Feature Extraction From Images Using Integrated Photonic Convolutional Kernel

IEEE Photonics Journal

Beiju Huang¹  Hongda Chen¹  Hengjie Zhang²  Run Chen²  Chuantong Cheng²  Huan Zhang²  Yulong Huang² 

[1] State Key Laboratory on Integrated Optoelectronics, ISCAS, Beijing, China;State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences (ISCAS), Beijing, China;

关键词: Integrated photonics;    micro-ring resonator;    convolution neural network;   

DOI  :  10.1109/JPHOT.2022.3163793

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown