        case conv_implementation::linebuffer:
+            pooling2d_buffer_cl<data_T, res_T, CONFIG_T>(data, res);
+            break;
+        case conv_implementation::encoded:
+            pooling2d_encoded_cl<data_T, res_T, CONFIG_T>(data, res);
+            break;
+        } 
+}
+
+// *************************************************
+//                  Pooling 1D
+// *************************************************
+
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_pool_encoded_1d(
+    const unsigned w_idx,
+    const data_T& in_elem,
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::pool_width * CONFIG_T::n_filt],
+    hls::stream<res_T> &res,
+    res_T &res_pack,
+    unsigned &outputs_ready
+) {
+    // Nearest W without unused pixels on the right
+    constexpr unsigned nW = ((CONFIG_T::n_in - CONFIG_T::pool_width) / CONFIG_T::stride_width) * CONFIG_T::stride_width + CONFIG_T::pool_width;
+    // Scaled W that behaves like original W
+    constexpr unsigned sW = (DIV_ROUNDUP(CONFIG_T::pool_width, CONFIG_T::stride_width) - 1) * CONFIG_T::stride_width + CONFIG_T::pool_width;
+
+#ifdef __SYNTHESIS__
+    bool initialized = false;
+    unsigned pool_table_width[CONFIG_T::n_in];
+#else
+    static bool initialized = false;
+    static unsigned pool_table_width[CONFIG_T::n_in];
+#endif
+    if (!initialized) {
+        init_pool_table<CONFIG_T::n_in, CONFIG_T::pool_width>(pool_table_width);
+        initialized = true;
+    }
+
+    #pragma HLS INLINE
+
+    if (data_T::size / CONFIG_T::n_filt > 1) {
+        #pragma HLS ARRAY_PARTITION variable=pool_table_width complete
+    }
+
+    typename CONFIG_T::accum_t pool_window[CONFIG_T::pool_width];
+    #pragma HLS ARRAY_PARTITION variable=pool_window complete
+
+    const unsigned wp_idx = w_idx * (data_T::size / CONFIG_T::n_filt);
+
+    PixelLoop: for (unsigned p = 0; p < data_T::size / CONFIG_T::n_filt; p++) {
+        #pragma HLS PIPELINE
+
+        ap_uint<CONFIG_T::pool_width> filt_mask = 0;
+        if (wp_idx + p < nW) {
+            filt_mask = pool_table_width[wp_idx + p] + 1;
+        }
+
+        CopyDataFilt: for (unsigned c = 0; c < CONFIG_T::n_filt; c++) {
+            if (filt_mask > 0) data_window[c * CONFIG_T::pool_width + filt_mask.to_uint() - 1].write(in_elem[p * CONFIG_T::n_filt + c]);
+        }
+
+        if (filt_mask == CONFIG_T::pool_width) {
+            FiltLoop: for(unsigned c = 0; c < CONFIG_T::n_filt; c++) {
+                PoolLoop: for(unsigned f = 0; f < CONFIG_T::pool_width; f++) {
+                    pool_window[f] = data_window[c * CONFIG_T::pool_width + f].read();
+                }
+                if (res_T::size / CONFIG_T::n_filt == 1) { // Saves resources if we don't pack output, compiler will remove the else branch
+                    res_pack[c] = reduce_pool<typename CONFIG_T::accum_t, CONFIG_T::pool_width, CONFIG_T>(pool_window);
+                } else {
+                    res_pack[outputs_ready * CONFIG_T::n_filt + c] = reduce_pool<typename CONFIG_T::accum_t, CONFIG_T::pool_width, CONFIG_T>(pool_window);
+                }
+
+            }
+            if (res_T::size / CONFIG_T::n_filt == 1) { // Saves resources if we don't pack output, compiler will remove the else branch
+                res.write(res_pack);
+            } else {
+                if (outputs_ready == (res_T::size / CONFIG_T::n_filt) - 1) {
+                    res.write(res_pack);
+                    outputs_ready = 0;
+                } else {
+                    outputs_ready++;
+                }
+            }
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pooling1d_encoded_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T> &res
+) {
+    assert(CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    assert(CONFIG_T::pool_width == CONFIG_T::stride_width);
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+    unsigned outputs_ready = 0;
+
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::pool_width * CONFIG_T::n_filt];
+    constexpr int win_depth = CONFIG_T::n_out;
+    for (unsigned i_out = 0; i_out < CONFIG_T::pool_width * CONFIG_T::n_filt; i_out++) {
+        #pragma HLS STREAM variable=data_window[i_out] depth=win_depth
+    }
+
+    constexpr int pack_factor = data_T::size / CONFIG_T::n_filt;
+
+    ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::n_in / (pack_factor); i_iw++) {
+        #pragma HLS LOOP_FLATTEN
+        if (res_T::size / CONFIG_T::n_filt == 1) {
+            #pragma HLS PIPELINE II=pack_factor
+        }
+        compute_pool_encoded_1d<data_T, res_T, CONFIG_T>(i_iw, data.read(), data_window, res, res_pack, outputs_ready);
+    }
+}
+
+// *************************************************
+//       Line Buffer Implementation (Phil's) 1D
+// *************************************************
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_pool_buffer_1d(
+    const data_T& in_elem,
+    hls::stream<res_T> &res
+) {
+    #pragma HLS INLINE
+    const static int lShiftX = CONFIG_T::pool_width - 1;
+    // Counters
+    static int pX = 0;
+    static int sX = 0;
+
+    typename data_T::value_type pool_window[CONFIG_T::pool_width];
+    #pragma HLS ARRAY_PARTITION variable=pool_window complete
+
+    static typename data_T::value_type kernel_data[CONFIG_T::pool_width * CONFIG_T::n_filt];
+    #pragma HLS ARRAY_PARTITION variable = kernel_data complete dim = 0
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+
+    // Add pixel into line buffer, return pooling kernels
+    // 1D case line buffer not necessary. Put directly into the kernel_data buffer
+    nnet::kernel_shift_1d<data_T, CONFIG_T>(in_elem, kernel_data);
+
+    // Can compute pooling output
+    if ( (sX - lShiftX) == 0 && pX > lShiftX - 1) {
+        FiltLoop: for(unsigned i_ic = 0; i_ic < CONFIG_T::n_filt; i_ic++) {
+            #pragma HLS PIPELINE
+
+            // Retrieve data for current channel
+            PoolLoop: for(unsigned i_iw = 0; i_iw < CONFIG_T::pool_width; i_iw++) {
+                pool_window[i_iw] = kernel_data[i_iw * CONFIG_T::n_filt + i_ic]; 
+            }
+
+            // Compute Pooling
+            res_pack[i_ic] = reduce_pool<typename data_T::value_type, CONFIG_T::pool_width, CONFIG_T>(pool_window);
+        }
+
+        // Write to output
+        res.write(res_pack);
+    }
+
+    // Counter Housekeeping
+    if (pX + 1 == CONFIG_T::n_in)  // Includes padding, end of line (padded)
+    {
+        pX = 0;
+        sX = 0;
+    } else {
+        pX = pX + 1;
+        // Update stride (threshold) ? subtract stride : increment stride
+        sX = ((sX - lShiftX) == 0) ? sX - CONFIG_T::stride_width + 1 : sX + 1; 
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pooling1d_buffer_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T> &res
+) {
+    assert(CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    
+    ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::n_in; i_iw++) {
+        #pragma HLS LOOP_FLATTEN
+        #pragma HLS PIPELINE
+        compute_pool_buffer_1d<data_T, res_T, CONFIG_T>(data.read(), res);
+    }
+}
+
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pooling1d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T> &res
+) {
+    #pragma HLS inline region
+    switch(CONFIG_T::implementation){
+        case conv_implementation::linebuffer:
+            pooling1d_buffer_cl<data_T, res_T, CONFIG_T>(data, res);
+            break;
+        case conv_implementation::encoded:
+            pooling1d_encoded_cl<data_T, res_T, CONFIG_T>(data, res);
+            break;
+    } 
+}
+
+
+// *************************************************
+//       Global max/average pooling
+// *************************************************
+
+template <class T, int N, class CONFIG_T>
+T reduce_global_pool(T x, T y[N]) {
+    #pragma HLS INLINE
+    if (CONFIG_T::pool_op == Max) {
+        Op_max<T> op_max;
+        T y_max = reduce<T, N, Op_max<T>>(y, op_max);
+        return (x > y_max) ? x : y_max;
+    } else {
+        Op_add<T> op_add;
+        T y_sum = reduce<T, N, Op_add<T>>(y, op_add);
+        return x + y_sum;
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_global_pool(
+    const data_T& in_elem,
+    typename CONFIG_T::accum_t data_window[CONFIG_T::n_filt]
+) {
+    PoolFilt: for (unsigned c = 0; c < CONFIG_T::n_filt; c++) {
+        #pragma HLS UNROLL
+
+        typename CONFIG_T::accum_t data_pack[data_T::size / CONFIG_T::n_filt];
+        #pragma HLS ARRAY_PARTITION variable=data_pack complete dim=0
+
+        PixelLoop: for (unsigned p = 0; p < data_T::size / CONFIG_T::n_filt; p++) {
+            #pragma HLS UNROLL
+            data_pack[p] = in_elem[p * CONFIG_T::n_filt + c];
+        }
+        data_window[c] = reduce_global_pool<typename CONFIG_T::accum_t, data_T::size / CONFIG_T::n_filt, CONFIG_T>(data_window[c], data_pack);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void global_pooling2d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T> &res
+) {
+    assert(CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    assert(CONFIG_T::pool_height == CONFIG_T::stride_height && CONFIG_T::pool_width == CONFIG_T::stride_width);
+
+    typename CONFIG_T::accum_t data_window[CONFIG_T::n_filt];
+    #pragma HLS ARRAY_PARTITION variable=data_window complete
+
+    typename CONFIG_T::accum_t init = 0;
+    if (CONFIG_T::pool_op == Max) {
+        init = hls::numeric_limits<typename CONFIG_T::accum_t>::min();
+    }
+
+    PoolInitLoop: for (unsigned i_init = 0; i_init < CONFIG_T::n_filt; i_init++) {
+        #pragma HLS UNROLL
+        data_window[i_init] = init;
+    }
+
+    ReadInputHeight: for (unsigned i_ih = 0; i_ih < CONFIG_T::in_height; i_ih++) {
+        ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width / (data_T::size / CONFIG_T::n_filt); i_iw++) {
+            #pragma HLS LOOP_FLATTEN
+            compute_global_pool<data_T, res_T, CONFIG_T>(data.read(), data_window);
+        }
+    }
+
+    if (CONFIG_T::pool_op == Max) {
+        MaxPoolRes: for (unsigned i_res = 0; i_res < CONFIG_T::n_filt / res_T::size; i_res++) {
+            #pragma HLS PIPELINE
+
+            res_T res_pack;
+            #pragma HLS DATA_PACK variable=res_pack
+            MaxPoolPack: for (unsigned i_pack = 0; i_pack < res_T::size; i_pack++) {
+                #pragma HLS UNROLL
+                res_pack[i_pack] = data_window[i_pack];
+            }
+            res.write(res_pack);
+        }
+    } else {
+        AvgPoolRes: for (unsigned i_res = 0; i_res < CONFIG_T::n_filt / res_T::size; i_res++) {
+            #pragma HLS PIPELINE
+
+            res_T res_pack;
+            #pragma HLS DATA_PACK variable=res_pack
+            AvgPoolPack: for (unsigned i_pack = 0; i_pack < res_T::size; i_pack++) {
+                #pragma HLS UNROLL
+                res_pack[i_pack] = data_window[i_pack] / (CONFIG_T::in_height * CONFIG_T::in_width);
+            }
+            res.write(res_pack);
+        }
+    }
+
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void global_pooling1d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T> &res
+) {
+    assert(CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    assert(CONFIG_T::pool_width == CONFIG_T::stride_width);
+
+    typename CONFIG_T::accum_t data_window[CONFIG_T::n_filt];
+    #pragma HLS ARRAY_PARTITION variable=data_window complete
+
+    typename CONFIG_T::accum_t init = 0;
+    if (CONFIG_T::pool_op == Max) {
+        init = hls::numeric_limits<typename CONFIG_T::accum_t>::min();
+    }
+
+    PoolInitLoop: for (unsigned i_init = 0; i_init < CONFIG_T::n_filt; i_init++) {
+        #pragma HLS UNROLL
+        data_window[i_init] = init;
+    }
+
+    ReadInput: for (unsigned i_iw = 0; i_iw < CONFIG_T::n_in / (data_T::size / CONFIG_T::n_filt); i_iw++) {
+        #pragma HLS LOOP_FLATTEN
+        compute_global_pool<data_T, res_T, CONFIG_T>(data.read(), data_window);
+    }
+
+    if (CONFIG_T::pool_op == Max) {
+        MaxPoolRes: for (unsigned i_res = 0; i_res < CONFIG_T::n_filt / res_T::size; i_res++) {
+            #pragma HLS PIPELINE
+
+            res_T res_pack;
+            #pragma HLS DATA_PACK variable=res_pack
+            MaxPoolPack: for (unsigned i_pack = 0; i_pack < res_T::size; i_pack++) {
+                #pragma HLS UNROLL
+                res_pack[i_pack] = data_window[i_pack];
+            }
+            res.write(res_pack);
+        }
+    } else {
+        AvgPoolRes: for (unsigned i_res = 0; i_res < CONFIG_T::n_filt / res_T::size; i_res++) {
+            #pragma HLS PIPELINE
+
+            res_T res_pack;
+            #pragma HLS DATA_PACK variable=res_pack
+            AvgPoolPack: for (unsigned i_pack = 0; i_pack < res_T::size; i_pack++) {
+                #pragma HLS UNROLL
+	      res_pack[i_pack] = data_window[i_pack] / CONFIG_T::n_in;
+            }
+            res.write(res_pack);
+        }
+    }
+
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_sepconv1d_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_sepconv1d_stream.h
new file mode 100644
index 0000000..b0f6ce9
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_sepconv1d_stream.h
@@ -0,0 +1,116 @@
+#ifndef NNET_SEPARABLE_CONV1D_STREAM_H_
+#define NNET_SEPARABLE_CONV1D_STREAM_H_
+
+#include "nnet_common.h"
+#include "hls_stream.h"
+#include "nnet_sepconv_stream.h"
+#include "nnet_conv1d_stream.h"
+
+namespace nnet {
+
+template<class data_T, class res_T, typename CONFIG_T>
+void depthwise_conv_1d_encoded_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_chan])
+{
+    assert(CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::filt_width * CONFIG_T::n_chan];
+    const int win_depth = CONFIG_T::out_width;
+    for (unsigned i_out = 0; i_out < CONFIG_T::filt_width * CONFIG_T::n_chan; i_out++) {
+        #pragma HLS STREAM variable=data_window[i_out] depth=win_depth
+    }
+
+    #pragma HLS ARRAY_PARTITION variable=CONFIG_T::pixels complete
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+    unsigned outputs_ready = 0;
+
+    ap_uint<CONFIG_T::filt_width> pixel_idx[data_T::size / CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=pixel_idx complete
+
+    ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width / (data_T::size / CONFIG_T::n_chan); i_iw++) {
+        #pragma HLS LOOP_FLATTEN
+        if (CONFIG_T::strategy == nnet::latency && data_T::size / CONFIG_T::n_chan == 1) {
+            #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+        }
+        compute_scaled_indices_1d<data_T, CONFIG_T>(i_iw, pixel_idx);
+        compute_depthwise_output_encoded<data_T, res_T, CONFIG_T>(data.read(), data_window, res, res_pack, outputs_ready, weights, biases, pixel_idx);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void depthwise_conv_1d_buffer_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_width * CONFIG_T::n_chan],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_chan])
+{
+    assert(CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+
+    ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width; i_iw++) {
+        #pragma HLS LOOP_FLATTEN
+        if (CONFIG_T::strategy == nnet::latency) {
+            #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+        }
+        compute_depthwise_output_buffer_1d<data_T, res_T, CONFIG_T>(data.read(), res, weights, biases);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pointwise_conv_1d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    assert(CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    assert(CONFIG_T::filt_width == 1);
+
+    #pragma HLS ARRAY_PARTITION variable=weights complete
+    #pragma HLS ARRAY_PARTITION variable=biases complete
+
+    ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width / (data_T::size / CONFIG_T::n_chan); i_iw++) {
+        if (CONFIG_T::strategy == nnet::latency && data_T::size / CONFIG_T::n_chan == 1) {
+            #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+        }
+        if (i_iw % CONFIG_T::stride_width == 0) {
+            pointwise_mult_buffer<data_T, res_T, CONFIG_T>(data.read(), res, weights, biases);
+        } else {
+            data.read();
+        }
+    }
+}
+
+
+template<class data_T, class res_T, typename CONFIG_T>
+void separable_conv_1d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::depthwise_config::weight_t depthwise_weights[CONFIG_T::depthwise_config::filt_width * CONFIG_T::depthwise_config::n_chan],
+    typename CONFIG_T::pointwise_config::weight_t pointwise_weights[CONFIG_T::pointwise_config::n_chan * CONFIG_T::pointwise_config::n_filt],
+    typename CONFIG_T::depthwise_config::bias_t   depthwise_biases[CONFIG_T::depthwise_config::n_chan],
+    typename CONFIG_T::pointwise_config::bias_t   pointwise_biases[CONFIG_T::pointwise_config::n_filt]
+) {
+    #pragma HLS DATAFLOW
+
+    hls::stream<data_T> depthwise_res;
+    unsigned res_depth = CONFIG_T::depthwise_config::out_width;
+    #pragma HLS STREAM variable=depthwise_res depth=res_depth
+
+    switch(CONFIG_T::depthwise_config::implementation){
+        case conv_implementation::linebuffer:
+            depthwise_conv_1d_buffer_cl<data_T, data_T, typename CONFIG_T::depthwise_config>(data, depthwise_res, depthwise_weights, depthwise_biases);
+            break;
+        case conv_implementation::encoded:
+            depthwise_conv_1d_encoded_cl<data_T, data_T, typename CONFIG_T::depthwise_config>(data, depthwise_res, depthwise_weights, depthwise_biases);
+            break;
+    } 
+    pointwise_conv_1d_cl<data_T, res_T, typename CONFIG_T::pointwise_config>(depthwise_res, res, pointwise_weights, pointwise_biases);
+}
+
+}
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_sepconv2d_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_sepconv2d_stream.h
new file mode 100644
index 0000000..69e2726
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_sepconv2d_stream.h
@@ -0,0 +1,132 @@
+#ifndef NNET_SEPARABLE_CONV2D_STREAM_H_
+#define NNET_SEPARABLE_CONV2D_STREAM_H_
+
+#include "nnet_common.h"
+#include "hls_stream.h"
+#include "nnet_sepconv_stream.h"
+#include "nnet_conv2d_stream.h"
+
+namespace nnet {
+
+template<class data_T, class res_T, typename CONFIG_T>
+void depthwise_conv_2d_encoded_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_chan])
+{
+    assert(CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    assert(CONFIG_T::filt_height == CONFIG_T::filt_width);
+
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan];
+    const int win_depth = CONFIG_T::filt_height * CONFIG_T::out_width;
+    for (unsigned i_out = 0; i_out < CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan; i_out++) {
+        #pragma HLS STREAM variable=data_window[i_out] depth=win_depth
+    }
+
+    #pragma HLS ARRAY_PARTITION variable=CONFIG_T::pixels complete
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+    unsigned outputs_ready = 0;
+
+    ap_uint<CONFIG_T::filt_height * CONFIG_T::filt_width> pixel_idx[data_T::size / CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=pixel_idx complete
+
+    ReadInputHeight: for (unsigned i_ih = 0; i_ih < CONFIG_T::in_height; i_ih++) {
+        ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width / (data_T::size / CONFIG_T::n_chan); i_iw++) {
+            #pragma HLS LOOP_FLATTEN
+            if (CONFIG_T::strategy == nnet::latency && data_T::size / CONFIG_T::n_chan == 1) {
+                #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+            }
+            compute_scaled_indices_2d<data_T, CONFIG_T>(i_ih, i_iw, pixel_idx);
+            compute_depthwise_output_encoded<data_T, res_T, CONFIG_T>(data.read(), data_window, res, res_pack, outputs_ready, weights, biases, pixel_idx);
+        }
+    }
+}
+
+// Line Buffer Implementation (Phil's)
+template<class data_T, class res_T, typename CONFIG_T>
+void depthwise_conv_2d_buffer_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_chan])
+{
+    assert(CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+
+    static ap_shift_reg<typename data_T::value_type, CONFIG_T::in_width> line_buffer[CONFIG_T::filt_height - 1][CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable = line_buffer complete dim = 2
+
+    ReadInputHeight: for (unsigned i_ih = 0; i_ih < CONFIG_T::in_height; i_ih++) {
+        ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width; i_iw++) {
+            #pragma HLS LOOP_FLATTEN
+            if (CONFIG_T::strategy == nnet::latency) {
+                #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+            }
+            if (CONFIG_T::filt_height > 1) {
+                compute_depthwise_output_buffer_2d<data_T, res_T, CONFIG_T>(data.read(), line_buffer, res, weights, biases);
+            } else {
+                compute_depthwise_output_buffer_1d<data_T, res_T, CONFIG_T>(data.read(), res, weights, biases);
+            }
+        }
+    }
+}
+
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pointwise_conv_2d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t   biases[CONFIG_T::n_filt])
+{
+    assert(CONFIG_T::pad_top == 0 && CONFIG_T::pad_bottom == 0 && CONFIG_T::pad_left == 0 && CONFIG_T::pad_right == 0);
+    assert(CONFIG_T::filt_height == 1 && CONFIG_T::filt_width == 1);
+
+    #pragma HLS ARRAY_PARTITION variable=weights complete
+    #pragma HLS ARRAY_PARTITION variable=biases complete
+
+    ReadInputHeight: for (unsigned i_ih = 0; i_ih < CONFIG_T::in_height; i_ih++) {
+        ReadInputWidth: for (unsigned i_iw = 0; i_iw < CONFIG_T::in_width / (data_T::size / CONFIG_T::n_chan); i_iw++) {
+            if (CONFIG_T::strategy == nnet::latency && data_T::size / CONFIG_T::n_chan == 1) {
+                #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+            }
+            if (i_ih % CONFIG_T::stride_height == 0 && i_iw % CONFIG_T::stride_width == 0) {
+                pointwise_mult_buffer<data_T, res_T, CONFIG_T>(data.read(), res, weights, biases);
+            } else {
+                data.read();
+            }
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void separable_conv_2d_cl(
+    hls::stream<data_T> &data,
+    hls::stream<res_T>  &res,
+    typename CONFIG_T::depthwise_config::weight_t depthwise_weights[CONFIG_T::depthwise_config::filt_height * CONFIG_T::depthwise_config::filt_width * CONFIG_T::depthwise_config::n_chan],
