Optical backpropagation training method and its applications

Tiankuang Zhou; Lu Fang; Tao Yan; Jiamin Wu; Yipeng Li; Jingtao Fan; Huaqiang Wu; Xing Lin; Qionghai Dai

doi:10.1117/12.2575111

10 October 2020 Optical backpropagation training method and its applications

Tiankuang Zhou, Lu Fang, Tao Yan, Jiamin Wu, Yipeng Li, Jingtao Fan, Huaqiang Wu, Xing Lin, Qionghai Dai

Proceedings Volume 11550, Optoelectronic Imaging and Multimedia Technology VII; 1155002 (2020) https://doi.org/10.1117/12.2575111
Event: SPIE/COS Photonics Asia, 2020, Online Only

Abstract

Training an artificial neural network with backpropagation algorithms requires an extensive computational process. Our recent work proposes to implement the backpropagation algorithm optically for in-situ training of both the linear and nonlinear diffractive optical neural networks which enables the acceleration of training speed and improvement on the energy efficiency on core computing modules. We numerically validated that the proposed in-situ optical learning architecture achieves comparable accuracy to the in-silico training with an electronic computer on the task of object classification and matrix-vector multiplication, which further allows adaptation to the system imperfections. Besides, the self-adaptive property of our approach facilitates the novel application of the network for all-optical imaging through scattering media. The proposed approach paves the way for the robust implementation of large-scale diffractive neural networks to perform distinctive tasks all-optically.

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Citation Download Citation

Tiankuang Zhou, Lu Fang, Tao Yan, Jiamin Wu, Yipeng Li, Jingtao Fan, Huaqiang Wu, Xing Lin, and Qionghai Dai "Optical backpropagation training method and its applications", Proc. SPIE 11550, Optoelectronic Imaging and Multimedia Technology VII, 1155002 (10 October 2020); https://doi.org/10.1117/12.2575111

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