Comparison of Invertible Architectures for High Speed Channel Design

Osama Waqar Bhatti, Oluwaseyi Akinwande, Nikita Ambasana, Xianbo Yang, Pavel R. Paladhi, Wiren Dale Becker, Madhavan Swaminathan

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

2 Scopus citations

Abstract

In this paper, we perform a comparison of state-of-the-art inverse design strategies for a high-speed link. We take into account three machine learning architectures: (1) traditional fully-connected neural networks, (2) conditional generative adversarial networks and (3) invertible neural networks. The metrics for evaluation include quantitative mean-squared-errors on the test set as well as qualitative posterior distributions' similarity to the actual posterior distributions. We find that, on average, invertible neural networks have minimum mean-squared error for the input design tuple and their posterior shapes are in accordance with the actual distributions.

Original languageEnglish (US)
Title of host publication2021 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665466134
DOIs
StatePublished - 2021
Event2021 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2021 - Urbana, United States
Duration: Dec 13 2021Dec 15 2021

Publication series

NameIEEE Electrical Design of Advanced Packaging and Systems Symposium
Volume2021-December
ISSN (Print)2151-1225
ISSN (Electronic)2151-1233

Conference

Conference2021 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2021
Country/TerritoryUnited States
CityUrbana
Period12/13/2112/15/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Automotive Engineering
  • General Computer Science

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