commit | c6edf92e9d9619bfe45ddd5baeea1293921dae74 | [log] [tgz] |
---|---|---|
author | Marat Dukhan <maratek@google.com> | Thu Oct 03 15:08:04 2019 -0700 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Thu Oct 03 15:08:26 2019 -0700 |
tree | c8fa07270670d3d4e24985152b7350c23cfdc732 | |
parent | 9d056a4f254ef0016ef37b92310d876a50d60839 [diff] |
Fix bug in Fully Connected F32 unit tests PiperOrigin-RevId: 272750791
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.