Feature Extraction From Images Using Integrated Photonic Convolutional Kernel
Yulong Huang, Beiju Huang, Chuantong Cheng, Huan Zhang, Hengjie Zhang, Run Chen, Hongda Chen · IEEE photonics journal · 2022
Optical neural networks are expected to solve the problems of computational efficiency and energy consumption in neural networks. Herein, we experimentally implemented a 2 × 2 photonic convolutional kernel (PCK) using four on-chip micro-ring resonators (MRRs) and demonstrated feature extraction for images with different convolutional kernels. We trained a simple convolutional neural network model to recognize the MNIST dataset and used our PCK devices for processing in the first convolutional layer, achieving a recognition rate of 91%, which further verified the feasibility of MRRs for convolution operations. In addition to the source, all silicon photonic devices used can be monolithically integrated and feature good scalability, which is important for realizing large-scale, low-cost optical neural networks.