commit | 629a33eef8ef2fa86764a16da85a4770f3898953 | [log] [tgz] |
---|---|---|
author | Marat Dukhan <maratek@google.com> | Tue Oct 01 10:39:14 2019 -0700 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Tue Oct 01 10:39:42 2019 -0700 |
tree | ced6c8afc7358979fdf3b11960794a778df94bf4 | |
parent | 4c2637da4b0a1df58bb30e8ef24b182fc3065fb7 [diff] |
Fix incompatibilities with open-source Bazel-based build - Add fall-backs for Clang-specific __builtin_shufflevector - Make definition of XNN_UNPREDICTABLE compatible with GCC - Add missing SIMD bitcasts in SSE PReLU micro-kernel - Fix FP16 include paths in benchmarks - Fix GTest include path in generated tests - Fully qualify std::signbit and std::isnan PiperOrigin-RevId: 272243695
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.