• 文献标题:   Photonic Spiking Neural Networks and Graphene-on-Silicon Spiking Neurons
  • 文献类型:   Article
  • 作  者:   JHA A, HUANG CR, PENG HT, SHASTRI B, PRUCNAL PR
  • 作者关键词:   photonic, neuron, optical resonator, hardware, laser mode, optical pumping, biological neural network, neural network, nonlinear photonic, photonic integrated circuit
  • 出版物名称:   JOURNAL OF LIGHTWAVE TECHNOLOGY
  • ISSN:   0733-8724 EI 1558-2213
  • 通讯作者地址:  
  • 被引频次:   8
  • DOI:   10.1109/JLT.2022.3146157
  • 出版年:   2022

▎ 摘  要

Spiking neural networks are known to be superior over artificial neural networks for their computational power efficiency and noise robustness. The benefits of spiking coupled with the high-bandwidth and low-latency of photonics can enable highly-efficient, noise-robust, high-speed neural processors. The landscape of photonic spiking neurons consists of an overwhelming majority of excitable lasers and a few demonstrations on nonlinear optical cavities. The silicon platform is best poised to host a scalable photonic technology given its CMOS-compatibility and low optical loss. Here, we present a survey of existing photonic spiking neurons, and propose a novel spiking neuron based on a hybrid graphene-on-silicon microring cavity. A comparison among a representative sample of photonic spiking devices is also presented. Finally, we discuss methods employed in training spiking neural networks, their challenges as well as the application domain that can be enabled by photonic spiking neural hardware.