Silicon Photonics for Neuromorphic Computing and Artificial Intelligence: Applications and Roadmap

B. J. Shastri, C. Huang, A. N. Tait, T. Ferreira De Lima, P. R. Prucnal

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

3 Scopus citations

Abstract

Artificial intelligence and neuromorphic computing driven by neural networks has enabled many applications. Software implementations of neural networks on electronic platforms are limited in speed and energy efficiency. Neuromorphic 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 publication2022 Photonics and Electromagnetics Research Symposium, PIERS 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages18-26
Number of pages9
ISBN (Electronic)9781665460231
DOIs
StatePublished - 2022
Event2022 Photonics and Electromagnetics Research Symposium, PIERS 2022 - Hangzhou, China
Duration: Apr 25 2022Apr 29 2022

Publication series

NameProgress in Electromagnetics Research Symposium
Volume2022-April
ISSN (Print)1559-9450
ISSN (Electronic)1931-7360

Conference

Conference2022 Photonics and Electromagnetics Research Symposium, PIERS 2022
Country/TerritoryChina
CityHangzhou
Period4/25/224/29/22

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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