+    typename CONFIG_T::pointwise_config::weight_t pointwise_weights[CONFIG_T::pointwise_config::n_chan * CONFIG_T::pointwise_config::n_filt],
+    typename CONFIG_T::depthwise_config::bias_t   depthwise_biases[CONFIG_T::depthwise_config::n_chan],
+    typename CONFIG_T::pointwise_config::bias_t   pointwise_biases[CONFIG_T::pointwise_config::n_filt]
+) {
+    #pragma HLS DATAFLOW
+
+    hls::stream<data_T> depthwise_res;
+    unsigned res_depth = CONFIG_T::depthwise_config::out_height * CONFIG_T::depthwise_config::out_width;
+    #pragma HLS STREAM variable=depthwise_res depth=res_depth
+
+    switch(CONFIG_T::depthwise_config::implementation){
+        case conv_implementation::linebuffer:
+            depthwise_conv_2d_buffer_cl<data_T, data_T, typename CONFIG_T::depthwise_config>(data, depthwise_res, depthwise_weights, depthwise_biases);
+            break;
+        case conv_implementation::encoded:
+            depthwise_conv_2d_encoded_cl<data_T, data_T, typename CONFIG_T::depthwise_config>(data, depthwise_res, depthwise_weights, depthwise_biases);
+            break;
+    } 
+
+    pointwise_conv_2d_cl<data_T, res_T, typename CONFIG_T::pointwise_config>(depthwise_res, res, pointwise_weights, pointwise_biases);
+}
+
+}
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_sepconv_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_sepconv_stream.h
new file mode 100644
index 0000000..6c3c3db
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_sepconv_stream.h
@@ -0,0 +1,307 @@
+#ifndef NNET_SEPARABLE_CONV_STREAM_H_
+#define NNET_SEPARABLE_CONV_STREAM_H_
+
+#include "nnet_common.h"
+#include "hls_stream.h"
+#include "nnet_conv_stream.h"
+
+namespace nnet {
+
+template<class data_T, class res_T, typename CONFIG_T>
+void depthwise_product(
+    data_T    data[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    res_T     res[CONFIG_T::n_chan],
+    typename CONFIG_T::weight_t  weights[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_chan]
+) {
+    #pragma HLS INLINE
+
+    typename CONFIG_T::accum_t mult[CONFIG_T::kernel_size * CONFIG_T::n_chan];
+    typename CONFIG_T::accum_t acc[CONFIG_T::n_chan];
+
+    // Use a function_instantiate in case it helps to explicitly optimize unchanging weights/biases
+    #pragma HLS function_instantiate variable=weights
+
+    #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+
+    #pragma HLS ARRAY_PARTITION variable=mult complete
+
+    int multiplier_limit  = ceil(float(CONFIG_T::kernel_size * CONFIG_T::n_chan) / float(CONFIG_T::reuse_factor)) - floor(float(CONFIG_T::n_zeros) / float(CONFIG_T::reuse_factor));
+    CONFIG_T::mult_config::template product<data_T, typename CONFIG_T::mult_config::weight_t, typename CONFIG_T::mult_config::accum_t>::limit(multiplier_limit);
+
+    // Do the matrix-multiply
+    Product: for(int ii = 0; ii < CONFIG_T::kernel_size * CONFIG_T::n_chan; ii++) {
+        #pragma HLS UNROLL
+        mult[ii] = CONFIG_T::mult_config::template product<data_T, typename CONFIG_T::mult_config::weight_t, typename CONFIG_T::mult_config::accum_t>::product(data[ii], weights[ii]);
+    }
+
+    // Initialize accumulator with input biases
+    ResetAccum: for(int iacc = 0; iacc < CONFIG_T::n_chan; iacc++) {
+        #pragma HLS UNROLL
+        acc[iacc] = (typename CONFIG_T::accum_t) biases[iacc];
+    }
+
+    // Accumulate multiplication result
+    Accum1: for(int ii = 0; ii < CONFIG_T::kernel_size; ii++) {
+        Accum2: for(int jj = 0; jj < CONFIG_T::n_chan; jj++) {
+            int index = ii * CONFIG_T::n_chan + jj;
+            acc[jj] += mult[index];
+        }
+    }
+
+    // Cast to "res_t" type
+    Result: for(int ires = 0; ires < CONFIG_T::n_chan; ires++){
+        #pragma HLS UNROLL
+        res[ires] = cast<data_T, res_T, CONFIG_T>(acc[ires]);
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void depthwise_mult_buffer(
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    res_T& res_pack,
+    hls::stream<res_T>& res_stream,
+    unsigned & outputs_ready,
+    typename CONFIG_T::weight_t weights[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_chan]
+) {
+    #pragma HLS INLINE
+
+    typename data_T::value_type data[CONFIG_T::kernel_size * CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=data complete
+    typename res_T::value_type res[CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=res complete
+
+    InitData: for (int id = 0; id < CONFIG_T::kernel_size * CONFIG_T::n_chan; id++) {
+        #pragma HLS UNROLL
+        data[id] = data_window[id].read();
+    }
+
+    #pragma HLS INLINE region
+    if (CONFIG_T::strategy == nnet::latency) {
+        depthwise_product<typename data_T::value_type, typename res_T::value_type, CONFIG_T>(data, res, weights, biases);
+    } else {
+        assert("Resource strategy for DepthwiseConv2D is not supported." && false);
+    }
+
+    CastLoop: for (unsigned jj = 0; jj < CONFIG_T::n_chan; jj++) {
+        #pragma HLS UNROLL
+        if (res_T::size / CONFIG_T::n_chan == 1) {
+            res_pack[jj] = res[jj];
+        } else {
+            res_pack[outputs_ready * CONFIG_T::n_chan + jj] = res[jj];
+        }
+    }
+
+    if (res_T::size / CONFIG_T::n_chan == 1) {
+        res_stream.write(res_pack);
+    } else {
+        if (outputs_ready == (res_T::size / CONFIG_T::n_chan) - 1) {
+            res_stream.write(res_pack);
+            outputs_ready = 0;
+        } else {
+            outputs_ready++;
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_depthwise_output_encoded(
+    const data_T& in_elem,
+    hls::stream<typename data_T::value_type> data_window[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    hls::stream<res_T> &res,
+    res_T &res_pack,
+    unsigned &outputs_ready,
+    typename CONFIG_T::weight_t weights[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_chan],
+    ap_uint<CONFIG_T::kernel_size> *pixel_idx
+) {
+    #pragma HLS INLINE
+
+    MultLoop: for (unsigned p = 0; p < data_T::size / CONFIG_T::n_chan; p++) {
+        #pragma HLS PIPELINE II=CONFIG_T::reuse_factor
+        CopyDataFilt: for (unsigned f = 0; f < CONFIG_T::kernel_size; f++) {
+            #pragma HLS UNROLL
+            CopyDataChan: for (unsigned c = 0; c < CONFIG_T::n_chan; c++) {
+                #pragma HLS UNROLL
+                if (pixel_idx[p][f]) data_window[f * CONFIG_T::n_chan + c].write(in_elem[p * CONFIG_T::n_chan + c]);
+            }
+        }
+        if (pixel_idx[p][CONFIG_T::kernel_size - 1]) {
+            depthwise_mult_buffer<data_T, res_T, CONFIG_T>(data_window, res_pack, res, outputs_ready, weights, biases);
+        }
+    }
+}
+
+
+template<class data_T, class res_T, typename CONFIG_T>
+void pointwise_mult_buffer(
+    const data_T &data_pack,
+    hls::stream<res_T> &res_stream,
+    typename CONFIG_T::weight_t weights[CONFIG_T::n_chan * CONFIG_T::n_filt],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_filt]
+) {
+    #pragma HLS INLINE
+
+    typename data_T::value_type data[CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=data complete
+
+    typename res_T::value_type res[CONFIG_T::n_filt];
+    #pragma HLS ARRAY_PARTITION variable=res complete
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+
+    InitData: for (int id = 0; id < CONFIG_T::n_chan; id++) {
+        #pragma HLS UNROLL
+        data[id] = data_pack[id];
+    }
+
+    #pragma HLS INLINE region
+    if (CONFIG_T::strategy == nnet::latency) {
+        dense_latency<typename data_T::value_type, typename res_T::value_type, typename CONFIG_T::mult_config>(data, res, weights, biases);
+    } else {
+        dense_resource<typename data_T::value_type, typename res_T::value_type, typename CONFIG_T::mult_config>(data, res, weights, biases);
+    }
+
+    CastLoop: for (unsigned jj = 0; jj < CONFIG_T::n_filt; jj++) {
+        #pragma HLS UNROLL
+        res_pack[jj] = res[jj];
+    }
+
+    res_stream.write(res_pack);
+}
+
+// Line Buffer Implementation (Phil's)
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_depthwise_output_buffer_1d(
+    const data_T& in_elem,
+    hls::stream<res_T> &res_stream,
+    typename CONFIG_T::weight_t weights[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_chan]
+) {
+    #pragma HLS INLINE
+
+    // Thresholds
+    const static int lShiftX = CONFIG_T::filt_width - 1;
+
+    // Counters
+    static int pX = 0;
+    static int sX = 0;
+
+    static typename data_T::value_type kernel_data[CONFIG_T::filt_width * CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=kernel_data complete
+
+    typename res_T::value_type res_out[CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=res_out complete dim = 0
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+
+    // Add pixel to buffer
+    nnet::kernel_shift_1d<data_T, CONFIG_T>(in_elem, kernel_data);
+
+    // Check to see if we have a full kernel
+    if ((sX - lShiftX) == 0 && pX > lShiftX - 1) { 
+      // Dense multiply
+      #pragma HLS INLINE region
+      if (CONFIG_T::strategy == nnet::latency) {
+        depthwise_product<typename data_T::value_type, typename res_T::value_type, CONFIG_T>(kernel_data, res_out, weights, biases);
+      } else {
+        assert("Resource strategy for DepthwiseConv1D is not supported." && false);
+      }
+
+      // Pack output
+      CastLoop: for (unsigned i_ic = 0; i_ic < CONFIG_T::n_filt; i_ic++) {
+          #pragma HLS UNROLL
+          res_pack[i_ic] = res_out[i_ic];
+      }
+
+      // Write output to stream when output ready
+      res_stream.write(res_pack);
+    }
+
+    // Pointer Housekeeping
+    if (pX + 1 == CONFIG_T::in_width)  // Includes padding, end of line (padded)
+    {
+        pX = 0;
+        sX = 0;
+    } else {
+        pX = pX + 1;
+        sX = ((sX - lShiftX) == 0) ? sX - CONFIG_T::stride_width + 1 : sX + 1; 
+    }
+  }
+
+template<class data_T, class res_T, typename CONFIG_T>
+void compute_depthwise_output_buffer_2d(
+    const data_T& in_elem,
+    ap_shift_reg<typename data_T::value_type, CONFIG_T::in_width> line_buffer[MAX(CONFIG_T::filt_height - 1,1)][CONFIG_T::n_chan],
+    hls::stream<res_T> &res_stream,
+    typename CONFIG_T::weight_t weights[CONFIG_T::kernel_size * CONFIG_T::n_chan],
+    typename CONFIG_T::bias_t biases[CONFIG_T::n_chan]
+) {
+    #pragma HLS INLINE
+
+    // Thresholds
+    const static int lShiftX = CONFIG_T::filt_width - 1;
+    const static int lShiftY = CONFIG_T::filt_height - 1;
+
+    // counters
+    static int pX = 0; // pixel X
+    static int pY = 0; // pixel Y
+
+    static int sX = 0; // stride X
+    static int sY = 0; // stride Y
+
+    static typename data_T::value_type kernel_data[CONFIG_T::filt_height * CONFIG_T::filt_width * CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=kernel_data complete
+
+    typename res_T::value_type res_out[CONFIG_T::n_chan];
+    #pragma HLS ARRAY_PARTITION variable=res_out complete dim = 0
+
+    res_T res_pack;
+    #pragma HLS DATA_PACK variable=res_pack
+
+    // Add pixel to buffer
+    nnet::shift_line_buffer<data_T, CONFIG_T>(in_elem, line_buffer, kernel_data);
+
