Physical modeling of photonic neural networks

Thomas Ferreira De Lima, Bhavin J. Shastri, Mitchell A. Nahmias, Alexander N. Tait, Paul R. Prucnal

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

2 Scopus citations

Abstract

Brain-inspired distributed computing has attracted attention for its energy-efficient processing, and photonic neuromorphic hardware can overcome latency vs. fan-in tradeoffs from neuromorphic electronics. Here, we introduce system-level, physically detailed modeling tools for photonic neural networks, and use them to study the behavior of attractor networks.

Original languageEnglish (US)
Title of host publication2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages222-223
Number of pages2
ISBN (Electronic)9781509019007
DOIs
StatePublished - Aug 22 2016
Event2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 - Newport Beach, United States
Duration: Jul 11 2016Jul 13 2016

Publication series

Name2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016

Other

Other2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016
CountryUnited States
CityNewport Beach
Period7/11/167/13/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

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    De Lima, T. F., Shastri, B. J., Nahmias, M. A., Tait, A. N., & Prucnal, P. R. (2016). Physical modeling of photonic neural networks. In 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 (pp. 222-223). [7548808] (2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHOSST.2016.7548808