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Optical Neural Ordinary Differential Equations

Authors :
Zhao, Yun
Chen, Hang
Lin, Min
Zhang, Haiou
Yan, Tao
Lin, Xing
Huang, Ruqi
Dai, Qionghai
Publication Year :
2022

Abstract

Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance. However, the successively cascading of network hidden layers results in larger integrated photonic chip areas. To address this issue, we propose the optical neural ordinary differential equations (ON-ODE) architecture that parameterizes the continuous dynamics of hidden layers with optical ODE solvers. The ON-ODE comprises the PNNs followed by the photonic integrator and optical feedback loop, which can be configured to represent residual neural networks (ResNet) and recurrent neural networks with effectively reduced chip area occupancy. For the interference-based optoelectronic nonlinear hidden layer, the numerical experiments demonstrate that the single hidden layer ON-ODE can achieve approximately the same accuracy as the two-layer optical ResNet in image classification tasks. Besides, the ONODE improves the model classification accuracy for the diffraction-based all-optical linear hidden layer. The time-dependent dynamics property of ON-ODE is further applied for trajectory prediction with high accuracy.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2209.12898
Document Type :
Working Paper
Full Text :
https://doi.org/10.1364/OL.477713