A Reliability Concern on Photonic Neural Networks

Published in DATE, 2022

Recommended citation: LIU, Yinyi, et al. "A Reliability Concern on Photonic Neural Networks." Design, Automation and Test in Europe Conference (DATE) (2022). https://ieeexplore.ieee.org/abstract/document/9383800

Emerging integrated photonic neural networks have experimentally proved to achieve an ultra-high speedup of deep neural network training and inference in the optical domain. However, photonic devices suffer from the inherent crosstalk noise and loss, inevitably leading to reliability concerns. This paper systematically analyzes the impacts of crosstalk and loss on photonic computing systems. We propose a crosstalk-aware model for reliability estimation and find out the worst-case bounds as we increase the footprints and scales of the photonic chips. Our evaluations show that -30dB crosstalk noise can cause maximal photonic chip integration to a sharp drop by 109x. To facilitate very-large-scale photonic integration for future computing, we further propose multiple heterogeneous bijou photonic-cores to address the crosstalk-aware reliability concern.