Silicon Photonic Neural Networks and Applications

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

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

Abstract

Neuromorphic photonic processors promise orders of magnitude improvements in both speed and energy efficiency over purely digital electronic approaches. We will provide an overview of neuromorphic photonic systems and their application to machine learning and specifically deep learning inference with a hybrid digital electronics and analog photonics architecture based on silicon photonics. We will discuss scalability in the context of designing a full-scale neuromorphic photonic processing system, considering aspects such as signal integrity, noise, and hardware fabrication platforms.

Original languageEnglish (US)
Title of host publication2020 Photonics North, PN 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728181080
DOIs
StatePublished - May 2020
Event2020 Photonics North, PN 2020 - Niagara Falls, Canada
Duration: May 26 2020May 28 2020

Publication series

Name2020 Photonics North, PN 2020

Conference

Conference2020 Photonics North, PN 2020
CountryCanada
CityNiagara Falls
Period5/26/205/28/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

Keywords

  • Silicon photonics
  • machine learning
  • neuromorphic computing
  • optical computing
  • optical neural networks

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