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

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 ph

SUBMITTER: Mourgias-Alexandris G 

PROVIDER: S-EPMC9508134 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature