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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, macOS, and iOS (including WatchOS and tvOS)
• 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
• RISC-V (RV32GV 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
• 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.

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.
• 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.