Symmetric silicon microring resonator optical crossbar array for accelerated inference and training in deep learning  

作　　者：RUI TANG SHUHEI OHNO KEN TANIZAWA KAZUHIRO IKEDA MAKOTO OKANO KASIDIT TOPRASERTPONG SHINICHI TAKAGI MITSURU TAKENAKA 

机构地区：[1]Department of Electrical Engineering and Information Systems,The University of Tokyo,Tokyo 113-8656,Japan [2]Quantum ICT Research Institute,Tamagawa University,Tokyo 194-8610,Japan [3]National Institute of Advanced Industrial Science and Technology,Ibaraki 305-8568,Japan

出　　处：《Photonics Research》2024年第8期1681-1688,共8页光子学研究（英文版）

基　　金：Japan Science and Technology Agency(CREST,JPMJCR2004);Japan Society for the Promotion of Science(22K14298)。

摘　　要：Photonic integrated circuits are emerging as a promising platform for accelerating matrix multiplications in deep learning,leveraging the inherent parallel nature of light.Although various schemes have been proposed and demonstrated to realize such photonic matrix accelerators,the in situ training of artificial neural networks using photonic accelerators remains challenging due to the difficulty of direct on-chip backpropagation on a photonic chip.In this work,we propose a silicon microring resonator(MRR)optical crossbar array with a symmetric structure that allows for simple on-chip backpropagation,potentially enabling the acceleration of both the inference and training phases of deep learning.We demonstrate a 4×4 circuit on a Si-on-insulator platform and use it to perform inference tasks of a simple neural network for classifying iris flowers,achieving a classification accuracy of 93.3%.Subsequently,we train the neural network using simulated on-chip backpropagation and achieve an accuracy of 91.1%in the same inference task after training.Furthermore,we simulate a convolutional neural network for handwritten digit recognition,using a 9×9 MRR crossbar array to perform the convolution operations.This work contributes to the realization of compact and energy-efficient photonic accelerators for deep learning.

关 键 词：RESONATOR neural matrix 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN491[自动化与计算机技术—控制科学与工程]