Receptive Field-Based All-Optical Spiking Neural Network for Image Processing

Authors:
raw_author_name: Taiyi Chen
raw_affiliation_string: School of Optoelectronic Science and Engineering, the Collaborative Innovation Center of Suzhou Nano Science and Technology, the Key Laboratory of Advanced Optical Manufacturing Technologies of Jiangsu Province, and the Key Laboratory of Modern Optical Technologies of Education Ministry of China, Soochow University, Suzhou, China
raw_author_name: Yu Huang
raw_affiliation_string: School of Optoelectronic Science and Engineering, the Collaborative Innovation Center of Suzhou Nano Science and Technology, the Key Laboratory of Advanced Optical Manufacturing Technologies of Jiangsu Province, and the Key Laboratory of Modern Optical Technologies of Education Ministry of China, Soochow University, Suzhou, China
raw_author_name: Pei Zhou
raw_affiliation_string: School of Optoelectronic Science and Engineering, the Collaborative Innovation Center of Suzhou Nano Science and Technology, the Key Laboratory of Advanced Optical Manufacturing Technologies of Jiangsu Province, and the Key Laboratory of Modern Optical Technologies of Education Ministry of China, Soochow University, Suzhou, China
raw_author_name: Penghua Mu
raw_affiliation_string: Institute of Science and Technology for Opto-Electronic Information, Yantai University, Yantai, China
raw_author_name: Shuiying Xiang
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi&#x2019;an, China
raw_author_name: V. N. Chizhevsky
raw_affiliation_string: B. I. Stepanov Institute of Physics, National Academy of Science of Belarus, Minsk, Belarus
raw_author_name: Nianqiang Li
raw_affiliation_string: School of Optoelectronic Science and Engineering, the Collaborative Innovation Center of Suzhou Nano Science and Technology, the Key Laboratory of Advanced Optical Manufacturing Technologies of Jiangsu Province, and the Key Laboratory of Modern Optical Technologies of Education Ministry of China, Soochow University, Suzhou, China

Abstract: We report on a novel structure of a receptive field (RF)-based multi-layer all-optical neural network using a micropillar laser with a saturable absorber (SA) for image processing. From the perspective of biological vision, the realization of image processing based on the RF provides the biological rationality for the machine vision implemented by the spiking neural network (SNN). By exploiting the fast physical mechanisms of gain and absorption in the SA laser, the photonic spike-timing-dependent plasticity (STDP) curves are achieved to train the weights. Here, the source image pixels are mapped into the temporal information of spike trains injected into the neural network through the temporal coding method called time-to-first-spike encoding. Different source images are processed and tested by the proposed photonic SNN. Simulation results show that our proposed system can process not only simple binary images but also complex color images under the adjustment of STDP rules. When considering the robustness, we demonstrate the tolerance of the image segmentation to the time jitter. These results indicate that our proposed photonic SNN can achieve high-resolution processing of complex source images. Additionally, the time-multiplexing technique can be further adopted to simplify the RF structure, which is expected to reduce the complexity of the whole system, thus facilitating physical applications. Our work offers the prospect for a high-speed photonic spiking platform for image processing.