+    // Check to see if we have a full kernel
+    if ((sX - lShiftX) == 0 && (sY - lShiftY) == 0 && pY > lShiftY - 1 && pX > lShiftX - 1) { 
+      // Dense multiply
+      #pragma HLS INLINE region
+      if (CONFIG_T::strategy == nnet::latency) {
+        depthwise_product<typename data_T::value_type, typename res_T::value_type, CONFIG_T>(kernel_data, res_out, weights, biases);
+      } else {
+        assert("Resource strategy for DepthwiseConv2D is not supported." && false);
+      }
+
+      // Pack output
+      CastLoop: for (unsigned i_ic = 0; i_ic < CONFIG_T::n_filt; i_ic++) {
+          #pragma HLS UNROLL
+          res_pack[i_ic] = res_out[i_ic];
+      }
+
+      // Write output to stream when output ready
+      res_stream.write(res_pack);
+    }
+
+    // Pointer Housekeeping
+    if (pX + 1 == CONFIG_T::in_width)  // Includes padding, end of line (padded)
+    {
+        pX = 0;
+        sX = 0;
+        if (pY + 1 == CONFIG_T::in_height) {  // Reached bottom of image
+            pY = 0;
+            sY = 0;
+        } else {
+            pY = pY + 1;
+            sY = ((sY - lShiftY) == 0) ? sY - CONFIG_T::stride_height + 1 : sY + 1; 
+        }
+    } else {
+        pX = pX + 1;
+        sX = ((sX - lShiftX) == 0) ? sX - CONFIG_T::stride_width + 1 : sX + 1; 
+    }
+  }
+
+}
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_stream.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_stream.h
new file mode 100644
index 0000000..6a13404
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_stream.h
@@ -0,0 +1,120 @@
+
+#ifndef NNET_STREAM_H
+#define NNET_STREAM_H
+
+#include "hls_stream.h"
+
+namespace nnet {
+
+struct broadcast_config
+{
+  static const unsigned in_height = 10;
+  static const unsigned in_width = 10;
+  static const unsigned n_chan = 1;
+  static const unsigned n_dupl = 2;
+};
+
+template<class data_T, class res_T, int N>
+void clone_stream(hls::stream<data_T> &data, hls::stream<res_T> &res1, hls::stream<res_T> &res2) {
+    CloneLoop: for (int i = 0; i < N / data_T::size; i++) {
+        #pragma HLS PIPELINE
+
+        data_T in_data = data.read();
+        res_T out_data1;
+        res_T out_data2;
+        #pragma HLS DATA_PACK variable=out_data1
+        #pragma HLS DATA_PACK variable=out_data2
+
+        ClonePack: for (int j = 0; j < data_T::size; j++) {
+            #pragma HLS UNROLL
+            out_data1[j] = in_data[j];
+            out_data2[j] = in_data[j];
+        }
+
+        res1.write(out_data1);
+        res2.write(out_data2);
+    }
+}
+
+template<class data_T, class res_T, int N>
+void repack_stream(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    if (data_T::size == res_T::size) {
+        for (int i = 0; i < N / data_T::size; i++) {
+            #pragma HLS PIPELINE
+
+            data_T in_data = data.read();
+            res_T out_data;
+            #pragma HLS DATA_PACK variable=out_data
+
+            for (int j = 0; j < data_T::size; j++) {
+                #pragma HLS UNROLL
+                out_data[j] = in_data[j];
+            }
+
+            res.write(out_data);
+        }
+    } else if (data_T::size > res_T::size) {
+        constexpr unsigned pack_diff = data_T::size / res_T::size;
+        for (int i = 0; i < N / data_T::size; i++) {
+            if (N / data_T::size > 1) {
+                #pragma HLS PIPELINE
+            }
+
+            data_T in_data = data.read();
+            res_T out_data;
+            #pragma HLS DATA_PACK variable=out_data
+
+            for (int j = 0; j < pack_diff; j++) {
+                #pragma HLS PIPELINE
+
+                res_T out_data;
+                for (int k = 0; k < res_T::size; k++) {
+                    #pragma HLS UNROLL
+                    out_data[k] = in_data[j * res_T::size + k];
+                }
+                res.write(out_data);
+            }
+        }
+    } else { // data_T::size < res_T::size
+        res_T out_data;
+        constexpr unsigned pack_diff = res_T::size / data_T::size;
+        unsigned pack_cnt = 0;
+        for (int i = 0; i < N / data_T::size; i++) {
+            #pragma HLS PIPELINE
+
+            data_T in_data = data.read();
+            for (int j = 0; j < data_T::size; j++) {
+                #pragma HLS UNROLL
+                out_data[pack_cnt * data_T::size + j] = in_data[j];
+            }
+
+            if (pack_cnt == pack_diff - 1) {
+                res.write(out_data);
+                pack_cnt = 0;
+            } else {
+                pack_cnt++;
+            }
+        }
+    }
+}
+
+template<class data_T, class res_T, typename CONFIG_T>
+void broadcast_stream(hls::stream<data_T> &data, hls::stream<res_T> &res) {
+    BroadcastLoop: for (int i = 0; i < CONFIG_T::in_height * CONFIG_T::in_width * CONFIG_T::n_chan / data_T::size; i++) {
+        #pragma HLS PIPELINE
+        data_T in_data = data.read();
+        for (int j = 0; j < CONFIG_T::n_dupl; j++) {
+            #pragma HLS PIPELINE
+            res_T out_data;
+            #pragma HLS DATA_PACK variable=out_data
+            for (int k = 0; k < res_T::size; k++) {
+                #pragma HLS UNROLL
+                out_data[k] = in_data[k];
+            }
+            res.write(out_data);
+        }
+    }
+}
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/nnet_utils/nnet_types.h b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_types.h
new file mode 100644
index 0000000..64bc0fb
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/nnet_utils/nnet_types.h
@@ -0,0 +1,42 @@
+#ifndef NNET_TYPES_H_
+#define NNET_TYPES_H_
+
+#include <assert.h>
+#include <cstddef>
+#include <cstdio>
+
+namespace nnet {
+
+// Fixed-size array
+template<typename T, unsigned N>
+struct array {
+    typedef T value_type;
+    static const unsigned size = N;
+
+    T data[N];
+
+    T& operator[](size_t pos) {
+        return data[pos];
+    }
+    
+    const T& operator[](size_t pos) const {
+        return data[pos];
+    }
+
+    array& operator=(const array &other) {
+        if(&other == this)
+            return *this;
+
+        assert(N == other.size && "Array sizes must match.");
+        
+        for (unsigned i = 0; i < N; i++) {
+            #pragma HLS UNROLL
+            data[i] = other[i];
+        }
+        return *this;
+    }  
+};
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/parameters.h b/model_1/hls4ml_prj/firmware/parameters.h
new file mode 100644
index 0000000..368dc24
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/parameters.h
@@ -0,0 +1,193 @@
+#ifndef PARAMETERS_H_
+#define PARAMETERS_H_
+
+#include "ap_int.h"
+#include "ap_fixed.h"
+
+#include "nnet_utils/nnet_helpers.h"
+//hls-fpga-machine-learning insert includes
+#include "nnet_utils/nnet_activation.h"
+#include "nnet_utils/nnet_activation_stream.h"
+#include "nnet_utils/nnet_dense.h"
+#include "nnet_utils/nnet_dense_compressed.h"
+#include "nnet_utils/nnet_dense_stream.h"
+ 
+//hls-fpga-machine-learning insert weights
+#include "weights/w2.h"
+#include "weights/b2.h"
+#include "weights/w4.h"
+#include "weights/b4.h"
+#include "weights/w6.h"
+#include "weights/b6.h"
+#include "weights/w8.h"
+#include "weights/b8.h"
+#include "weights/w10.h"
+#include "weights/b10.h"
+#include "weights/w12.h"
+#include "weights/b12.h"
+
+//hls-fpga-machine-learning insert layer-config
+// dense
+struct config2 : nnet::dense_config {
+    static const unsigned n_in = N_INPUT_1_1;
+    static const unsigned n_out = N_LAYER_2;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned strategy = nnet::latency;
+    static const unsigned reuse_factor = 1;
+    static const unsigned n_zeros = 0;
+    static const unsigned n_nonzeros = 100352;
+    static const bool store_weights_in_bram = false;
+    typedef ap_fixed<16,6> accum_t;
+    typedef model_default_t bias_t;
+    typedef model_default_t weight_t;
+    typedef ap_uint<1> index_t;
+    template<class x_T, class y_T, class res_T>
+    using product = nnet::product::mult<x_T, y_T, res_T>;
+};
+
+// dense_relu
+struct relu_config3 : nnet::activ_config {
+    static const unsigned n_in = N_LAYER_2;
+    static const unsigned table_size = 1024;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned reuse_factor = 1;
+    typedef ap_fixed<18,8> table_t;
+};
+
+// dense_1
+struct config4 : nnet::dense_config {
+    static const unsigned n_in = N_LAYER_2;
+    static const unsigned n_out = N_LAYER_4;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned strategy = nnet::latency;
+    static const unsigned reuse_factor = 1;
+    static const unsigned n_zeros = 0;
+    static const unsigned n_nonzeros = 8192;
+    static const bool store_weights_in_bram = false;
+    typedef ap_fixed<16,6> accum_t;
+    typedef model_default_t bias_t;
+    typedef model_default_t weight_t;
+    typedef ap_uint<1> index_t;
+    template<class x_T, class y_T, class res_T>
+    using product = nnet::product::mult<x_T, y_T, res_T>;
+};
+
+// dense_1_relu
+struct relu_config5 : nnet::activ_config {
+    static const unsigned n_in = N_LAYER_4;
+    static const unsigned table_size = 1024;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned reuse_factor = 1;
+    typedef ap_fixed<18,8> table_t;
+};
+
+// encoder_output
+struct config6 : nnet::dense_config {
+    static const unsigned n_in = N_LAYER_4;
+    static const unsigned n_out = N_LAYER_6;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned strategy = nnet::latency;
+    static const unsigned reuse_factor = 1;
+    static const unsigned n_zeros = 0;
+    static const unsigned n_nonzeros = 128;
+    static const bool store_weights_in_bram = false;
+    typedef ap_fixed<16,6> accum_t;
+    typedef model_default_t bias_t;
+    typedef model_default_t weight_t;
+    typedef ap_uint<1> index_t;
+    template<class x_T, class y_T, class res_T>
+    using product = nnet::product::mult<x_T, y_T, res_T>;
+};
+
+// encoder_output_relu
+struct relu_config7 : nnet::activ_config {
+    static const unsigned n_in = N_LAYER_6;
+    static const unsigned table_size = 1024;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned reuse_factor = 1;
+    typedef ap_fixed<18,8> table_t;
+};
+
+// dense_2
+struct config8 : nnet::dense_config {
+    static const unsigned n_in = N_LAYER_6;
+    static const unsigned n_out = N_LAYER_8;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned strategy = nnet::latency;
+    static const unsigned reuse_factor = 1;
+    static const unsigned n_zeros = 0;
+    static const unsigned n_nonzeros = 128;
+    static const bool store_weights_in_bram = false;
+    typedef ap_fixed<16,6> accum_t;
+    typedef model_default_t bias_t;
+    typedef model_default_t weight_t;
+    typedef ap_uint<1> index_t;
+    template<class x_T, class y_T, class res_T>
+    using product = nnet::product::mult<x_T, y_T, res_T>;
+};
+
+// dense_2_relu
+struct relu_config9 : nnet::activ_config {
+    static const unsigned n_in = N_LAYER_8;
+    static const unsigned table_size = 1024;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned reuse_factor = 1;
+    typedef ap_fixed<18,8> table_t;
+};
+
+// dense_3
+struct config10 : nnet::dense_config {
+    static const unsigned n_in = N_LAYER_8;
