| commit | afbca9ad87f3f295157794eeb6bbaf762722d4dc | [log] [tgz] |
|---|---|---|
| author | Frank Barchard <fbarchard@google.com> | Mon Oct 07 18:20:45 2019 -0700 |
| committer | XNNPACK Team <xnnpack-github-robot@google.com> | Mon Oct 07 18:21:08 2019 -0700 |
| tree | f920fb5863a809b1bc4ac5a5582693e3dc5004df | |
| parent | d620972ea323fdd9779360bbddd0385c97fa0e9a [diff] |
Remove unused x21 and switch x20 to x8 to avoid push. Add missing prefetches in outer loop as done in GEMM kernels. PiperOrigin-RevId: 273422307
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 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.
XNNPACK is a based on QNNPACK library. However, unlike QNNPACK, XNNPACK focuses entirely on floating-point operators, and its API is no longer compatible with QNNPACK.