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XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as [TensorFlow Lite](https://www.tensorflow.org/lite), [TensorFlow.js](https://www.tensorflow.org/js), [PyTorch](https://pytorch.org/), [ONNX Runtime](https://onnxruntime.ai), and [MediaPipe](https://mediapipe.dev).\n\n## Supported Architectures\n\n- ARM64 on Android, iOS, macOS, Linux, and Windows\n- ARMv7 (with NEON) on Android\n- ARMv6 (with VFPv2) on Linux\n- x86 and x86-64 (up to AVX512) on Windows, Linux, macOS, Android, and iOS simulator\n- WebAssembly MVP\n- WebAssembly SIMD\n- [WebAssembly Relaxed SIMD](https://github.com/WebAssembly/relaxed-simd) (experimental)\n- RISC-V (RV32GC and RV64GC)\n\n## Operator Coverage\n\nXNNPACK implements the following neural network operators:\n\n- 2D Convolution (including grouped and depthwise)\n- 2D Deconvolution (AKA Transposed Convolution)\n- 2D Average Pooling\n- 2D Max Pooling\n- 2D ArgMax Pooling (Max Pooling + indices)\n- 2D Unpooling\n- 2D Bilinear Resize\n- 2D Depth-to-Space (AKA Pixel Shuffle)\n- Add (including broadcasting, two inputs only)\n- Subtract (including broadcasting)\n- Divide (including broadcasting)\n- Maximum (including broadcasting)\n- Minimum (including broadcasting)\n- Multiply (including broadcasting)\n- Squared Difference (including broadcasting)\n- Global Average Pooling\n- Channel Shuffle\n- Fully Connected\n- Abs (absolute value)\n- Bankers' Rounding (rounding to nearest, ties to even)\n- Ceiling (rounding to integer above)\n- Clamp (includes ReLU and ReLU6)\n- Convert (includes fixed-point and half-precision quantization and\n  dequantization)\n- Copy\n- ELU\n- Floor (rounding to integer below)\n- HardSwish\n- Leaky ReLU\n- Negate\n- Sigmoid\n- Softmax\n- Square\n- Tanh\n- Transpose\n- Truncation (rounding to integer towards zero)\n- PReLU\n\nAll operators in XNNPACK support NHWC layout, but additionally allow custom stride along the **C**hannel dimension. Thus, operators can consume a subset of channels in the input tensor, and produce a subset of channels in the output tensor, providing a zero-cost Channel Split and Channel Concatenation operations.\n\n## Performance\n\n### Mobile phones\n\nThe table below presents **single-threaded** performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.\n\n| Model                   | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |\n| ----------------------- | :-------: | :---------: | :----------: |\n| FP32 MobileNet v1 1.0X  |    82     |      86     |      88      |\n| FP32 MobileNet v2 1.0X  |    49     |      53     |      55      |\n| FP32 MobileNet v3 Large |    39     |      42     |      44      |\n| FP32 MobileNet v3 Small |    12     |      14     |      14      |\n\nThe following table presents **multi-threaded** (using as many threads as there are big cores) performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.\n\n| Model                   | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |\n| ----------------------- | :-------: | :---------: | :----------: |\n| FP32 MobileNet v1 1.0X  |    43     |      27     |      46      |\n| FP32 MobileNet v2 1.0X  |    26     |      18     |      28      |\n| FP32 MobileNet v3 Large |    22     |      16     |      24      |\n| FP32 MobileNet v3 Small |     7     |       6     |       8      |\n\nBenchmarked on March 27, 2020 with `end2end_bench --benchmark_min_time=5` on an Android/ARM64 build with Android NDK r21 (`bazel build -c opt --config android_arm64 :end2end_bench`) and neural network models with randomized weights and inputs.\n\n### Raspberry Pi\n\nThe table below presents **multi-threaded** performance of XNNPACK library on three generations of MobileNet models and three generations of Raspberry Pi boards.\n\n| Model                   | RPi Zero W (BCM2835), ms | RPi 2 (BCM2836), ms | RPi 3+ (BCM2837B0), ms | RPi 4 (BCM2711), ms | RPi 4 (BCM2711, ARM64), ms |\n| ----------------------- | :----------------------: | :-----------------: | :--------------------: | :-----------------: | :------------------------: |\n| FP32 MobileNet v1 1.0X  |          3919            |         302         |          114           |          72         |             77             |\n| FP32 MobileNet v2 1.0X  |          1987            |         191         |           79           |          41         |             46             |\n| FP32 MobileNet v3 Large |          1658            |         161         |           67           |          38         |             40             |\n| FP32 MobileNet v3 Small |           474            |          50         |           22           |          13         |             15             |\n| INT8 MobileNet v1 1.0X  |          2589            |         128         |           46           |          29         |             24             |\n| INT8 MobileNet v2 1.0X  |          1495            |          82         |           30           |          20         |             17             |\n\nBenchmarked on Feb 8, 2022 with `end2end-bench --benchmark_min_time=5` on a Raspbian Buster build with CMake (`./scripts/build-local.sh`) and neural network models with randomized weights and inputs. INT8 inference was evaluated on per-channel quantization schema.\n\n## Minimum build requirements\n\n- C11\n- C++14\n- Python 3\n\n## Publications\n\n- Marat Dukhan \"The Indirect Convolution Algorithm\". Presented on [Efficient Deep Learning for Compute Vision (ECV) 2019](https://sites.google.com/corp/view/ecv2019/) workshop ([slides](https://drive.google.com/file/d/1ZayB3By5ZxxQIRtN7UDq_JvPg1IYd3Ac/view), [paper on ArXiv](https://arxiv.org/abs/1907.02129)).\n- Erich Elsen, Marat Dukhan, Trevor Gale, Karen Simonyan \"Fast Sparse ConvNets\".\n  [Paper on ArXiv](https://arxiv.org/abs/1911.09723), [pre-trained sparse\n  models](https://github.com/google-research/google-research/tree/master/fastconvnets).\n- Marat Dukhan, Artsiom Ablavatski \"The Two-Pass Softmax Algorithm\".\n  [Paper on ArXiv](https://arxiv.org/abs/2001.04438).\n- Yury Pisarchyk, Juhyun Lee \"Efficient Memory Management for Deep Neural Net Inference\".\n  [Paper on ArXiv](https://arxiv.org/abs/2001.03288).\n\n## Ecosystem\n\n### Machine Learning Frameworks\n\n- [TensorFlow Lite](https://blog.tensorflow.org/2020/07/accelerating-tensorflow-lite-xnnpack-integration.html).\n- [TensorFlow.js WebAssembly backend](https://blog.tensorflow.org/2020/03/introducing-webassembly-backend-for-tensorflow-js.html).\n- [PyTorch Mobile](https://pytorch.org/mobile).\n- [ONNX Runtime Mobile](https://onnxruntime.ai/docs/execution-providers/Xnnpack-ExecutionProvider.html)\n- [MediaPipe for the Web](https://developers.googleblog.com/2020/01/mediapipe-on-web.html).\n- [Alibaba HALO (Heterogeneity-Aware Lowering and Optimization)](https://github.com/alibaba/heterogeneity-aware-lowering-and-optimization)\n- [Samsung ONE (On-device Neural Engine)](https://github.com/Samsung/ONE)\n\n## Acknowledgements\n\nXNNPACK is a based on [QNNPACK](https://github.com/pytorch/QNNPACK) library. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.\n","funding_links":[],"categories":["Tools"],"sub_categories":["Libraries"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle%2Fxnnpack","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgoogle%2Fxnnpack","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle%2Fxnnpack/lists"}