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Awesome GitHub RepositoriesPluggable Hardware Backends
Custom backends that extend array and autograd operations to new hardware devices without modifying core logic.
Distinguishing note: None of the hardware-iot candidates match: this is about software-defined device backends for a deep learning framework, not physical hardware identification, redirection, or driver mapping.
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Awesome Pluggable Hardware Backends GitHub Repositories
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- chainer/chainer
chainer/chainer
5,919Vezi pe GitHubChainer is an open-source deep learning framework built around define-by-run automatic differentiation, where computation graphs are constructed dynamically during forward execution. This imperative approach allows networks to be built using standard Python control flow, with gradients computed automatically through reverse-mode differentiation on the dynamically recorded graph. The framework supports GPU acceleration through a NumPy-compatible array backend with CUDA and cuDNN support, and provides a pluggable device abstraction that lets users switch between CPU and GPU computation without c
ChainerX plugs in custom backends to support additional device types without modifying the core array or autograd logic.
Python
