Brain-like training of a pre-sensor optical neural network with a backpropagation-free algorithm

Deep learning has rapidly advanced amidst the proliferation of large models, leading to challenges in computational resources and power consumption. Optical neural networks (ONNs) offer a solution by shifting computation to optics, thereby leveraging the benefits of low power consumption, low latency, and high parallelism. The current training paradigm for ONNs primarily relies on backpropagation (BP). However, the reliance is incompatible with potential unknown processes within the system, which necessitates detailed knowledge and precise mathematical modeling of the optical process. In this paper, we present a pre-sensor multilayer ONN with nonlinear activation, utilizing a forward-forward algorithm to directly train both optical and digital parameters, which replaces the traditional backward pass with an additional forward pass. Our proposed nonlinear optical system demonstrates significant improvements in image classification accuracy, achieving a maximum enhancement of 9.0%. It also validates the efficacy of training parameters in the presence of unknown nonlinear components in the optical system. The proposed training method addresses the limitations of BP, paving the way for applications with a broader range of physical transformations in ONNs.