commit | 22f38e4ee00858c28209aece109cd81276ff1296 | [log] [tgz] |
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author | Marat Dukhan <maratek@google.com> | Tue Oct 01 11:17:55 2019 -0700 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Tue Oct 01 11:18:35 2019 -0700 |
tree | 44a626fce4434dfa17052cfebe3a8d87d8a4231e | |
parent | 1dadbf71d9e2468dad817bcb66e0b6bd7279b980 [diff] |
Refactor architecture identification macros PiperOrigin-RevId: 272253535
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.