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

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.