commit | 0090f5b838de262a47e8d6b708eb8f74495508ca | [log] [tgz] |
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author | Frank Barchard <fbarchard@google.com> | Mon Dec 16 17:02:57 2019 -0800 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Mon Dec 16 17:03:33 2019 -0800 |
tree | a40ace751f715254353bca6b3f884b2a01f92474 | |
parent | 5cc1cc2d76ab44ba5b99c78001537d7764e4170b [diff] |
4x8 FMA sorted by B to match load order On Pixel 3 Was f32_gemm_4x8__aarch32_neon_cortex_a75/mobilenet_v2/real_time 42246 us 41699 us 17 Freq=2.8032G Now f32_gemm_4x8__aarch32_neon_cortex_a75/mobilenet_v2/real_time 42024 us 41438 us 17 Freq=2.8032G PiperOrigin-RevId: 285881661
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.
XNNPACK implements the following neural network operators:
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.
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.
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.
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.