/XNNPACK

High-efficiency floating-point neural network inference operators for mobile, server, and Web

Primary LanguageC++OtherNOASSERTION

XNNPACK

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 (SSE2 level) 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 MediaPipe, TensorFlow Lite, and TensorFlow.js.

Supported Architectures

  • ARM64 on Android and Linux
  • ARMv7 (with NEON) on Android and Linux
  • WebAssembly MVP
  • WebAssembly SIMD (experimental)
  • x86 and x86-64 (up to AVX512) on Android, Linux, and macOS

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
  • Add (including broadcasting, two inputs only)
  • Subtract (including broadcasting)
  • Divide (including broadcasting)
  • Maximum (including broadcasting)
  • Minimum (including broadcasting)
  • Multiply (including broadcasting)
  • Global Average Pooling
  • Channel Shuffle
  • Fully Connected
  • Clamp (includes ReLU and ReLU6)
  • HardSwish
  • PReLU

All operators in XNNPACK support NHWC layout, but additionally allow custom stride along the Channel 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 two generations of MobileNet models and three generations of Pixel phones.

Model Pixel, ms Pixel 2, ms Pixel 3a, ms
MobileNet v1 1.0X 81 93 88
MobileNet v2 1.0X 48 58 54

Benchmarked on October 9, 2019 with end2end_bench --benchmark_min_time=5 on an Android/ARM64 build (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 2 (BCM2836), ms RPi 3+ (BCM2837B0), ms RPi 4 (BCM2711), ms
MobileNet v1 1.0X 342 122 79
MobileNet v2 1.0X 199 82 47
MobileNet v3 Large 166 71 42
MobileNet v3 Small 53 24 15

Benchmarked on December 12, 2019 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.

Publications

Acknowledgements

XNNPACK is a based on QNNPACK library. Unlike QNNPACK, XNNPACK focuses entirely on floating-point operators, and its API is no longer compatible with QNNPACK.