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https://github.com/google/XNNPACK

High-efficiency floating-point neural network inference operators for mobile, server, and Web
https://github.com/google/XNNPACK

convolutional-neural-network convolutional-neural-networks cpu inference inference-optimization matrix-multiplication mobile-inference multithreading neural-network neural-networks simd

Last synced: 2 months ago
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High-efficiency floating-point neural network inference operators for mobile, server, and Web

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# 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.