Edge computing with optical neural networks via WDM weight broadcasting

Ryan Hamerly; Alexander Sludds; Saumil Bandyopadhyay; Liane Bernstein; Zaijun Chen; Manya Ghobadi; Dirk Englund

doi:10.1117/12.2594886

2 September 2021 Edge computing with optical neural networks via WDM weight broadcasting

Ryan Hamerly, Alexander Sludds, Saumil Bandyopadhyay, Liane Bernstein, Zaijun Chen, Manya Ghobadi, Dirk Englund

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041R (2021) https://doi.org/10.1117/12.2594886
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

We introduce an optical neural-network architecture for edge computing that takes advantage of wavelength multiplexing, high-bandwidth modulation, and integration detection. Our protocol consists of a server and a client, which divide the task of neural-network inference into two steps: (1) a difficult step of optical weight distribution, performed at the server and (2) an easy step of modulation and integration detection, performed at the edge device. This arrangement allows for large-scale neural networks to be run on low-power edge devices accessible by an optical link. We perform simulations to estimate the speed and energy limits of this scheme.

Conference Presentation

Citation Download Citation

Ryan Hamerly, Alexander Sludds, Saumil Bandyopadhyay, Liane Bernstein, Zaijun Chen, Manya Ghobadi, and Dirk Englund "Edge computing with optical neural networks via WDM weight broadcasting", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041R (2 September 2021); https://doi.org/10.1117/12.2594886

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