Noise-resilient and high-speed deep learning with coherent silicon photonics

G. Mourgias-Alexandris (), M. Moralis-Pegios, A. Tsakyridis, S. Simos, G. Dabos, A. Totovic, N. Passalis, M. Kirtas, T. Rutirawut, F. Y. Gardes, A. Tefas and N. Pleros
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G. Mourgias-Alexandris: Aristotle University of Thessaloniki
M. Moralis-Pegios: Aristotle University of Thessaloniki
A. Tsakyridis: Aristotle University of Thessaloniki
S. Simos: Aristotle University of Thessaloniki
G. Dabos: Aristotle University of Thessaloniki
A. Totovic: Aristotle University of Thessaloniki
N. Passalis: Aristotle University of Thessaloniki
M. Kirtas: Aristotle University of Thessaloniki
T. Rutirawut: University of Southampton
F. Y. Gardes: University of Southampton
A. Tefas: Aristotle University of Thessaloniki
N. Pleros: Aristotle University of Thessaloniki

Nature Communications, 2022, vol. 13, issue 1, 1-7

Abstract: Abstract The explosive growth of deep learning applications has triggered a new era in computing hardware, targeting the efficient deployment of multiply-and-accumulate operations. In this realm, integrated photonics have come to the foreground as a promising energy efficient deep learning technology platform for enabling ultra-high compute rates. However, despite integrated photonic neural network layouts have already penetrated successfully the deep learning era, their compute rate and noise-related characteristics are still far beyond their promise for high-speed photonic engines. Herein, we demonstrate experimentally a noise-resilient deep learning coherent photonic neural network layout that operates at 10GMAC/sec/axon compute rates and follows a noise-resilient training model. The coherent photonic neural network has been fabricated as a silicon photonic chip and its MNIST classification performance was experimentally evaluated to support accuracy values of >99% and >98% at 5 and 10GMAC/sec/axon, respectively, offering 6× higher on-chip compute rates and >7% accuracy improvement over state-of-the-art coherent implementations.

Date: 2022
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DOI: 10.1038/s41467-022-33259-z

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