A Multi-Layer Topologically Reconfigurable Broadcast-and-Weight Photonic Neural Network

  • Joshua C. Lederman
  • , Yusuf Jimoh
  • , Simon Bilodeau
  • , Weipeng Zhang
  • , Eric C. Blow
  • , Thomas Ferreira De Lima
  • , Bhavin J. Shastri
  • , Paul R. Prucnal

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

Abstract

Broadcast-and-weight (BaW) photonic neural networks can process high-bandwidth signals with limited chip area, but they traditionally lack topological reconfigurability. We propose using a fully-connected recurrent BaW system as a topologically reconfigurable network and demonstrate a multi-layer feedforward network implemented on such a system.

Original languageEnglish (US)
Title of host publication2023 IEEE Photonics Conference, IPC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350347227
DOIs
StatePublished - 2023
Event2023 IEEE Photonics Conference, IPC 2023 - Orlando, United States
Duration: Nov 12 2023Nov 16 2023

Publication series

Name2023 IEEE Photonics Conference, IPC 2023 - Proceedings

Conference

Conference2023 IEEE Photonics Conference, IPC 2023
Country/TerritoryUnited States
CityOrlando
Period11/12/2311/16/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Atomic and Molecular Physics, and Optics

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