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
- 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.
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.