Optical convolutional neural network with the hybrid training method

Convolutional neural networks trained on electronic digital computers face deteriorated performance when inference is done with analog optical computing systems due to inherent analog noise. To reduce the impact of analog noise in an optical convolutional neural network, a hybrid training method is employed, using an optical convolution processor for forward propagation and an electronic digital computer for back-propagation. The inference accuracies of the network trained using this hybrid method on MNIST, Fashion-MNIST, and CIFAR-10 datasets were comparable to the results achieved by a network trained on an electronic digital computer, unaffected by analog computation errors.