FLOPS: EFficient On-Chip Learning for OPtical Neural Networks Through Stochastic Zeroth-Order Optimization

Optical neural networks (ONNs) have attracted extensive attention due to its ultra-high execution speed and low energy consumption. The traditional software-based ONN training, however, suffers the problems of expensive hardware mapping and inaccurate variation modeling while the current on-chip training methods fail to leverage the self-learning capability of ONNs due to algorithmic inefficiency and poor variation- robustness. In this work, we propose an on-chip learning method to resolve the aforementioned problems that impede ONNs' full potential for ultra-fast forward acceleration. We directly optimize optical components using stochastic zeroth-order optimization on-chip, avoiding the traditional high-overhead back-propagation, matrix decomposition, or in situ devicelevel intensity measurements. Experimental results demonstrate that the proposed on-chip learning framework provides an efficient solution to train integrated ONNs with 3~4× fewer ONN forward, higher inference accuracy, and better variation-robustness than previous works.