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Evaluating the impact of optical interconnects on a multi-chip machine-learning architecture
Cited 3 time in
Web of Science
Cited 3 time in Scopus
- Authors
- Issue Date
- 2018-08
- Publisher
- MDPI AG
- Citation
- Electronics (Basel), Vol.7 No.8, p. 130
- Abstract
- Following trends that emphasize neural networks for machine learning, many studies regarding computing systems have focused on accelerating deep neural networks. These studies often propose utilizing the accelerator specialized in a neural network and the cluster architecture composed of interconnected accelerator chips. We observed that inter-accelerator communication within a cluster has a significant impact on the training time of the neural network. In this paper, we show the advantages of optical interconnects for multi-chip machine-learning architecture by demonstrating performance improvements through replacing electrical interconnects with optical ones in an existing multi-chip system. We propose to use highly practical optical interconnect implementation and devise an arithmetic performance model to fairly assess the impact of optical interconnects on a machine-learning accelerator platform. In our evaluation of nine Convolutional Neural Networks with various input sizes, 100 and 400 Gbps optical interconnects reduce the training time by an average of 20.6% and 35.6%, respectively, compared to the baseline system with 25.6 Gbps electrical ones.
- ISSN
- 2079-9292
- Language
- English
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