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

• URL: http://arxiv.org/abs/2203.09044v1
• Date: Thu, 17 Mar 2022 02:47:13 GMT
• Title: Convert, compress, correct: Three steps toward communication-efficient DNN training
• Authors: Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang, Stefano Rini
• Abstract summary: We introduce a novel algorithm, $mathsfCO_3$, for communication-efficiency distributed Deep Neural Network (DNN) training. $mathsfCO_3$ is a joint training/communication protocol, which encompasses three processing steps for the network gradients. The interplay of these three steps in processing the gradients is carefully balanced to yield a robust and high-performance scheme.
• Score: 19.440030100380632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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