¹ State Key Laboratory of Integrated Service Networks, State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, Xidian University, Xi'an 710071, China
中国 西安 西安电子科技大学 综合业务网理论及关键技术国家重点实验室 宽带隙半导体技术国家重点学科实验室
² Yongjiang laboratory, No. 1792 Cihai South Road, Ningbo 315202, China
中国 宁波 甬江实验室
³ The School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
中国 西安 西安邮电大学通信与信息工程学院
⁴ Laboratory of Solid-State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
中国 北京 中国科学院半导体研究所 固态光电信息技术研究室
⁵ School of Information Science and Technology, Nantong University, Nantong 226019, China
中国 南通 南通大学信息科学技术学院
⁶ The College of Engineering and Applied Sciences, Nanjing University, Nanjing 210023, China
中国 南京 南京大学现代工程与应用科学学院
⁷ Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
中国 杭州 西湖大学工学院 浙江省3D微纳加工和表征研究重点实验室
⁸ Lightelligence Group, Hangzhou 311121, China
中国 杭州 曦智科技
Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture. Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network (PSNN). However, they are separately implemented with different photonic materials and devices, hindering the large-scale integration of PSNN.
Here, we propose, fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback (DFB) laser with a saturable absorber (DFB-SA). A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.
Furthermore, a four-channel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network, achieving a recognition accuracy of 87% for the MNIST dataset. The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.