google / XNNPACK

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, TensorFlow.js, PyTorch, ONNX Runtime, ExecuTorch, and MediaPipe. 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 (experimental) • RISC-V (RV32GC and RV64GC) • Hexagon (with HVX) 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 on an Android/ARM64 build with Android NDK r21 ( ) 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 on a Raspbian Buster build with CMake ( ) and neural network models with randomized weights and inputs. INT8 inference was evaluated on per-channel quantization schema. Minimum build requirements • C11 • C++17 • Python 3 Publications • Marat Dukhan "The Indirect Convolution Algorithm". Presented on Efficient Deep Learning for Compute Vision (ECV) 2019 workshop (slides, paper on ArXiv). • Erich Elsen, Marat Dukhan, Trevor Gale, Karen Simonyan "Fast Sparse ConvNets". Paper on ArXiv, pre-trained sparse models. • Marat Dukhan, Artsiom Ablavatski "The Two-Pass Softmax Algorithm". Paper on ArXiv. • Yury Pisarchyk, Juhyun Lee "Efficient Memory Management for Deep Neural Net Inference". Paper on ArXiv. Ecosystem Machine Learning Frameworks • TensorFlow Lite. • TensorFlow.js WebAssembly backend. • PyTorch Mobile. • ONNX Runtime Mobile • MediaPipe for the Web. • Alibaba HALO (Heterogeneity-Aware Lowering and Optimization) • Samsung ONE (On-device Neural Engine) Acknowledgements XNNPACK is based on QNNPACK library. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.