Opto-Electronic Advances (Nov 2023)

Photonic integrated neuro-synaptic core for convolutional spiking neural network

  • Shuiying Xiang,
  • Yuechun Shi,
  • Yahui Zhang,
  • Xingxing Guo,
  • Ling Zheng,
  • Yanan Han,
  • Yuna Zhang,
  • Ziwei Song,
  • Dianzhuang Zheng,
  • Tao Zhang,
  • Hailing Wang,
  • Xiaojun Zhu,
  • Xiangfei Chen,
  • Min Qiu,
  • Yichen Shen,
  • Wanhua Zheng,
  • Yue Hao

DOI
https://doi.org/10.29026/oea.2023.230140
Journal volume & issue
Vol. 6, no. 11
pp. 1 – 14

Abstract

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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.

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