PhotoFourier: silicon photonics joint transfer correlator for convolution neural network

Nicola Peserico; Shurui Li; Hangbo Yang; Hamed Dalir; Puneet Gupta; Volker J. Sorger

doi:10.1117/12.2678666

4 October 2023 PhotoFourier: silicon photonics joint transfer correlator for convolution neural network

Nicola Peserico, Shurui Li, Hangbo Yang, Hamed Dalir, Puneet Gupta, Volker J. Sorger

Proceedings Volume 12673, Optics and Photonics for Information Processing XVII; 1267307 (2023) https://doi.org/10.1117/12.2678666
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Convolution Neural Networks are one of the pillars of the Machine Learning revolution over the last years. However, convolution operation is associated with a high computational cost. Here, we present a compact and high-speed solution to perform convolution leveraging optics on a chip, with the potential to reach over 350 TOPS/W.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nicola Peserico, Shurui Li, Hangbo Yang, Hamed Dalir, Puneet Gupta, and Volker J. Sorger "PhotoFourier: silicon photonics joint transfer correlator for convolution neural network", Proc. SPIE 12673, Optics and Photonics for Information Processing XVII, 1267307 (4 October 2023); https://doi.org/10.1117/12.2678666

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