Optical–electronic hybrid Fourier convolutional neural network based on super-pixel complex-valued modulation
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Published:2023-02-07
Issue:5
Volume:62
Page:1337
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ISSN:1559-128X
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Container-title:Applied Optics
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language:en
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Short-container-title:Appl. Opt.
Author:
Fan Li,
Long Xilin,
Dai Jun,
Li Chong1,
Dong Xiaowen1,
He Jian-JunORCID
Affiliation:
1. Huawei Technologies Co., Ltd.
Abstract
An optical–electronic hybrid convolutional neural network (CNN) system is proposed and investigated for its parallel processing capability and system design robustness. It is regarded as a practical way to implement real-time optical computing. In this paper, we propose a complex-valued modulation method based on an amplitude-only liquid-crystal-on-silicon spatial light modulator and a fixed four-level diffractive optical element. A comparison of computational results of convolutions between different modulation methods in the Fourier plane shows the feasibility of the proposed complex-valued modulation method. A hybrid CNN model with one convolutional layer of multiple channels is proposed and trained electrically for different classification tasks. Our simulation results show that this model has a classification accuracy of 97.55% for MNIST, 88.81% for Fashion MNIST, and 56.16% for Cifar10, which outperforms models using only amplitude or phase modulation and is comparable to the ideal complex-valued modulation method.
Funder
National Key Research and Development Program of China
Huawei-ZJU Center for Innovation on Optical Computing
Publisher
Optica Publishing Group
Subject
Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering