commit | 0c57d2a08ca62c3020a2acdb4acfe825ed2b2657 | [log] [tgz] |
---|---|---|
author | Daniel Smilkov <smilkov@google.com> | Mon Oct 07 10:06:44 2019 -0700 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Mon Oct 07 10:07:34 2019 -0700 |
tree | d11c9dbdf7c1edaffb0e9c3ff91a58190d9bc85a | |
parent | 1a729ec44926acdb98d42af4435d8d840bf62a86 [diff] |
Fix Bazel build for XNNPACK when using emscripten toolchain. These fixes allow TF.js to depend and successfully build the XNNPACK library in the open-source world. PiperOrigin-RevId: 273317397
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.