| commit | df6985f793b2d81ae2a3538500cd97b4ab597561 | [log] [tgz] |
|---|---|---|
| author | Marat Dukhan <maratek@google.com> | Tue Oct 01 17:04:18 2019 -0700 |
| committer | XNNPACK Team <xnnpack-github-robot@google.com> | Tue Oct 01 17:04:46 2019 -0700 |
| tree | cde5a86a8cfaabc3cf983edd5c64b7e5c11252d9 | |
| parent | 33f0c7aa50f657f749cd93f173976d0c0f0d5e4e [diff] |
Map Exynos-M[1-4] micro-architectures to equivalents in OSS cpuinfo PiperOrigin-RevId: 272328080
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.