Reliability Analysis of Optical Neural Networks with Non-Ideal Signal Transmission

作     者：Fu, Pengju Xie, Yiyuan Su, Ye Ye, Yichen Chai, Junxiong Jiang, Xiao Yang, Hongyu 

作者机构：College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China Key Laboratory of Networks and Cloud Computing Security of Universities Chongqing China 

出 版 物：《SSRN》 

年 卷 期：2024年

核心收录：

主　　题：Crosstalk 

摘      要：Machine learning plays a significant role in various fields. As a fundamental part of machine learning, matrix operations are one of the most important computational parts of neural networks. However, the advancement of Moore’s law is hindered by limitations in the number and size of transistors on electronic chips. Therefore, the implementation of optical neural network (ONN) using photonic devices to accomplish matrix-vector multiplication (MVM) has attracted significant attention. Compared with traditional electrical on neural networks, ONN exhibits faster computational speed, higher parallel input capability and greater energy efficiency. Nevertheless, crosstalk and signal loss are inevitable characteristics in optical signal transmission, which can degrade the performance of ONN. In this paper, we present analysis models and simulations to assess the effects of crosstalk and loss on optical computing systems. We have observed that the accuracy of ONN degrades when crosstalk and loss are present. In severe cases, ONN is unable to complete the classification task. To mitigate the interference of crosstalk, we have designed a novel double-layer structure to reduce the number of optical devices required for signal transmission and optimize the impact of crosstalk on ONN performance. For the identical classification task, the ONN based on a double-layer structure exhibits better interference immunity than the generic ONN, with a 10% performance improvement. In particular, performance is improved by 50% when only crosstalk is considered. © 2024, The Authors. All rights reserved.