+    static const unsigned n_out = N_LAYER_10;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned strategy = nnet::latency;
+    static const unsigned reuse_factor = 1;
+    static const unsigned n_zeros = 0;
+    static const unsigned n_nonzeros = 8192;
+    static const bool store_weights_in_bram = false;
+    typedef ap_fixed<16,6> accum_t;
+    typedef model_default_t bias_t;
+    typedef model_default_t weight_t;
+    typedef ap_uint<1> index_t;
+    template<class x_T, class y_T, class res_T>
+    using product = nnet::product::mult<x_T, y_T, res_T>;
+};
+
+// dense_3_relu
+struct relu_config11 : nnet::activ_config {
+    static const unsigned n_in = N_LAYER_10;
+    static const unsigned table_size = 1024;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned reuse_factor = 1;
+    typedef ap_fixed<18,8> table_t;
+};
+
+// ecoder_output
+struct config12 : nnet::dense_config {
+    static const unsigned n_in = N_LAYER_10;
+    static const unsigned n_out = N_LAYER_12;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned strategy = nnet::latency;
+    static const unsigned reuse_factor = 1;
+    static const unsigned n_zeros = 0;
+    static const unsigned n_nonzeros = 100352;
+    static const bool store_weights_in_bram = false;
+    typedef ap_fixed<16,6> accum_t;
+    typedef model_default_t bias_t;
+    typedef model_default_t weight_t;
+    typedef ap_uint<1> index_t;
+    template<class x_T, class y_T, class res_T>
+    using product = nnet::product::mult<x_T, y_T, res_T>;
+};
+
+// ecoder_output_sigmoid
+struct sigmoid_config13 : nnet::activ_config {
+    static const unsigned n_in = N_LAYER_12;
+    static const unsigned table_size = 1024;
+    static const unsigned io_type = nnet::io_parallel;
+    static const unsigned reuse_factor = 1;
+    typedef ap_fixed<18,8> table_t;
+};
+
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/b10.h b/model_1/hls4ml_prj/firmware/weights/b10.h
new file mode 100644
index 0000000..ba793a3
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b10.h
@@ -0,0 +1,15 @@
+//Numpy array shape [128]
+//Min -0.289923638105
+//Max 0.964105486870
+//Number of zeros 21
+
+#ifndef B10_H_
+#define B10_H_
+
+#ifndef __SYNTHESIS__
+model_default_t b10[128];
+#else
+model_default_t b10[128] = {0.0563411936, 0.0901205912, -0.0054596392, 0.0423877910, 0.0410075411, 0.0000000000, -0.0099724857, 0.0093818828, -0.0412447155, 0.1745020151, 0.1347084790, 0.0000000000, 0.0081430422, 0.0593126677, 0.2021944523, 0.0369338579, 0.5248419046, 0.0464899316, 0.0509243272, 0.0000000000, 0.0091477828, 0.4828712344, 0.0436681658, -0.0866716281, 0.1132095754, 0.0907230154, 0.0285819340, 0.1767815500, 0.0334526151, 0.4540293217, 0.0171458665, 0.0428425111, 0.6175319552, 0.1585544944, -0.2828673124, 0.9581031799, -0.1228909865, -0.0138136949, 0.0498096608, 0.1533503830, 0.2496771961, -0.0163597651, 0.7668238282, 0.0334771127, -0.0039314935, 0.8062980175, 0.0271378439, 0.5407310724, 0.0000000000, 0.3267443776, -0.2138318717, 0.2864348590, 0.0971224979, 0.6271553040, -0.0201856587, 0.0809063092, 0.0787378922, 0.0377311446, -0.0117548723, -0.0189392474, 0.0652147606, 0.0034164668, -0.1215668619, 0.3549564183, 0.0965118408, 0.9641054869, 0.0704768449, 0.0727795437, -0.0377357751, 0.0000000000, 0.0000000000, -0.1845853329, 0.0000000000, 0.0791801289, 0.2522611320, -0.0165859498, 0.0062663299, 0.0000000000, 0.2137696296, 0.0694802329, 0.1285533011, -0.0143099083, 0.0080002071, 0.0802555680, 0.0000000000, 0.2866473198, 0.3114060163, 0.0000000000, 0.0000000000, -0.0036031767, 0.0000000000, -0.1256172806, -0.2899236381, 0.0000000000, 0.0277307946, 0.0663242266, 0.1003493816, -0.2711140811, 0.0625475273, 0.0882070884, 0.4724733531, 0.0000000000, 0.0643851832, 0.0000000000, -0.0158664808, 0.0621246286, 0.0443842709, -0.0058661262, 0.0000000000, 0.3025134504, 0.0276738722, 0.2164741009, 0.0000000000, 0.0000000000, 0.0000000000, 0.6037406921, 0.0000000000, -0.0053245081, 0.0572495125, -0.0869827718, -0.0077316896, 0.1063261330, -0.2023086399, 0.0000000000, 0.0766916424, 0.1522105038, 0.1098891050, 0.3478351533};
+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/b10.txt b/model_1/hls4ml_prj/firmware/weights/b10.txt
new file mode 100644
index 0000000..f2b65fe
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b10.txt
@@ -0,0 +1 @@
+0.0563411936, 0.0901205912, -0.0054596392, 0.0423877910, 0.0410075411, 0.0000000000, -0.0099724857, 0.0093818828, -0.0412447155, 0.1745020151, 0.1347084790, 0.0000000000, 0.0081430422, 0.0593126677, 0.2021944523, 0.0369338579, 0.5248419046, 0.0464899316, 0.0509243272, 0.0000000000, 0.0091477828, 0.4828712344, 0.0436681658, -0.0866716281, 0.1132095754, 0.0907230154, 0.0285819340, 0.1767815500, 0.0334526151, 0.4540293217, 0.0171458665, 0.0428425111, 0.6175319552, 0.1585544944, -0.2828673124, 0.9581031799, -0.1228909865, -0.0138136949, 0.0498096608, 0.1533503830, 0.2496771961, -0.0163597651, 0.7668238282, 0.0334771127, -0.0039314935, 0.8062980175, 0.0271378439, 0.5407310724, 0.0000000000, 0.3267443776, -0.2138318717, 0.2864348590, 0.0971224979, 0.6271553040, -0.0201856587, 0.0809063092, 0.0787378922, 0.0377311446, -0.0117548723, -0.0189392474, 0.0652147606, 0.0034164668, -0.1215668619, 0.3549564183, 0.0965118408, 0.9641054869, 0.0704768449, 0.0727795437, -0.0377357751, 0.0000000000, 0.0000000000, -0.1845853329, 0.0000000000, 0.0791801289, 0.2522611320, -0.0165859498, 0.0062663299, 0.0000000000, 0.2137696296, 0.0694802329, 0.1285533011, -0.0143099083, 0.0080002071, 0.0802555680, 0.0000000000, 0.2866473198, 0.3114060163, 0.0000000000, 0.0000000000, -0.0036031767, 0.0000000000, -0.1256172806, -0.2899236381, 0.0000000000, 0.0277307946, 0.0663242266, 0.1003493816, -0.2711140811, 0.0625475273, 0.0882070884, 0.4724733531, 0.0000000000, 0.0643851832, 0.0000000000, -0.0158664808, 0.0621246286, 0.0443842709, -0.0058661262, 0.0000000000, 0.3025134504, 0.0276738722, 0.2164741009, 0.0000000000, 0.0000000000, 0.0000000000, 0.6037406921, 0.0000000000, -0.0053245081, 0.0572495125, -0.0869827718, -0.0077316896, 0.1063261330, -0.2023086399, 0.0000000000, 0.0766916424, 0.1522105038, 0.1098891050, 0.3478351533
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/b12.h b/model_1/hls4ml_prj/firmware/weights/b12.h
new file mode 100644
index 0000000..bf915e4
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b12.h
@@ -0,0 +1,15 @@
+//Numpy array shape [784]
+//Min -0.557436227798
+//Max 0.188575848937
+//Number of zeros 0
+
+#ifndef B12_H_
+#define B12_H_
+
+#ifndef __SYNTHESIS__
+model_default_t b12[784];
+#else
+model_default_t b12[784] = {-0.0302925743, -0.0323416144, -0.0399386287, -0.0280058626, -0.0281412527, -0.0343991108, -0.0312841088, -0.0311926752, -0.0298394896, -0.0300760493, -0.0320610963, -0.0310727600, -0.0301502850, -0.0271047987, -0.0309349094, -0.0261824653, -0.0297538862, -0.0296175703, -0.0343044549, -0.0308477264, -0.0273809880, -0.0383024588, -0.0310344249, -0.0313213989, -0.0305076782, -0.0300725047, -0.0383807793, -0.0351116061, -0.0293116253, -0.0336604454, -0.0329398140, -0.0322506987, -0.0321502127, -0.0268918443, -0.0325322077, -0.0276005957, -0.0294997077, -0.0267338213, -0.0370591506, -0.0355297439, -0.0298816431, -0.0307553224, -0.0302713849, -0.0287091136, -0.0322699957, -0.0307619125, -0.0290182475, -0.0297649112, -0.0307495967, -0.0275365449, -0.0337618329, -0.0318790115, -0.0283170063, -0.0304332953, -0.0302713662, -0.0290690511, -0.0309400130, -0.0281878300, -0.0302483533, -0.0389609672, -0.0284423996, -0.0284215175, -0.0283769835, -0.0337144583, 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+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/b12.txt b/model_1/hls4ml_prj/firmware/weights/b12.txt
new file mode 100644
index 0000000..eae9f61
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b12.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/b2.h b/model_1/hls4ml_prj/firmware/weights/b2.h
new file mode 100644
index 0000000..16d4c59
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b2.h
@@ -0,0 +1,15 @@
+//Numpy array shape [128]
+//Min -0.372153937817
+//Max 0.740155339241
+//Number of zeros 0
+
+#ifndef B2_H_
+#define B2_H_
+
+#ifndef __SYNTHESIS__
+model_default_t b2[128];
+#else
+model_default_t b2[128] = {-0.0159076545, 0.3537502587, 0.0350487418, 0.1070150435, 0.1077778637, -0.0167995431, 0.1079417616, 0.1483102143, 0.0360456444, 0.6670153141, -0.2667517364, 0.1540724933, 0.1861016899, -0.0136832409, -0.1279028952, 0.4381679296, 0.1522240788, -0.0171369445, 0.1881253272, 0.1425249428, 0.2355151474, 0.0300067663, 0.0557061657, -0.0241414141, -0.2589327097, -0.0172147583, 0.2516817153, 0.1358118206, -0.0227994639, -0.0157579985, 0.3096978366, 0.4709223211, 0.1767027676, -0.0144620975, 0.0994442105, -0.0145926811, 0.4506802857, -0.0139673175, 0.2189942747, -0.0173691902, 0.0453611836, 0.0391293243, 0.0956988111, -0.0760397241, 0.1652096063, -0.0670340434, -0.0185385626, 0.1548633426, -0.0219991580, 0.1528024822, -0.0175007917, -0.0161746535, 0.2828319073, 0.2378503829, 0.1253592223, 0.1160033867, -0.0265171453, 0.0921318233, 0.7401553392, 0.0354856178, -0.0579514876, 0.0899377912, 0.0855800360, -0.0120199053, 0.2225090563, 0.2388966680, 0.2650041282, -0.0251975432, 0.2794161439, 0.2614513338, 0.1841154397, 0.0929609537, 0.1727735102, 0.4082507491, 0.2864688933, -0.0157372598, -0.0339476205, 0.1435472667, -0.0169724878, -0.0001539622, 0.2587320805, -0.0177119058, 0.6146150827, 0.0136253871, 0.0549077988, 0.1556056440, -0.0148502011, 0.0157464296, 0.3479851484, 0.0698142797, 0.2968578041, -0.0085641686, 0.1802492887, 0.0737154186, 0.4622211158, 0.1829001456, -0.0164566990, 0.1703426391, 0.1675969809, -0.0156947616, -0.0183128193, -0.0184801370, 0.2179798037, 0.2764606774, 0.1078149080, -0.0132545549, -0.0197760295, -0.0244939364, -0.1376467645, 0.0139207635, 0.1198376641, 0.1151235774, 0.1300821602, -0.0154692819, 0.2066581696, 0.1740918458, 0.1191261336, 0.1768275201, -0.0167813879, 0.2113632411, -0.0148023656, 0.0706837773, -0.0167929195, 0.3857131600, 0.6017785668, -0.3721539378, 0.0179192033, 0.0780076161};
+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/b2.txt b/model_1/hls4ml_prj/firmware/weights/b2.txt
new file mode 100644
index 0000000..3e3636a
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b2.txt
@@ -0,0 +1 @@
