| # XNNPACK | |
| XNNPACK is a highly optimized solution for neural network inference on ARM, x86, WebAssembly, and RISC-V platforms. 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). | |
| ## Supported Architectures | |
| - ARM64 on Android, iOS, macOS, Linux, and Windows | |
| - ARMv7 (with NEON) on Android | |
| - ARMv6 (with VFPv2) on Linux | |
| - x86 and x86-64 (up to AVX512) on Windows, Linux, macOS, Android, and iOS simulator | |
| - WebAssembly MVP | |
| - WebAssembly SIMD | |
| - [WebAssembly Relaxed SIMD](https://github.com/WebAssembly/relaxed-simd) (experimental) | |
| - RISC-V (RV32GC and RV64GC) | |
| ## Operator Coverage | |
| XNNPACK implements the following neural network operators: | |
| - 2D Convolution (including grouped and depthwise) | |
| - 2D Deconvolution (AKA Transposed Convolution) | |
| - 2D Average Pooling | |
| - 2D Max Pooling | |
| - 2D ArgMax Pooling (Max Pooling + indices) | |
| - 2D Unpooling | |
| - 2D Bilinear Resize | |
| - 2D Depth-to-Space (AKA Pixel Shuffle) | |
| - Add (including broadcasting, two inputs only) | |
| - Subtract (including broadcasting) | |
| - Divide (including broadcasting) | |
| - Maximum (including broadcasting) | |
| - Minimum (including broadcasting) | |
| - Multiply (including broadcasting) | |
| - Squared Difference (including broadcasting) | |
| - Global Average Pooling | |
| - Channel Shuffle | |
| - Fully Connected | |
| - Abs (absolute value) | |
| - Bankers' Rounding (rounding to nearest, ties to even) | |
| - Ceiling (rounding to integer above) | |
| - Clamp (includes ReLU and ReLU6) | |
| - Convert (includes fixed-point and half-precision quantization and | |
| dequantization) | |
| - Copy | |
| - ELU | |
| - Floor (rounding to integer below) | |
| - HardSwish | |
| - Leaky ReLU | |
| - Negate | |
| - Sigmoid | |
| - Softmax | |
| - Square | |
| - Tanh | |
| - Transpose | |
| - Truncation (rounding to integer towards zero) | |
| - PReLU | |
| All 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. | |
| ## Performance | |
| ### Mobile phones | |
| The table below presents **single-threaded** performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones. | |
| | Model | Pixel, ms | Pixel 2, ms | Pixel 3a, ms | | |
| | ----------------------- | :-------: | :---------: | :----------: | | |
| | FP32 MobileNet v1 1.0X | 82 | 86 | 88 | | |
| | FP32 MobileNet v2 1.0X | 49 | 53 | 55 | | |
| | FP32 MobileNet v3 Large | 39 | 42 | 44 | | |
| | FP32 MobileNet v3 Small | 12 | 14 | 14 | | |
| The 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. | |
| | Model | Pixel, ms | Pixel 2, ms | Pixel 3a, ms | | |
| | ----------------------- | :-------: | :---------: | :----------: | | |
| | FP32 MobileNet v1 1.0X | 43 | 27 | 46 | | |
| | FP32 MobileNet v2 1.0X | 26 | 18 | 28 | | |
| | FP32 MobileNet v3 Large | 22 | 16 | 24 | | |
| | FP32 MobileNet v3 Small | 7 | 6 | 8 | | |
| Benchmarked 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. | |
| ### Raspberry Pi | |
| The table below presents **multi-threaded** performance of XNNPACK library on three generations of MobileNet models and three generations of Raspberry Pi boards. | |
| | Model | RPi Zero W (BCM2835), ms | RPi 2 (BCM2836), ms | RPi 3+ (BCM2837B0), ms | RPi 4 (BCM2711), ms | RPi 4 (BCM2711, ARM64), ms | | |
| | ----------------------- | :----------------------: | :-----------------: | :--------------------: | :-----------------: | :------------------------: | | |
| | FP32 MobileNet v1 1.0X | 3919 | 302 | 114 | 72 | 77 | | |
| | FP32 MobileNet v2 1.0X | 1987 | 191 | 79 | 41 | 46 | | |
| | FP32 MobileNet v3 Large | 1658 | 161 | 67 | 38 | 40 | | |
| | FP32 MobileNet v3 Small | 474 | 50 | 22 | 13 | 15 | | |
| | INT8 MobileNet v1 1.0X | 2589 | 128 | 46 | 29 | 24 | | |
| | INT8 MobileNet v2 1.0X | 1495 | 82 | 30 | 20 | 17 | | |
| Benchmarked 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. | |
| ## Minimum build requirements | |
| - C11 | |
| - C++14 | |
| - Python 3 | |
| ## Publications | |
| - 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)). | |
| - Erich Elsen, Marat Dukhan, Trevor Gale, Karen Simonyan "Fast Sparse ConvNets". | |
| [Paper on ArXiv](https://arxiv.org/abs/1911.09723), [pre-trained sparse | |
| models](https://github.com/google-research/google-research/tree/master/fastconvnets). | |
| - Marat Dukhan, Artsiom Ablavatski "The Two-Pass Softmax Algorithm". | |
| [Paper on ArXiv](https://arxiv.org/abs/2001.04438). | |
| - Yury Pisarchyk, Juhyun Lee "Efficient Memory Management for Deep Neural Net Inference". | |
| [Paper on ArXiv](https://arxiv.org/abs/2001.03288). | |
| ## Ecosystem | |
| ### Machine Learning Frameworks | |
| - [TensorFlow Lite](https://blog.tensorflow.org/2020/07/accelerating-tensorflow-lite-xnnpack-integration.html). | |
| - [TensorFlow.js WebAssembly backend](https://blog.tensorflow.org/2020/03/introducing-webassembly-backend-for-tensorflow-js.html). | |
| - [PyTorch Mobile](https://pytorch.org/mobile). | |
| - [ONNX Runtime Mobile](https://onnxruntime.ai/docs/execution-providers/Xnnpack-ExecutionProvider.html) | |
| - [MediaPipe for the Web](https://developers.googleblog.com/2020/01/mediapipe-on-web.html). | |
| - [Alibaba HALO (Heterogeneity-Aware Lowering and Optimization)](https://github.com/alibaba/heterogeneity-aware-lowering-and-optimization) | |
| - [Samsung ONE (On-device Neural Engine)](https://github.com/Samsung/ONE) | |
| ## Acknowledgements | |
| XNNPACK 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. | |