Leapfrogging for parallelism in deep neural networks

Published 15 Jan 2018 in cs.LG and cs.DC | (1801.04928v1)

Abstract: We present a technique, which we term leapfrogging, to parallelize back- propagation in deep neural networks. We show that this technique yields a savings of $1-1/k$ of a dominant term in backpropagation, where k is the number of threads (or gpus).

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Authors (1)

Yatin Saraiya

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