XNNPACK

by google

google / XNNPACK

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

593 Stars 77 Forks Last release: Not found Other 981 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

XNNPACK

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 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, and MediaPipe.

Supported Architectures

  • ARM64 on Android, Linux, and iOS (including WatchOS and tvOS)
  • ARMv7 (with NEON) on Android, Linux, and iOS (including WatchOS)
  • x86 and x86-64 (up to AVX512) on Windows, Linux, macOS, Android, and iOS simulator
  • WebAssembly MVP
  • WebAssembly SIMD (experimental)

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)
  • 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)
  • Copy
  • Floor (rounding to integer below)
  • HardSwish
  • Leaky ReLU
  • Negate
  • Sigmoid
  • Softmax
  • Square
  • Truncation (rounding to integer towards zero)
  • 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 three generations of MobileNet models and three generations of Pixel phones.

| Model | Pixel, ms | Pixel 2, ms | Pixel 3a, ms | | ------------------ | :-------: | :---------: | :----------: | | MobileNet v1 1.0X | 82 | 86 | 88 | | MobileNet v2 1.0X | 49 | 53 | 55 | | MobileNet v3 Large | 39 | 42 | 44 | | 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 | | ------------------ | :-------: | :---------: | :----------: | | MobileNet v1 1.0X | 43 | 27 | 46 | | MobileNet v2 1.0X | 26 | 18 | 28 | | MobileNet v3 Large | 22 | 16 | 24 | | 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 | | ------------------ | :----------------------: | :-----------------: | :--------------------: | :-----------------: | | MobileNet v1 1.0X | 4004 | 337 | 116 | 72 | | MobileNet v2 1.0X | 2011 | 195 | 83 | 41 | | MobileNet v3 Large | 1694 | 163 | 70 | 38 | | MobileNet v3 Small | 482 | 52 | 23 | 13 |

Benchmarked on May 22, 2020 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

Ecosystem

Machine Learning Frameworks

Acknowledgements

XNNPACK is a based on QNNPACK library. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.