Flexible optical interconnects for efficient resource utilization and distributed machine learning training in disaggregated architectures

Madeleine Glick; Zhenguo Wu; Shijia Yan; Ziyi Zhu; Keren Bergman

doi:10.1117/12.2615686

3 March 2022 Flexible optical interconnects for efficient resource utilization and distributed machine learning training in disaggregated architectures

Madeleine Glick, Zhenguo Wu, Shijia Yan, Ziyi Zhu, Keren Bergman

Author Affiliations +

Proceedings Volume 12027, Metro and Data Center Optical Networks and Short-Reach Links V; 1202703 (2022) https://doi.org/10.1117/12.2615686
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

The explosive growth in data analytics is driving an intensely growing need for compute performance. We will review our ARPA-E PINE ENLITENED experimental testbed and simulation results to motivate performance advantages of disaggregation through the use of flexible photonic interconnect networks for distributed deep learning applications.

Conference Presentation

Citation Download Citation

Madeleine Glick, Zhenguo Wu, Shijia Yan, Ziyi Zhu, and Keren Bergman "Flexible optical interconnects for efficient resource utilization and distributed machine learning training in disaggregated architectures", Proc. SPIE 12027, Metro and Data Center Optical Networks and Short-Reach Links V, 1202703 (3 March 2022); https://doi.org/10.1117/12.2615686

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