On-chip deep residual photonic neural networks using optical-electrical shortcut connections

On-chip photonic neural networks (PNNs) have recently emerged as an attractive hardware accelerator for deep learning applications. However, deep PNNs with higher inference complexity are harder to train due to gradient vanishing and exploding problems. In this work, we propose an on-chip deep residual photonic neural network architecture (Res-PNN), which enables the training of deeper PNNs by using optical-electrical shortcut connections. The optical-electrical shortcut connection is designed using a power splitter, a wavelength demultiplexer, and photodetectors to directly connect the input and the output across optical weight layers. This optical-electrical shortcut connection alleviates the gradient vanishing and exploding problems by providing a direct path for gradient backpropagation, ensuring stable training of deeper PNNs. The proposed Res-PNN achieves classification accuracies of 88.4% on the CIFAR-10 dataset and 80.3% on the CIFAR-100 dataset. Compared to fully connected PNNs, Res-PNN improves classification accuracy by 3.2% on the CIFAR-10 dataset and 11.3% on the CIFAR-100 dataset.