[DOCS] ARM CPU plugin docs #10885
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| # Arm® CPU device {#openvino_docs_OV_UG_supported_plugins_ARM_CPU} | ||
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| ## Introducing the Arm® CPU Plugin | ||
| The ARM® CPU plugin is developed in order to enable deep neural networks inference on Arm® CPU, using [Compute Library](https://github.com/ARM-software/ComputeLibrary) as a backend. | ||
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| The Arm® CPU plugin is not a part of the Intel® Distribution of OpenVINO™ toolkit and is not distributed in pre-built form. To use the plugin, it should be buid from source code. Plugin build procedure is described on [How to build Arm® CPU plugin](https://github.com/openvinotoolkit/openvino_contrib/wiki/How-to-build-ARM-CPU-plugin). | ||
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| The set of supported layers is defined on [Operation set specification](https://github.com/openvinotoolkit/openvino_contrib/wiki/ARM-plugin-operation-set-specification). | ||
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| ## Supported inference data types | ||
| The Arm® CPU plugin supports the following data types as inference precision of internal primitives: | ||
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| - Floating-point data types: | ||
| - f32 | ||
| - f16 | ||
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There was a problem hiding this comment. I set f32 everywhere |
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| - Quantized data types: | ||
| - i8 | ||
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| > **NOTE**: i8 support is experimental. | ||
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| [Hello Query Device C++ Sample](../../../samples/cpp/hello_query_device/README.md) can be used to print out supported data types for all detected devices. | ||
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| ## Supported properties | ||
| The plugin supports the properties listed below. | ||
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| ### Read-write properties | ||
| All parameters must be set before calling `ov::Core::compile_model()` in order to take effect or passed as additional argument to `ov::Core::compile_model()` | ||
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| - ov::enable_profiling | ||
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There was a problem hiding this comment. I suppose you also support streams, pinning, etc There was a problem hiding this comment. Thus these options are in the supported list they do not provide expected threading control. We even test only in latency (sync) mode only. I am going to enable tbb threading backend and provide full threading control as in the CPU plugin. It won't give any valuable throughput gain but give full threading control. So we can claim support of these options. |
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| ### Read-only properties | ||
| - ov::supported_properties | ||
| - ov::available_devices | ||
| - ov::range_for_async_infer_requests | ||
| - ov::range_for_streams | ||
| - ov::device::full_name | ||
| - ov::device::capabilities | ||
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| ## Known Layers Limitation | ||
| * `AvgPool` layer is supported via arm_compute library for 4D input tensor and via reference implementation for another cases. | ||
| * `BatchToSpace` layer is supported 4D tensors only and constant nodes: `block_shape` with `N` = 1 and `C`= 1, `crops_begin` with zero values and `crops_end` with zero values. | ||
| * `Convert` layer is supported via arm_compute library with configuration: src data type -> dst data type | ||
| - U8 -> U16, S16, S32 | ||
| - U16 -> U8, U32 | ||
| - S16 -> U8, S32 | ||
| - F16 -> F32 | ||
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| for another cases layer is implemented via runtime reference. | ||
| * `ConvertLike` layer is supported configuration like `Convert`. | ||
| * `DepthToSpace` layer is supported 4D tensors only and for `BLOCKS_FIRST` of `mode` attribute. | ||
| * `Equal` does not support `broadcast` for inputs. | ||
| * `Gather` layer is supported constant scalar or 1D indices axes only. Layer is supported as via arm_compute library for non negative indices and via reference implementation otherwise. | ||
| * `Interpolate` layer is supported 4D tensors and zero value of `pads_begin` and `pads_end` attributes only. | ||
| * `Less` does not support `broadcast` for inputs. | ||
| * `LessEqual` does not support `broadcast` for inputs. | ||
| * `LRN` layer is supported `axes = {1}` or `axes = {2, 3}` only. | ||
| * `MaxPool-1` layer is supported via arm_compute library for 4D input tensor and via reference implementation for another cases. | ||
| * `Mod` layer is supported for FP32 only. | ||
| * `MVN` layer is supported via arm_compute library for 2D inputs and `false` value of `normalize_variance` and `false` value of `across_channels`, for another cases layer is implemented via runtime reference. | ||
| * `Normalize` layer is supported via arm_compute library with `MAX` value of `eps_mode` and `axes = {2 | 3}`, and for `ADD` value of `eps_mode` layer uses `'DecomposeNormalizeL2Add'`, for another cases layer is implemented via runtime reference. | ||
| * `NotEqual` does not support `broadcast` for inputs. | ||
| * `Pad` layer works with `pad_mode = {REFLECT | CONSTANT | SYMMETRIC}` parameters only. | ||
| * `Round` layer is supported via arm_compute library with `RoundMode::HALF_AWAY_FROM_ZERO` value of `mode`, for another cases layer is implemented via runtime reference. | ||
| * `SpaceToBatch` layer is supported 4D tensors only and constant nodes: `shapes`, `pads_begin` or `pads_end` with zero paddings for batch or channels and one values `shapes` for batch and channels. | ||
| * `SpaceToDepth` layer is supported 4D tensors only and for `BLOCKS_FIRST` of `mode` attribute. | ||
| * `StridedSlice` layer is supported via arm_compute library for tensors with dims < 5 and zero values of `ellipsis_mask` or zero values of `new_axis_mask` and `shrink_axis_mask`, for another cases layer is implemented via runtime reference. | ||
| * `Transpose` layer is supported via arm_compute library for tensors with dims < 5, for another cases layer is implemented via runtime reference. | ||
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| ## See Also | ||
| * [How to run YOLOv4 model inference using OpenVINO™ and OpenCV on Arm®](https://opencv.org/how-to-run-yolov4-using-openvino-and-opencv-on-arm/) | ||
| * [Face recognition on Android™ using OpenVINO™ toolkit with Arm® plugin](https://opencv.org/face-recognition-on-android-using-openvino-toolkit-with-arm-plugin/) | ||
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should be built
described on ... page ?