Dual Optical Frequency Comb Neuron: Co-Developing Hardware and Algorithm

Abstract

Previous studies on photonic neural network have demonstrated that algorithm can inspire hardware design. This study seeks to demonstrate that hardware can also inspire algorithm design. To further exploit the advantages of photonic analog computing, the authors develop hardware and algorithm simultaneously for photonic convolutional neural networks. Specifically, this work developed an architecture called dual optical frequency comb neuron (DOFCN) enabled by an integrated microcomb to perform cosinusoidal nonlinear activation and vector convolution without temporal or spatial dispersion and large-scale modulator arrays. Furthermore, DOFCN-based composite vector convolutional neural networks (CVCNNs), an optical-electric hybrid model, are proposed to perform classification and regression tests in signal modulation format identification and optical structure inverse design tasks, respectively. The ablation experiments show that under 4-bit precision limit, the element-wise activation CVCNN has 14% higher classification accuracy, 76% lower regression residuals, and 100% higher training efficiency than that of the 32-bit standard convolutional neural network (CNN). DOFCN exhibits impressive spectral information processing ability to facilitate signal-processing tasks related to optics and electromagnetics.