Hardware-efficient, large-scale reconfigurable optical neural network (ONN) with backpropagation

Fei Xia; Ziao Wang; Logan Wright; Tatsuhiro Onodera; Martin Stein; Jianqi Hu; Peter McMahon; Sylvain Gigan

doi:10.1117/12.2646861

15 March 2023 Hardware-efficient, large-scale reconfigurable optical neural network (ONN) with backpropagation

Fei Xia, Ziao Wang, Logan Wright, Tatsuhiro Onodera, Martin Stein, Jianqi Hu, Peter McMahon, Sylvain Gigan

Author Affiliations +

Proceedings Volume 12438, AI and Optical Data Sciences IV; 124380Z (2023) https://doi.org/10.1117/12.2646861
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

We developed and implemented a deep optical neural network (ONN) design capable of performing large-scale training and inference in situ. For each elementary building block in the ONN, we introduce trainable parameters in a programmable device, weight mixing with a diffuser, and nonlinear detection on the camera for activation and optical readout. With automated reconfigurable neural architecture search, we optimized the architecture of deep ONNs that can perform multiple tasks at high speed and at large scale. The task accuracies achieved by our experiments are close to state-of-the-art benchmarks with conventional multilayer neural networks.

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Citation Download Citation

Fei Xia, Ziao Wang, Logan Wright, Tatsuhiro Onodera, Martin Stein, Jianqi Hu, Peter McMahon, and Sylvain Gigan "Hardware-efficient, large-scale reconfigurable optical neural network (ONN) with backpropagation", Proc. SPIE 12438, AI and Optical Data Sciences IV, 124380Z (15 March 2023); https://doi.org/10.1117/12.2646861

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