Matrix Multiplication Unit Scalability Investigation for InP SOA-based Photonic Deep Neural Network

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Abstract

We investigate the scalability of a SOA-based photonic DNN with WDM inputs. For a 3-layer DNN, a quasi-linear dependence between the prediction accuracy and errors/layer is found for a NRMSE <0.09. Optimized passive losses can enable synaptic function at half of the power consumption.

Original languageEnglish
Title of host publication2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Number of pages2
ISBN (Electronic)9781728158877
DOIs
Publication statusPublished - Jul 2020
Event2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 - Cabo San Lucas, Mexico
Duration: 13 Jul 202015 Jul 2020

Publication series

Name2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 - Proceedings

Conference

Conference2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020
CountryMexico
CityCabo San Lucas
Period13/07/2015/07/20

Keywords

  • Artificial neural networks
  • Image classification
  • Photonic integrated circuits
  • Semiconductor optical amplifiers
  • Simulation

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