+-0.0159076545, 0.3537502587, 0.0350487418, 0.1070150435, 0.1077778637, -0.0167995431, 0.1079417616, 0.1483102143, 0.0360456444, 0.6670153141, -0.2667517364, 0.1540724933, 0.1861016899, -0.0136832409, -0.1279028952, 0.4381679296, 0.1522240788, -0.0171369445, 0.1881253272, 0.1425249428, 0.2355151474, 0.0300067663, 0.0557061657, -0.0241414141, -0.2589327097, -0.0172147583, 0.2516817153, 0.1358118206, -0.0227994639, -0.0157579985, 0.3096978366, 0.4709223211, 0.1767027676, -0.0144620975, 0.0994442105, -0.0145926811, 0.4506802857, -0.0139673175, 0.2189942747, -0.0173691902, 0.0453611836, 0.0391293243, 0.0956988111, -0.0760397241, 0.1652096063, -0.0670340434, -0.0185385626, 0.1548633426, -0.0219991580, 0.1528024822, -0.0175007917, -0.0161746535, 0.2828319073, 0.2378503829, 0.1253592223, 0.1160033867, -0.0265171453, 0.0921318233, 0.7401553392, 0.0354856178, -0.0579514876, 0.0899377912, 0.0855800360, -0.0120199053, 0.2225090563, 0.2388966680, 0.2650041282, -0.0251975432, 0.2794161439, 0.2614513338, 0.1841154397, 0.0929609537, 0.1727735102, 0.4082507491, 0.2864688933, -0.0157372598, -0.0339476205, 0.1435472667, -0.0169724878, -0.0001539622, 0.2587320805, -0.0177119058, 0.6146150827, 0.0136253871, 0.0549077988, 0.1556056440, -0.0148502011, 0.0157464296, 0.3479851484, 0.0698142797, 0.2968578041, -0.0085641686, 0.1802492887, 0.0737154186, 0.4622211158, 0.1829001456, -0.0164566990, 0.1703426391, 0.1675969809, -0.0156947616, -0.0183128193, -0.0184801370, 0.2179798037, 0.2764606774, 0.1078149080, -0.0132545549, -0.0197760295, -0.0244939364, -0.1376467645, 0.0139207635, 0.1198376641, 0.1151235774, 0.1300821602, -0.0154692819, 0.2066581696, 0.1740918458, 0.1191261336, 0.1768275201, -0.0167813879, 0.2113632411, -0.0148023656, 0.0706837773, -0.0167929195, 0.3857131600, 0.6017785668, -0.3721539378, 0.0179192033, 0.0780076161
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/b4.h b/model_1/hls4ml_prj/firmware/weights/b4.h
new file mode 100644
index 0000000..f2fad77
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b4.h
@@ -0,0 +1,15 @@
+//Numpy array shape [64]
+//Min -0.407243847847
+//Max 0.363378673792
+//Number of zeros 0
+
+#ifndef B4_H_
+#define B4_H_
+
+#ifndef __SYNTHESIS__
+model_default_t b4[64];
+#else
+model_default_t b4[64] = {-0.0178948697, -0.0219059084, -0.0365103222, -0.0241461582, -0.0362796001, 0.1376670003, -0.3566413522, 0.0256951954, 0.1102882251, -0.1343680918, 0.1841858774, -0.4072438478, 0.0788901970, 0.1651297659, 0.0525129661, 0.0605876707, 0.3475530446, 0.1663242280, -0.0421531275, -0.1707555205, 0.2011818439, -0.0220936909, -0.0250488296, 0.1369738281, -0.0284161177, -0.0137947630, 0.1641891003, 0.1255390644, 0.1276032478, -0.1742733568, 0.0502241738, -0.0753756389, 0.1514173597, -0.1843371540, 0.1502337456, -0.0114498856, 0.1818476170, 0.1801761985, -0.0261800792, 0.0093805827, 0.0857421160, 0.0862519220, 0.1583384722, 0.0178061910, -0.0342405476, 0.0543225780, 0.3633786738, 0.2119298875, -0.0427544378, 0.1151982024, 0.0484854840, 0.0091737891, 0.1588285565, 0.0909368098, -0.0255258586, -0.0563766956, 0.0538526997, 0.2192914933, -0.0276375562, -0.1485933065, 0.1779690981, 0.0474430844, 0.1113862544, 0.2349776328};
+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/b4.txt b/model_1/hls4ml_prj/firmware/weights/b4.txt
new file mode 100644
index 0000000..2d5e8ea
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b4.txt
@@ -0,0 +1 @@
+-0.0178948697, -0.0219059084, -0.0365103222, -0.0241461582, -0.0362796001, 0.1376670003, -0.3566413522, 0.0256951954, 0.1102882251, -0.1343680918, 0.1841858774, -0.4072438478, 0.0788901970, 0.1651297659, 0.0525129661, 0.0605876707, 0.3475530446, 0.1663242280, -0.0421531275, -0.1707555205, 0.2011818439, -0.0220936909, -0.0250488296, 0.1369738281, -0.0284161177, -0.0137947630, 0.1641891003, 0.1255390644, 0.1276032478, -0.1742733568, 0.0502241738, -0.0753756389, 0.1514173597, -0.1843371540, 0.1502337456, -0.0114498856, 0.1818476170, 0.1801761985, -0.0261800792, 0.0093805827, 0.0857421160, 0.0862519220, 0.1583384722, 0.0178061910, -0.0342405476, 0.0543225780, 0.3633786738, 0.2119298875, -0.0427544378, 0.1151982024, 0.0484854840, 0.0091737891, 0.1588285565, 0.0909368098, -0.0255258586, -0.0563766956, 0.0538526997, 0.2192914933, -0.0276375562, -0.1485933065, 0.1779690981, 0.0474430844, 0.1113862544, 0.2349776328
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/b6.h b/model_1/hls4ml_prj/firmware/weights/b6.h
new file mode 100644
index 0000000..413ad5e
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b6.h
@@ -0,0 +1,15 @@
+//Numpy array shape [2]
+//Min 0.127385094762
+//Max 0.324288964272
+//Number of zeros 0
+
+#ifndef B6_H_
+#define B6_H_
+
+#ifndef __SYNTHESIS__
+model_default_t b6[2];
+#else
+model_default_t b6[2] = {0.1273850948, 0.3242889643};
+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/b6.txt b/model_1/hls4ml_prj/firmware/weights/b6.txt
new file mode 100644
index 0000000..d1b9eb8
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b6.txt
@@ -0,0 +1 @@
+0.1273850948, 0.3242889643
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/b8.h b/model_1/hls4ml_prj/firmware/weights/b8.h
new file mode 100644
index 0000000..9df4ccd
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b8.h
@@ -0,0 +1,15 @@
+//Numpy array shape [64]
+//Min -0.819486320019
+//Max 1.252106666565
+//Number of zeros 19
+
+#ifndef B8_H_
+#define B8_H_
+
+#ifndef __SYNTHESIS__
+model_default_t b8[64];
+#else
+model_default_t b8[64] = {0.9116554856, 0.6417621970, -0.3277860284, 0.2047643214, 0.3333912194, 0.0929917917, 0.1754881740, 0.2867740989, 0.0000000000, 0.0429598428, -0.0445397496, 0.3732690215, 0.5324907899, 0.8947879672, 0.2089297324, -0.1648707688, 0.0000000000, 0.1090742797, 0.5743152499, 0.0000000000, 0.0000000000, -0.4483927190, -0.0447519869, 0.7441324592, 0.3290302157, 0.0000000000, 0.0000000000, -0.0950412005, 0.0723769590, 0.0000000000, -0.1597335935, 0.0000000000, 0.1511487663, 0.4967732131, 0.0000000000, 0.1278440952, 0.2261707634, 0.0000000000, 0.1474430114, 1.2521066666, 0.0000000000, 0.9732121229, 0.3135904074, -0.8194863200, 0.9351823330, 0.0000000000, 0.0000000000, 0.5619241595, 0.0000000000, 1.2367826700, 0.0000000000, -0.1281038821, 0.0000000000, 0.0000000000, -0.4014127851, 0.2246306688, 0.0000000000, 0.1236402765, 0.3808113039, 0.3428134620, -0.0142224766, 0.5248672962, 0.0000000000, -0.1288120896};
+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/b8.txt b/model_1/hls4ml_prj/firmware/weights/b8.txt
new file mode 100644
index 0000000..59464ab
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/b8.txt
@@ -0,0 +1 @@
+0.9116554856, 0.6417621970, -0.3277860284, 0.2047643214, 0.3333912194, 0.0929917917, 0.1754881740, 0.2867740989, 0.0000000000, 0.0429598428, -0.0445397496, 0.3732690215, 0.5324907899, 0.8947879672, 0.2089297324, -0.1648707688, 0.0000000000, 0.1090742797, 0.5743152499, 0.0000000000, 0.0000000000, -0.4483927190, -0.0447519869, 0.7441324592, 0.3290302157, 0.0000000000, 0.0000000000, -0.0950412005, 0.0723769590, 0.0000000000, -0.1597335935, 0.0000000000, 0.1511487663, 0.4967732131, 0.0000000000, 0.1278440952, 0.2261707634, 0.0000000000, 0.1474430114, 1.2521066666, 0.0000000000, 0.9732121229, 0.3135904074, -0.8194863200, 0.9351823330, 0.0000000000, 0.0000000000, 0.5619241595, 0.0000000000, 1.2367826700, 0.0000000000, -0.1281038821, 0.0000000000, 0.0000000000, -0.4014127851, 0.2246306688, 0.0000000000, 0.1236402765, 0.3808113039, 0.3428134620, -0.0142224766, 0.5248672962, 0.0000000000, -0.1288120896
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/w10.h b/model_1/hls4ml_prj/firmware/weights/w10.h
new file mode 100644
index 0000000..4919ac1
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w10.h
@@ -0,0 +1,15 @@
+//Numpy array shape [64, 128]
+//Min -1.913820147514
+//Max 0.958133757114
+//Number of zeros 0
+
+#ifndef W10_H_
+#define W10_H_
+
+#ifndef __SYNTHESIS__
+model_default_t w10[8192];
+#else
+model_default_t w10[8192] = {0.0711485222, 0.1183774024, 0.1268402785, 0.3771098256, -0.1766405702, 0.1474550813, -0.0164577011, -0.1528083682, 0.1002150327, -0.0478152931, 0.0859498382, -0.0684490502, 0.0091116875, 0.0814607814, 0.3133277297, 0.2122834772, 0.0266019553, -0.0938843936, -0.2356852740, -0.0005148202, 0.0291043371, 0.4595880210, -0.1573119164, -0.1995550394, -0.1199680641, 0.2002227306, 0.0287034102, 0.3031578064, 0.0036726324, 0.3382670879, -0.0819384232, -0.2838631272, 0.6767870784, -0.3724706471, -0.0996443331, 0.5014832020, -0.0104604438, -0.1518561244, 0.1520589292, 0.1257798821, 0.1586939991, -0.2167888880, 0.0924798399, 0.1946399957, -0.0975541919, 0.2007210553, 0.1634091139, 0.0371725485, 0.0339954346, 0.5033847690, 0.7780697942, -0.7122339606, 0.1016548127, 0.3809193969, 0.3486288190, -0.0704989806, 0.0246585328, -0.1459894329, 0.1233595312, 0.0937005952, -0.1215899810, -0.1736155450, -0.6664772630, -0.0582048446, 0.3481180072, 0.4063063264, -0.1088707298, 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-0.0804246962, 0.1288464516, 0.0584239550, 0.0446808264, -0.0640234202, -0.0570443831, -0.1116168350, 0.3706718087, 0.0170899443, 0.5054997802, 0.1552046686, -0.2583371103, -0.0479618013, 0.1559329480, 0.6027809978, -0.0958353803, -0.0090468638, 0.1136312038, 0.2142695785, 0.4356980622, -0.1973216236, -0.0762193054, 0.1991847157, -0.2237563133, 0.1019178852, 0.3591220975, 0.6372525096, 0.1499971449, 0.5657117367, -0.0593862385, -0.0848867074, 0.4304741621, -0.1925422251, -0.0462423600, 0.2336026132, -0.1421941221, -0.0468251333, 0.0778083056, 0.1184382662, 0.1638597101, 0.1053160504, 0.0249052942, 0.3932701349, 0.1461409032, 0.4799572229, 0.0042702826, -0.1269129366, 0.0976974070, -0.0570705645, 0.2133925706, 0.1040244028, -0.1082497090, 0.1532146931, 0.0094202952, -0.0522377379, -0.2146094590, -0.0806265995, -0.1791776419, 0.6031731963, 0.1761626452, -0.0673816726, 0.0612191744, -0.1651086360, 0.0486733317, -0.1390186399, -0.1505784988, -0.0196387302, 0.2641560137, 0.0704465061, 0.0789258182, -0.0371320099, 0.4922623038, 0.2631997764, 0.1964576840, 0.0561740883, 0.2787259221, 0.3724322319, 0.1587575525, 0.3253241479, 0.1340151578, 0.0545067042, -0.0273172706, -0.1119860336, -0.1073555201, -0.0777302906, 0.0327367932, -0.0675932616, 0.1164573207, 0.0590923317, 0.1083640084, 0.0447361283, 0.0121658202, -0.0554495752, -0.3253154457, 0.0411757827, -0.2815483809, 0.1392745525, 0.1155744344, -0.1395620108, 0.0968681425, 0.0671777278, 0.0675922930, -0.0498413816, 0.3230519891, -0.0226126760, -0.0768622383, 0.0777309686, 0.1677971035, 0.4925158024, 0.1288412660, -0.0659606457, 0.2607370317, -0.0334351733, -0.1346355379, -0.1394234300, 0.0980786979, 0.1055837125, 0.1182124764, 0.1560422182, 0.2369200289, 0.0139408754, 0.0114662619, -0.1485929191, -0.1580806822, -0.0106909508, -0.1229193658, -0.0141901225, -0.1352143884, 0.0319204070, -0.0995625705, 0.0207589809, -0.2140125632, 0.0002305061, -0.1566291302, 0.0526404791, -0.1340456903, -0.1104645804, -0.1967563182, 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-0.0789323747, -0.0318750069, 0.1522992104, 0.1615933180, 0.3967038989, 0.1656897813, -0.0222502518, 0.0185757857, -0.3001782000, -0.4574815333, -0.4017780125, 0.1959928423, 0.1406324208, -0.1904294789, -0.1282738596, 0.2688387334, -0.0584486239, -1.0361814499, -0.1402468085, 0.1276805550, 0.0917421579, -0.2354868948, 0.1166819111, -0.1282604933, 0.0098641217, -0.2080227584, -0.3744165003, 0.1410263628, 0.1292912066, 0.3230203688, -0.0709596574, 0.1971074790, 0.1369492561, -0.0192122255, 0.1341730058, 0.0300856140, 0.1618361324, -0.3189656734, 0.2760095894, -0.1183482632, 0.2234912217, -0.0565204993, 0.1437164545, -0.0132421944, -0.1410961449, -0.0369656980, 0.1233347282, -0.1107395813, -0.1401031315, -0.1232179478, 0.1227027923, 0.1243244335, -0.1197593361, -0.6341339350, -0.1362826675, -0.0902076215, 0.0208458770, -0.0189269055, 0.1431261748, 0.0458523184, -0.0060025044, -0.1280794144, -0.0258549452, 0.1176040024, -0.3258913457, 0.0334468186, 0.0947903916, 0.1627949327, 0.0868995935, 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+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/w10.txt b/model_1/hls4ml_prj/firmware/weights/w10.txt
