Invertible Neural Networks for High-Speed Channel Design Parameter Distribution Estimation

Nikita Ambasana, Osama W. Bhatti, Majid A. Dolatsara, Madhavan Swaminathan, Xianbo Yang, Pavel R. Paladhi, Wiren Dale Becker

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

4 Scopus citations

Abstract

High-speed channels have complex topologies with large number of design and process variables. Design and analysis of such channels, for given specifications has been a challenge for the signal integrity community for a while. In this work, we present the use of a unique architecture of invertible neural networks (INN) for predicting the joint posterior distributions in the channel design space, conditioned on the channel passing given specifications in eye-height and eye-width. To provide a proof-of-concept, we perform a full-factorial channel simulation on a 16 Gbps differential pair channel with five variables to calculate the joint posterior distributions for the channel parameters. We then compare them to the ones obtained by the INN. Such an inverse design technique gives a fast channel model when used in the forward direction and a joint posterior distribution estimator when used in the reverse direction for the same cost of training. Channel parameter correlations that are identified based on the estimated joint posterior distributions are also shown.

Original languageEnglish (US)
Title of host publicationEPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665442695
DOIs
StatePublished - 2021
Event30th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2021 - Austin, United States
Duration: Oct 17 2021Oct 20 2021

Publication series

NameEPEPS 2021 - IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systems

Conference

Conference30th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2021
Country/TerritoryUnited States
CityAustin
Period10/17/2110/20/21

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Electronic, Optical and Magnetic Materials
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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