A Large-Scale Photonic CNN Based on Spike Coding and Temporal Integration

Abstract

The real-time massive-data intelligent information processing tasks highlight a vital requirement for the novel, intelligent optimization hardware. Convolutional neural networks are highly capable of extracting the hierarchical feature map and enhancing recognition accuracy, with photonics-enabled implementations drawing considerable interest. Here, we propose a large-scale and reconfigurable photonic convolutional neural network (PCNN), based on a hardware-friendly distributed feedback laser diode (DFB-LD). Our approach applies biological time-to-first-spike coding to a DFB-LD neuron to perform a temporal convolutional operation (TCO) for image processing tasks. In PCNN, experimental results demonstrate that we successfully implement the TCO to extract the image features with convolutional kernels of size 11×11. Furthermore, we investigate the temporal pulse shaping of a DFB-LD neuron to build a densely-connected fully connected layer, which synaptic weights can be rapidly adjusted at a rate of 5 GHz, achieving full MNIST and Fashion-MNIST benchmark image classification tasks, with classification accuracies of 98.56% and 87.48%, respectively. This work highlights the potential of neuron-like optical analog computing platforms for real-time and more complex intelligent processing networks.