Demonstration of a silicon photonic neural network

Alexander N. Tait, Ellen Zhou, Allie X. Wu, Mitchell A. Nahmias, Thomas Ferreira De Lima, Bhavin J. Shastri, Paul R. Prucnal

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

2 Scopus citations

Abstract

Silicon photonic integration could enable high-performance brain-inspired photonic processors. We demonstrate a 3 node recurrent photonic neural network. Cusp and Hopf bifurcations induced by synaptic reconfiguration are shown as proof-of-concept. The prototype represents an early step towards network-based models of physical computing with integrated photonics.

Original languageEnglish (US)
Title of host publication2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages72-73
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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    Tait, A. N., Zhou, E., Wu, A. X., Nahmias, M. A., De Lima, T. F., Shastri, B. J., & Prucnal, P. R. (2016). Demonstration of a silicon photonic neural network. In 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 (pp. 72-73). [7548726] (2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHOSST.2016.7548726