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arXiv · 2203.09044

Convert, compress, correct: Three steps toward communication-efficient DNN training

Zhong-Jing Chen·Eduin E. Hernandez·Yu-Chih Huang·Stefano Rini
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Abstract

In this paper, we introduce a novel algorithm, $\mathsf{CO}_3$, for communication-efficiency distributed Deep Neural Network (DNN) training. $\mathsf{CO}_3$ is a joint training/communication protocol, which encompasses three processing steps for the network gradients: (i) quantization through floating-point conversion, (ii) lossless compression, and (iii) error correction. These three components are crucial in the implementation of distributed DNN training over rate-constrained links. The interplay of these three steps in processing the DNN gradients is carefully balanced to yield a robust and high-performance scheme. The performance of the proposed scheme is investigated through numerical evaluations over CIFAR-10.

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Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang, Stefano Rini. 2022-03-17. Convert, compress, correct: Three steps toward communication-efficient DNN training. https://arxiv.org/abs/2203.09044

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