| commit | 4a5c77126f15be6f7a6f5a0e5dd6a4ee546d72cf | [log] [tgz] |
|---|---|---|
| author | Marat Dukhan <maratek@google.com> | Wed Jan 05 22:43:13 2022 -0800 |
| committer | XNNPACK Team <xnnpack-github-robot@google.com> | Wed Jan 05 22:44:11 2022 -0800 |
| tree | 52bdc15e12efc6ffe02587e4a387fc4f89bf0907 | |
| parent | 5999c92486e57eeeb25ade631fe33773a6a6d1a6 [diff] |
Refactor F32 RADDSTOREEXPMINUSMAX microkernels - Move mask_table into AVX microkernel parameters to unblock amalgamation - Move constant literals into microkernel parameters PiperOrigin-RevId: 419984793
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 |
|---|---|---|---|
| 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.
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 | 3937 | 299 | 114 | 72 | 76 |
| FP32 MobileNet v2 1.0X | 1987 | 187 | 79 | 41 | 44 |
| FP32 MobileNet v3 Large | 1658 | 158 | 67 | 38 | 41 |
| FP32 MobileNet v3 Small | 487 | 50 | 23 | 13 | 14 |
| INT8 MobileNet v1 1.0X | 2598 | 169 | 61 | 29 | 24 |
| INT8 MobileNet v2 1.0X | 1487 | 109 | 40 | 20 | 17 |
Benchmarked on Oct 15, 2021 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.
XNNPACK is a based on QNNPACK library. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.