commit | b1cd3811959d9930a586b623816a8a63396f7a51 | [log] [tgz] |
---|---|---|
author | Frank Barchard <fbarchard@google.com> | Wed Aug 11 18:02:12 2021 -0700 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Wed Aug 11 18:03:08 2021 -0700 |
tree | e408348effebefacd98d363af25c0e70915d8c00 | |
parent | 20255159c914f55efae4ce586f4db0972669dc47 [diff] |
Enable dot production microkernels for QU8 on Cortex A55 - Switch from neon lane to dotproduct on Cortex A55 in aarch64 - Change from 4x16 to 4x8 to reduce register pressure. PiperOrigin-RevId: 390261202
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.
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 three generations of MobileNet models and three generations of Pixel phones.
Model | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |
---|---|---|---|
MobileNet v1 1.0X | 82 | 86 | 88 |
MobileNet v2 1.0X | 49 | 53 | 55 |
MobileNet v3 Large | 39 | 42 | 44 |
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 |
---|---|---|---|
MobileNet v1 1.0X | 43 | 27 | 46 |
MobileNet v2 1.0X | 26 | 18 | 28 |
MobileNet v3 Large | 22 | 16 | 24 |
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.
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 |
---|---|---|---|---|
MobileNet v1 1.0X | 4004 | 337 | 116 | 72 |
MobileNet v2 1.0X | 2011 | 195 | 83 | 41 |
MobileNet v3 Large | 1694 | 163 | 70 | 38 |
MobileNet v3 Small | 482 | 52 | 23 | 13 |
Benchmarked on May 22, 2020 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. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.