Noise-resilient and high-speed deep learning with coherent silicon photonics
George Mourgias-Alexandris, Miltiadis Moralis‐Pegios, Apostolos Tsakyridis, Stelios Simos, George Dabos, Angelina Totović, Nikolaos Passalis, Manos Kirtas, Teerapat Rutirawut, Frédéric Y. Gardes, Anastasios Tefas, Nikos Pleros
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.