Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151456
Title: Efficient on-chip training of optical neural networks using genetic algorithm
Authors: Zhang, Hui
Thompson, Jayne
Gu, Mile
Jiang, Xudong
Cai, Hong
Liu, Patricia Yang
Shi, Yuzhi
Zhang, Yi
Muhammad Faeyz Karim
Lo, Guo Qiang
Luo, Xianshu
Dong, Bin
Kwek, Leong Chuan
Liu, Ai Qun
Keywords: Engineering
Issue Date: 2021
Source: Zhang, H., Thompson, J., Gu, M., Jiang, X., Cai, H., Liu, P. Y., Shi, Y., Zhang, Y., Muhammad Faeyz Karim, Lo, G. Q., Luo, X., Dong, B., Kwek, L. C. & Liu, A. Q. (2021). Efficient on-chip training of optical neural networks using genetic algorithm. ACS Photonics, 8(6), 1662-1672. https://dx.doi.org/10.1021/acsphotonics.1c00035
Journal: ACS Photonics 
Abstract: Recent advances in silicon photonic chips have made huge progress in optical computing owing to their flexibility in the reconfiguration of various tasks. Its deployment of neural networks serves as an alternative for mitigating the rapidly increased demand for computing resources in electronic platforms. However, it remains a formidable challenge to train the online programmable optical neural networks efficiently, being restricted by the difficulty in obtaining gradient information on a physical device when executing a gradient descent algorithm. Here, we experimentally demonstrate an efficient, physics-agnostic, and closed-loop protocol for training optical neural networks on chip. A gradient-free algorithm, that is, the genetic algorithm, is adopted. The protocol is on-chip implementable, physical agnostic (no need to rely on characterization and offline modeling), and gradient-free. The protocol works for various types of chip structures and is especially helpful to those that cannot be analytically decomposed and characterized. We confirm its viability using several practical tasks, including the crossbar switch and the Iris classification. Finally, by comparing our physics-agonistic and gradient-free method to the off-chip and gradient-based training methods, we demonstrate the robustness of our system to perturbations such as imperfect phase implementation and photodetection noise. Optical processors with gradient-free genetic algorithms have broad application potentials in pattern recognition, reinforcement learning, quantum computing, and realistic applications (such as facial recognition, natural language processing, and autonomous vehicles).
URI: https://hdl.handle.net/10356/151456
ISSN: 2330-4022
DOI: 10.1021/acsphotonics.1c00035
Rights: This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Photonics, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acsphotonics.1c00035
Fulltext Permission: embargo_20220616
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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  Until 2022-06-16
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