_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
xnnpack 0.0-3.08f1489
Dependencies: clog@0.0-3.05332fd cpuinfo@0.0-3.05332fd pthreadpool@0.1-3.560c60d googletest@1.12.1 googlebenchmark@1.8.3 fxdiv@0.0-1.63058ef fp16@0.0-1.0a92994 psimd@0.0-1.072586a
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/google/XNNPACK
Licenses: Modified BSD
Synopsis: Optimized floating-point neural network inference operators
Description:

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms. XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch, and MediaPipe.

xnnpack 0.0-2.51a9875
Dependencies: clog@0.0-3.05332fd cpuinfo@0.0-3.05332fd pthreadpool@0.1-3.560c60d googletest@1.12.1 googlebenchmark@1.8.3 fxdiv@0.0-1.63058ef fp16@0.0-1.0a92994 psimd@0.0-1.072586a
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/google/XNNPACK
Licenses: Modified BSD
Synopsis: Optimized floating-point neural network inference operators
Description:

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms. XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch, and MediaPipe.

Total results: 2