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