Training optronic convolutional neural networks on an optical system through backpropagation algorithms

Abstract

The development of optical neural networks greatly slows the urgent demand of searching for fast computing approaches to solve big data processing. However, most optical neural networks following electronic training and optical inferencing do not really take full advantage of optical computing to reduce computational burden. Take the extensively used optronic convolutional neural networks (OPCNN) as an example, the convolutional operations still require vast computational operations in training stages on the computer. To address this issue, this study proposes the in-situ training algorithm to train the networks directly in optics. We derive the backpropagation algorithms of OPCNN hence the complicated gradient calculation in backward propagating processes can be obtained through optical computing. Both forward propagation and backward propagation are all executed on the same optical system. Furthermore, we successfully realize the introduction of optical nonlinearity in networks through utilizing photorefractive crystal SBN:60 and we also derive the corresponding backpropagation algorithm. The numerical simulation results of classification performance on several datasets validates the feasibility of the proposed algorithms. Through in-situ training, the reduction in performance resulting from the inconsistency of the plantform between training and inferencing stages can be eliminated completely. For example, we demonstrate that by using the optical training approach, OPCNN is capable of gaining a strong robustness under several misalignmed situations, which enhances the practicability of OPCNN and greatly expands its application range.