Silicon Photonic Neural Applications and Prospects

Bhavin J. Shastri, Chaoran Huang, Alexander N. Tait, Thomas Ferreira de Lima, Paul R. Prucnal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Neural networks have enabled many applications in artificial intelligence and neuromorphic computing ranging from scientific computing, intelligent communications, security etc. Neural networks implemented in on digital platforms are limited in speed and energy efficiency. Neuromorphic (i.e., neuron-isomorphic) photonics aims to build processors in which optical hardware mimic neural networks in the brain. These processors promise orders of magnitude improvements in both speed and energy efficiency over purely digital electronic approaches. However, integrated optical neural networks are much smaller (hundreds of neurons) than electronic implementations (tens of millions of neurons). This raises a question: what are the applications where sub-nanosecond latencies and energy efficiency trump the sheer size of processor? We provide an overview of neuromorphic photonic systems and their real-world applications to machine learning and neuromorphic computing.

Original languageEnglish (US)
Title of host publicationAI and Optical Data Sciences III
EditorsBahram Jalali, Ken-ichi Kitayama
PublisherSPIE
ISBN (Electronic)9781510649095
DOIs
StatePublished - 2022
Externally publishedYes
EventAI and Optical Data Sciences III 2022 - Virtual, Online
Duration: Feb 20 2022Feb 24 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12019
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAI and Optical Data Sciences III 2022
CityVirtual, Online
Period2/20/222/24/22

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Silicon photonics
  • machine learning
  • neuromorphic computing
  • photonic integrated circuits

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