Photonic tensor core for machine learning: a review

Nicola Peserico; Xiaoxuan Ma; Bahvin J. Shastri; Volker J. Sorger

doi:10.1117/12.2633916

3 October 2022 Photonic tensor core for machine learning: a review

Nicola Peserico, Xiaoxuan Ma, Bahvin J. Shastri, Volker J. Sorger

Proceedings Volume 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; 1220407 (2022) https://doi.org/10.1117/12.2633916
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Photonic tensor core circuits have been widely explored as possible hardware accelerators for the next generation of machine learning applications, due to the large bandwidth, low latency, and energy saving that light has. Many architectures have been presented, especially exploiting photonic integrated circuits. However, most of the proposed solutions lack some features, such as integration, scalability, or energy saving. In this paper, we review the major achievements in recent years, showing how high integration can lead to better performance, but it could also limit the scalability of the overall system.

Citation Download Citation

Nicola Peserico, Xiaoxuan Ma, Bahvin J. Shastri, and Volker J. Sorger "Photonic tensor core for machine learning: a review", Proc. SPIE 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, 1220407 (3 October 2022); https://doi.org/10.1117/12.2633916

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