new file mode 100644
index 0000000..1573865
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w10.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/w12.h b/model_1/hls4ml_prj/firmware/weights/w12.h
new file mode 100644
index 0000000..b630080
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w12.h
@@ -0,0 +1,15 @@
+//Numpy array shape [128, 784]
+//Min -1.362056016922
+//Max 0.854874610901
+//Number of zeros 0
+
+#ifndef W12_H_
+#define W12_H_
+
+#ifndef __SYNTHESIS__
+model_default_t w12[100352];
+#else
+model_default_t w12[100352] = {-0.0211559180, -0.0751956403, -0.0046757502, -0.0582516603, -0.0208510738, -0.0973467305, -0.0774937272, 0.0370164029, -0.1090346947, -0.0243875235, 0.0354884937, -0.0524914265, -0.0534021072, -0.0621392280, 0.0177184287, -0.0726893768, -0.0572052598, -0.0612812378, -0.1029517725, -0.0590488613, 0.0266847238, 0.0283656195, -0.0919918865, -0.0716435909, 0.0400140584, 0.0265420061, -0.0047368770, -0.0578532405, 0.0412015058, -0.0050922586, -0.0517962016, -0.0826299787, -0.0313400142, -0.0667494982, -0.0049951761, -0.0865570381, 0.0322269946, -0.0731189102, -0.0374507308, -0.0686112344, -0.0587324277, -0.0976987183, -0.0387119278, -0.0204042047, -0.0390599929, -0.0791260451, -0.0714068860, -0.0789975524, -0.0151681844, -0.0931244791, -0.1127198860, -0.0148425000, -0.0988237187, 0.0050622397, -0.0529720969, 0.0222948790, 0.0057879183, -0.0740517080, -0.0628459528, 0.0238478743, -0.0239364654, 0.0220813807, -0.1071194336, 0.0061404216, -0.0161033683, 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+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/w12.txt b/model_1/hls4ml_prj/firmware/weights/w12.txt
new file mode 100644
index 0000000..e8a8673
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w12.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/w2.h b/model_1/hls4ml_prj/firmware/weights/w2.h
new file mode 100644
index 0000000..e315436
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w2.h
@@ -0,0 +1,15 @@
+//Numpy array shape [784, 128]
+//Min -0.649703264236
+//Max 0.419865667820
+//Number of zeros 0
+
+#ifndef W2_H_
+#define W2_H_
+
+#ifndef __SYNTHESIS__
+model_default_t w2[100352];
+#else
+model_default_t w2[100352] = {0.0778457448, 0.0326245651, 0.0251421854, -0.0042175874, -0.0392924286, -0.0455851704, -0.0521515831, 0.0319578201, 0.0130669475, 0.0128156617, 0.0676478818, 0.0233890936, 0.0263433680, 0.0467042699, -0.0313544609, -0.0080536082, 0.0693462119, -0.0086449757, -0.0308505818, 0.0668911859, -0.0794955567, -0.0594141632, -0.0352490395, -0.0436870754, 0.0245420039, 0.0603361502, -0.0790811554, -0.0346802250, -0.0743149221, -0.0688885897, -0.0049367025, 0.0049604699, -0.0435718000, 0.0646038577, 0.0615156516, 0.0622761324, 0.0339496061, 0.0456748828, -0.0785900578, -0.0781316459, -0.0066667646, 0.0448279753, 0.0542977080, 0.0250710621, -0.0648595542, -0.0787406042, -0.0096604303, -0.0173240453, -0.0395161733, -0.0563605465, 0.0239392072, 0.0085453019, 0.0076145679, -0.0230627991, 0.0006885007, -0.0486272387, -0.0151953772, -0.0509255156, 0.0150843188, -0.0191338547, -0.0634900331, -0.0256772786, 0.0164806023, 0.0747463182, 0.0187140405, -0.0335130990, 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+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/w2.txt b/model_1/hls4ml_prj/firmware/weights/w2.txt
new file mode 100644
index 0000000..783a4be
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w2.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/w4.h b/model_1/hls4ml_prj/firmware/weights/w4.h
new file mode 100644
index 0000000..b078db0
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w4.h
@@ -0,0 +1,15 @@
+//Numpy array shape [128, 64]
+//Min -1.056936383247
+//Max 0.945414483547
+//Number of zeros 0
+
+#ifndef W4_H_
+#define W4_H_
+
+#ifndef __SYNTHESIS__
+model_default_t w4[8192];
+#else
+model_default_t w4[8192] = {-0.1024347320, 0.1580925733, -0.1195079684, -0.0145012653, -0.0662090331, 0.1286663264, -0.1558848768, 0.0030739189, -0.0730843097, -0.1382862777, -0.0001545500, 0.0976648629, 0.0563901290, 0.1284960955, -0.0091955299, -0.1066015735, 0.0921613127, 0.0419771969, 0.0530826822, 0.1530858278, 0.1852529347, -0.0486647040, 0.0217420999, -0.1373990774, 0.0822225660, 0.0972839817, 0.0720267147, -0.1189460903, 0.0331181549, 0.1072639748, -0.0470880009, 0.0101071522, -0.1276854575, -0.1404817402, 0.0258428417, 0.0913370773, -0.0083628772, -0.1545203030, -0.0632796660, 0.0510900915, 0.0050868695, 0.0097615523, 0.0455809049, -0.0206484571, 0.0474385694, -0.1494552940, 0.1270029396, -0.0900770202, -0.1718640029, -0.0390231982, -0.1556255072, 0.0910637081, -0.1229904443, -0.1581456810, 0.1576830000, 0.0439900681, -0.0001465695, -0.1285269111, 0.0260719471, 0.0253946576, -0.0551439747, 0.0621736385, 0.1063646972, 0.0101971263, -0.1550291032, -0.1649144590, 0.0334843062, 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+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/w4.txt b/model_1/hls4ml_prj/firmware/weights/w4.txt
new file mode 100644
index 0000000..cfecbe1
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w4.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/w6.h b/model_1/hls4ml_prj/firmware/weights/w6.h
new file mode 100644
index 0000000..4309e28
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w6.h
@@ -0,0 +1,15 @@
+//Numpy array shape [64, 2]
+//Min -0.712567985058
+//Max 0.947521388531
+//Number of zeros 0
+
+#ifndef W6_H_
+#define W6_H_
+
+#ifndef __SYNTHESIS__
+model_default_t w6[128];
+#else
+model_default_t w6[128] = {-0.2521221340, -0.0931792706, -0.1506270766, -0.2208057046, -0.2566644549, -0.1013721898, -0.0932383835, 0.2718042433, -0.2737328112, 0.2577145994, -0.1666022837, 0.5829053521, -0.2350935340, -0.3850346506, 0.2557749450, -0.2263706475, 0.3265716732, -0.0570674427, -0.4998079240, -0.0815971866, 0.1932166070, 0.3973506391, -0.4274958670, -0.0737074316, 0.2416843772, -0.1686288118, 0.3753469586, 0.3334763646, -0.3993154466, -0.2972927690, 0.2659172118, -0.4889976382, 0.6368127465, 0.1190705821, 0.2626693249, 0.3339673579, -0.4219647348, 0.2446989566, 0.2715161443, -0.7125679851, 0.2237076759, 0.3520907462, -0.1395215988, -0.1008200720, -0.1082896441, -0.2171834856, 0.0962596983, 0.2804696858, 0.1394276768, -0.2260994315, -0.1438072622, -0.1696370542, 0.1052655503, 0.1246949881, 0.2026538700, -0.0021858432, 0.3508234620, 0.0755241364, -0.2820727527, 0.4151495993, -0.2527797818, 0.9475213885, -0.0347094461, -0.2769165337, 0.2471448630, 0.0981276706, -0.0608540960, -0.1768337935, 0.2333320975, 0.3007498085, -0.1863356233, -0.2031490654, 0.1159987450, 0.0751283914, 0.1587699503, 0.1903635412, -0.2290252745, 0.0243944600, 0.1266293228, -0.2095412910, 0.2278464139, -0.0867382661, 0.3041147590, -0.0527333319, 0.3267803788, 0.2517623305, 0.8703408241, -0.2327734083, -0.0311755110, -0.2270359844, 0.1987231374, -0.1668900102, 0.3694724441, 0.3667044938, 0.0876766890, 0.2547260225, -0.2046753466, -0.2431853563, 0.2669458985, 0.1223935857, 0.1979640424, -0.1946889907, 0.0689499527, -0.2450715601, 0.1095191762, 0.2426410168, 0.3306998610, -0.0720374957, -0.2393945605, -0.1254741102, -0.0638027191, 0.4366956055, 0.0364898778, -0.3437084854, 0.0393819287, 0.5670780540, -0.2690891623, -0.1388636827, -0.6188575625, -0.0583462752, 0.1415957808, 0.2507102787, -0.1431225240, -0.3309644759, 0.2567139864, 0.0308074020, 0.1505190432, 0.2880377471};
+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/w6.txt b/model_1/hls4ml_prj/firmware/weights/w6.txt
new file mode 100644
index 0000000..0613656
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w6.txt
@@ -0,0 +1 @@
+-0.2521221340, -0.0931792706, -0.1506270766, -0.2208057046, -0.2566644549, -0.1013721898, -0.0932383835, 0.2718042433, -0.2737328112, 0.2577145994, -0.1666022837, 0.5829053521, -0.2350935340, -0.3850346506, 0.2557749450, -0.2263706475, 0.3265716732, -0.0570674427, -0.4998079240, -0.0815971866, 0.1932166070, 0.3973506391, -0.4274958670, -0.0737074316, 0.2416843772, -0.1686288118, 0.3753469586, 0.3334763646, -0.3993154466, -0.2972927690, 0.2659172118, -0.4889976382, 0.6368127465, 0.1190705821, 0.2626693249, 0.3339673579, -0.4219647348, 0.2446989566, 0.2715161443, -0.7125679851, 0.2237076759, 0.3520907462, -0.1395215988, -0.1008200720, -0.1082896441, -0.2171834856, 0.0962596983, 0.2804696858, 0.1394276768, -0.2260994315, -0.1438072622, -0.1696370542, 0.1052655503, 0.1246949881, 0.2026538700, -0.0021858432, 0.3508234620, 0.0755241364, -0.2820727527, 0.4151495993, -0.2527797818, 0.9475213885, -0.0347094461, -0.2769165337, 0.2471448630, 0.0981276706, -0.0608540960, -0.1768337935, 0.2333320975, 0.3007498085, -0.1863356233, -0.2031490654, 0.1159987450, 0.0751283914, 0.1587699503, 0.1903635412, -0.2290252745, 0.0243944600, 0.1266293228, -0.2095412910, 0.2278464139, -0.0867382661, 0.3041147590, -0.0527333319, 0.3267803788, 0.2517623305, 0.8703408241, -0.2327734083, -0.0311755110, -0.2270359844, 0.1987231374, -0.1668900102, 0.3694724441, 0.3667044938, 0.0876766890, 0.2547260225, -0.2046753466, -0.2431853563, 0.2669458985, 0.1223935857, 0.1979640424, -0.1946889907, 0.0689499527, -0.2450715601, 0.1095191762, 0.2426410168, 0.3306998610, -0.0720374957, -0.2393945605, -0.1254741102, -0.0638027191, 0.4366956055, 0.0364898778, -0.3437084854, 0.0393819287, 0.5670780540, -0.2690891623, -0.1388636827, -0.6188575625, -0.0583462752, 0.1415957808, 0.2507102787, -0.1431225240, -0.3309644759, 0.2567139864, 0.0308074020, 0.1505190432, 0.2880377471
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/firmware/weights/w8.h b/model_1/hls4ml_prj/firmware/weights/w8.h
new file mode 100644
index 0000000..f9e24ef
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w8.h
@@ -0,0 +1,15 @@
+//Numpy array shape [2, 64]
+//Min -1.085807919502
+//Max 0.464720219374
+//Number of zeros 0
+
+#ifndef W8_H_
+#define W8_H_
+
+#ifndef __SYNTHESIS__
+model_default_t w8[128];
+#else
+model_default_t w8[128] = {0.1783390194, -0.4715767801, 0.2071530968, 0.1981619895, 0.1228899956, 0.1453293860, -0.3742446005, 0.1242215112, -0.2946704030, -0.4366247952, 0.2382424027, 0.0469613858, -0.4455777407, -0.1931826025, 0.1688087881, 0.2821114659, -0.0848135799, 0.2258329242, -0.5976009965, -0.0655483007, -0.2353488505, 0.1210898161, -0.1194427088, -0.3165359497, 0.1777818948, -0.1944278032, -0.2899663150, 0.3041272163, 0.2387445867, -0.0149270296, 0.1959121823, -0.0182853937, 0.1595407575, -0.2767862380, -0.0076378882, 0.0973134041, -0.3644964695, -0.2450073063, 0.1651122868, -0.0770191476, -0.1445089281, -0.0696583092, 0.2166158557, 0.1821525991, -0.2264757752, -0.0597819090, -0.1513944566, -0.0180241633, -0.1358190775, -0.1277131438, -0.1958501339, 0.2546433210, -0.0394751430, -0.2572230101, 0.0638089702, 0.2886827588, -0.1619671434, -0.1843720675, 0.2068926096, 0.2090057433, -0.0078384681, -1.0858079195, -0.1205070168, 0.0094620073, -0.3067599535, 0.1345360726, -0.1685454100, -0.4842284024, 0.3317993879, -0.2561226487, 0.3665867448, -0.6747920513, -0.0446522832, 0.3660125732, 0.4138215482, 0.1090682521, 0.1344663799, 0.3174964786, -0.4181019664, 0.2503911257, -0.0759881437, 0.1135396212, 0.1840300858, -0.1137526929, -0.2747659087, 0.2814601958, 0.2831479311, 0.1998926252, -0.2659226060, -0.0443856716, -0.0897455364, 0.1178034097, -0.1973266602, -0.2512937188, 0.2512212396, -0.2634111047, -0.1304829121, 0.4647202194, -0.0577095747, 0.3429631293, 0.2018115371, -0.1450720876, -0.1409144551, 0.0335462093, -0.1947531551, 0.2730999291, -0.5390598774, 0.0425966904, 0.0586383194, -0.1476947665, -0.1063065380, 0.4539033473, -0.2981294692, 0.2609289289, -0.1228159219, 0.4104956985, -0.0376032293, -0.0247030258, 0.3710552454, -0.2553243637, -0.1599802226, 0.3747279048, -0.3340233564, -0.5207332373, -0.1497970372, 0.0974429101, -0.2417513728, 0.2636135817};
