Photonic Spiking Neural Networks and Graphene-on-Silicon Spiking Neurons

Aashu Jha, Chaoran Huang, Hsuan Tung Peng, Bhavin Shastri, Paul R. Prucnal

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2901-2914
Number of pages14
JournalJournal of Lightwave Technology
Volume40
Issue number9
DOIs
StatePublished - May 1 2022

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics

Keywords

  • Neural networks
  • nonlinear photonics
  • photonic integrated circuits

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