A Reliability Concern on Photonic Neural Networks
Yinyi Liu1,Jiaxu Zhang1 Jun Feng1Shixi Chen1 and Jiang Xu2,1
1Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology
2Microelectronics Thrust, The Hong Kong University of Science and Technology
jiang.xu@ust.hk
ABSTRACT
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.
Keywords: Reliability, Crosstalk, Photonic Neural Networks, Photonic Cores, Many-Core System, Heterogeneous Chiplet, VLSI.