+#endif
+
+#endif
diff --git a/model_1/hls4ml_prj/firmware/weights/w8.txt b/model_1/hls4ml_prj/firmware/weights/w8.txt
new file mode 100644
index 0000000..4d67129
--- /dev/null
+++ b/model_1/hls4ml_prj/firmware/weights/w8.txt
@@ -0,0 +1 @@
+0.1783390194, -0.4715767801, 0.2071530968, 0.1981619895, 0.1228899956, 0.1453293860, -0.3742446005, 0.1242215112, -0.2946704030, -0.4366247952, 0.2382424027, 0.0469613858, -0.4455777407, -0.1931826025, 0.1688087881, 0.2821114659, -0.0848135799, 0.2258329242, -0.5976009965, -0.0655483007, -0.2353488505, 0.1210898161, -0.1194427088, -0.3165359497, 0.1777818948, -0.1944278032, -0.2899663150, 0.3041272163, 0.2387445867, -0.0149270296, 0.1959121823, -0.0182853937, 0.1595407575, -0.2767862380, -0.0076378882, 0.0973134041, -0.3644964695, -0.2450073063, 0.1651122868, -0.0770191476, -0.1445089281, -0.0696583092, 0.2166158557, 0.1821525991, -0.2264757752, -0.0597819090, -0.1513944566, -0.0180241633, -0.1358190775, -0.1277131438, -0.1958501339, 0.2546433210, -0.0394751430, -0.2572230101, 0.0638089702, 0.2886827588, -0.1619671434, -0.1843720675, 0.2068926096, 0.2090057433, -0.0078384681, -1.0858079195, -0.1205070168, 0.0094620073, -0.3067599535, 0.1345360726, -0.1685454100, -0.4842284024, 0.3317993879, -0.2561226487, 0.3665867448, -0.6747920513, -0.0446522832, 0.3660125732, 0.4138215482, 0.1090682521, 0.1344663799, 0.3174964786, -0.4181019664, 0.2503911257, -0.0759881437, 0.1135396212, 0.1840300858, -0.1137526929, -0.2747659087, 0.2814601958, 0.2831479311, 0.1998926252, -0.2659226060, -0.0443856716, -0.0897455364, 0.1178034097, -0.1973266602, -0.2512937188, 0.2512212396, -0.2634111047, -0.1304829121, 0.4647202194, -0.0577095747, 0.3429631293, 0.2018115371, -0.1450720876, -0.1409144551, 0.0335462093, -0.1947531551, 0.2730999291, -0.5390598774, 0.0425966904, 0.0586383194, -0.1476947665, -0.1063065380, 0.4539033473, -0.2981294692, 0.2609289289, -0.1228159219, 0.4104956985, -0.0376032293, -0.0247030258, 0.3710552454, -0.2553243637, -0.1599802226, 0.3747279048, -0.3340233564, -0.5207332373, -0.1497970372, 0.0974429101, -0.2417513728, 0.2636135817
\ No newline at end of file
diff --git a/model_1/hls4ml_prj/hls4ml_config.yml b/model_1/hls4ml_prj/hls4ml_config.yml
new file mode 100644
index 0000000..12b5e58
--- /dev/null
+++ b/model_1/hls4ml_prj/hls4ml_config.yml
@@ -0,0 +1,16 @@
+Backend: Vivado
+Board: null
+ClockPeriod: 5
+HLSConfig:
+  Model:
+    Precision: ap_fixed<16,6>
+    ReuseFactor: 1
+    Strategy: Latency
+IOType: io_parallel
+InputData: null
+KerasModel: !keras_model 'model_1/hls4ml_prj/keras_model.h5'
+OutputDir: model_1/hls4ml_prj
+OutputPredictions: null
+ProjectName: myproject
+Stamp: 3D7f2580
+XilinxPart: xcu250-figd2104-2L-e
diff --git a/model_1/hls4ml_prj/keras_model.h5 b/model_1/hls4ml_prj/keras_model.h5
new file mode 100644
index 0000000..9485c6a
Binary files /dev/null and b/model_1/hls4ml_prj/keras_model.h5 differ
diff --git a/model_1/hls4ml_prj/myproject_bridge.cpp b/model_1/hls4ml_prj/myproject_bridge.cpp
new file mode 100644
index 0000000..f801d2d
--- /dev/null
+++ b/model_1/hls4ml_prj/myproject_bridge.cpp
@@ -0,0 +1,87 @@
+#ifndef MYPROJECT_BRIDGE_H_
+#define MYPROJECT_BRIDGE_H_
+
+#include "firmware/myproject.h"
+#include "firmware/nnet_utils/nnet_helpers.h"
+#include <algorithm>
+#include <map>
+
+//hls-fpga-machine-learning insert bram
+
+
+namespace nnet {
+    bool trace_enabled = false;
+    std::map<std::string, void *> *trace_outputs = NULL;
+    size_t trace_type_size = sizeof(double);
+}
+
+extern "C" {
+
+struct trace_data {
+    const char *name;
+    void *data;
+};
+
+void allocate_trace_storage(size_t element_size) {
+    nnet::trace_enabled = true;
+    nnet::trace_outputs = new std::map<std::string, void *>;
+    nnet::trace_type_size = element_size;
+}
+
+void free_trace_storage() {
+    for (std::map<std::string, void *>::iterator i = nnet::trace_outputs->begin(); i != nnet::trace_outputs->end(); i++) {
+        void *ptr = i->second;
+        free(ptr);
+    }
+    nnet::trace_outputs->clear();
+    delete nnet::trace_outputs;
+    nnet::trace_outputs = NULL;
+    nnet::trace_enabled = false;
+}
+
+void collect_trace_output(struct trace_data *c_trace_outputs) {
+    int ii = 0;
+    for (std::map<std::string, void *>::iterator i = nnet::trace_outputs->begin(); i != nnet::trace_outputs->end(); i++) {
+        c_trace_outputs[ii].name = i->first.c_str();
+        c_trace_outputs[ii].data = i->second;
+        ii++;
+    }
+}
+
+// Wrapper of top level function for Python bridge
+void myproject_float(
+    float encoder_input[N_INPUT_1_1],
+    float layer13_out[N_LAYER_12],
+    unsigned short &const_size_in_1,
+    unsigned short &const_size_out_1
+) {
+    
+    input_t encoder_input_ap[N_INPUT_1_1];
+    nnet::convert_data<float, input_t, N_INPUT_1_1>(encoder_input, encoder_input_ap);
+
+    result_t layer13_out_ap[N_LAYER_12];
+
+    myproject(encoder_input_ap,layer13_out_ap,const_size_in_1,const_size_out_1);
+
+    nnet::convert_data<result_t, float, N_LAYER_12>(layer13_out_ap, layer13_out);
+}
+
+void myproject_double(
+    double encoder_input[N_INPUT_1_1],
+    double layer13_out[N_LAYER_12],
+    unsigned short &const_size_in_1,
+    unsigned short &const_size_out_1
+) {
+    input_t encoder_input_ap[N_INPUT_1_1];
+    nnet::convert_data<double, input_t, N_INPUT_1_1>(encoder_input, encoder_input_ap);
+
+    result_t layer13_out_ap[N_LAYER_12];
+
+    myproject(encoder_input_ap,layer13_out_ap,const_size_in_1,const_size_out_1);
+
+    nnet::convert_data<result_t, double, N_LAYER_12>(layer13_out_ap, layer13_out);
+}
+
+}
+
+#endif
diff --git a/model_1/hls4ml_prj/myproject_test.cpp b/model_1/hls4ml_prj/myproject_test.cpp
new file mode 100644
index 0000000..15ec41e
--- /dev/null
+++ b/model_1/hls4ml_prj/myproject_test.cpp
@@ -0,0 +1,130 @@
+//
+//    rfnoc-hls-neuralnet: Vivado HLS code for neural-net building blocks
+//
+//    Copyright (C) 2017 EJ Kreinar
+//
+//    This program is free software: you can redistribute it and/or modify
+//    it under the terms of the GNU General Public License as published by
+//    the Free Software Foundation, either version 3 of the License, or
+//    (at your option) any later version.
+//
+//    This program is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+//    GNU General Public License for more details.
+//
+//    You should have received a copy of the GNU General Public License
+//    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+//
+#include <fstream>
+#include <iostream>
+#include <algorithm>
+#include <vector>
+#include <map>
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+
+#include "firmware/myproject.h"
+#include "firmware/nnet_utils/nnet_helpers.h"
+
+//hls-fpga-machine-learning insert bram
+
+#define CHECKPOINT 5000
+
+namespace nnet {
+    bool trace_enabled = true;
+    std::map<std::string, void *> *trace_outputs = NULL;
+    size_t trace_type_size = sizeof(double);
+}
+
+int main(int argc, char **argv)
+{
+  //load input data from text file
+  std::ifstream fin("tb_data/tb_input_features.dat");
+  //load predictions from text file
+  std::ifstream fpr("tb_data/tb_output_predictions.dat");
+
+#ifdef RTL_SIM
+  std::string RESULTS_LOG = "tb_data/rtl_cosim_results.log";
+#else
+  std::string RESULTS_LOG = "tb_data/csim_results.log";
+#endif
+  std::ofstream fout(RESULTS_LOG);
+
+  std::string iline;
+  std::string pline;
+  int e = 0;
+
+  if (fin.is_open() && fpr.is_open()) {
+    while ( std::getline(fin,iline) && std::getline (fpr,pline) ) {
+      if (e % CHECKPOINT == 0) std::cout << "Processing input " << e << std::endl;
+      char* cstr=const_cast<char*>(iline.c_str());
+      char* current;
+      std::vector<float> in;
+      current=strtok(cstr," ");
+      while(current!=NULL) {
+        in.push_back(atof(current));
+        current=strtok(NULL," ");
+      }
+      cstr=const_cast<char*>(pline.c_str());
+      std::vector<float> pr;
+      current=strtok(cstr," ");
+      while(current!=NULL) {
+        pr.push_back(atof(current));
+        current=strtok(NULL," ");
+      }
+
+      //hls-fpga-machine-learning insert data
+      input_t encoder_input[N_INPUT_1_1];
+      nnet::copy_data<float, input_t, 0, N_INPUT_1_1>(in, encoder_input);
+      result_t layer13_out[N_LAYER_12];
+
+      //hls-fpga-machine-learning insert top-level-function
+      unsigned short size_in1,size_out1;
+      myproject(encoder_input,layer13_out,size_in1,size_out1);
+
+      if (e % CHECKPOINT == 0) {
+        std::cout << "Predictions" << std::endl;
+        //hls-fpga-machine-learning insert predictions
+        for(int i = 0; i < N_LAYER_12; i++) {
+          std::cout << pr[i] << " ";
+        }
+        std::cout << std::endl;
+        std::cout << "Quantized predictions" << std::endl;
+        //hls-fpga-machine-learning insert quantized
+        nnet::print_result<result_t, N_LAYER_12>(layer13_out, std::cout, true);
+      }
+      e++;
+
+      //hls-fpga-machine-learning insert tb-output
+      nnet::print_result<result_t, N_LAYER_12>(layer13_out, fout);
+
+    }
+    fin.close();
+    fpr.close();
+  } else {
+    std::cout << "INFO: Unable to open input/predictions file, using default input." << std::endl;
+
+    //hls-fpga-machine-learning insert zero
+    input_t encoder_input[N_INPUT_1_1];
+    nnet::fill_zero<input_t, N_INPUT_1_1>(encoder_input);
+    result_t layer13_out[N_LAYER_12];
+
+    //hls-fpga-machine-learning insert top-level-function
+    unsigned short size_in1,size_out1;
+    myproject(encoder_input,layer13_out,size_in1,size_out1);
+
+    //hls-fpga-machine-learning insert output
+    nnet::print_result<result_t, N_LAYER_12>(layer13_out, std::cout, true);
+
+    //hls-fpga-machine-learning insert tb-output
+    nnet::print_result<result_t, N_LAYER_12>(layer13_out, fout);
+
+  }
+
+  fout.close();
+  std::cout << "INFO: Saved inference results to file: " << RESULTS_LOG << std::endl;
+
+  return 0;
+}
diff --git a/model_1/hls4ml_prj/vivado_synth.tcl b/model_1/hls4ml_prj/vivado_synth.tcl
new file mode 100644
index 0000000..920c9a4
--- /dev/null
+++ b/model_1/hls4ml_prj/vivado_synth.tcl
@@ -0,0 +1,3 @@
+add_files myproject_prj/solution1/syn/vhdl
+synth_design -top myproject -part xcu250-figd2104-2L-e
+report_utilization -file vivado_synth.rpt
\ No newline at end of file