Bitwise Neural Network Acceleration Using Silicon Photonics

Kyle Shiflett, Avinash Karanth, Ahmed Louri, Razvan Bunescu

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

Abstract

Hardware accelerators provide significant speedup and improve energy efficiency for several demanding deep neural network (DNN) applications. DNNs have several hidden layers that perform concurrent matrix-vector multiplications (MVMs) between the network weights and input features. As MVMs are critical to the performance of DNNs, previous research has optimized the performance and energy efficiency of MVMs at both the architecture and algorithm levels. In this paper, we propose to use emerging silicon photonics technology to improve parallelism, speed and overall efficiency with the goal of providing real-time inference and fast training of neural nets. We use microring resonators (MRRs) and Mach-Zehnder interferometers (MZIs) to design two versions (all-optical and partial-optical) of hybrid matrix multiplications for DNNs. Our results indicate that our partial optical design gave the best performance in both energy efficiency and latency, with a reduction of 33.1% for energy-delay product (EDP) with conservative estimates and a 76.4% reduction for EDP with aggressive estimates.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2021 - Proceedings of the 2021 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Pages9-14
Number of pages6
ISBN (Electronic)9781450383936
DOIs
StatePublished - Jun 22 2021
Externally publishedYes
Event31st Great Lakes Symposium on VLSI, GLSVLSI 2021 - Virtual, Online, United States
Duration: Jun 22 2021Jun 25 2021

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference31st Great Lakes Symposium on VLSI, GLSVLSI 2021
Country/TerritoryUnited States
CityVirtual, Online
Period6/22/216/25/21

Keywords

  • deep neural networks
  • optical signal processing
  • silicon photonics

ASJC Scopus subject areas

  • Engineering(all)

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