cuDNN: Efficient Primitives for Deep Learning

CoRR, 2014.

Cited by: 1049|Bibtex|Views201
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels must be reoptimized, which makes maintaining codebases difficult over time. Similar issues have long...More

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