Bayesian Active Learning for Uncertainty Quantification of High Speed Channel Signaling

Hakki M. Torun, Jose A. Hejase, Junyan Tang, Wiren D. Beckert, Madhavan Swaminathan

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

12 Scopus citations

Abstract

Increasing data rates in server high-speed communication busses makes their performance more susceptible to uncertainties in manufacturing processes. As a result, it is essential to understand channel design limitations and performance under tolerances to ensure a robust system. Predicting channel performance under tolerances can become very straining in time and computational resources. To address this, we propose a new active learning based algorithm that starts with no training data to simultaneously derive an accurate predictive model while finding the worst case scenario to ensure channel compliance in reduced CPU time compared to conventional methods.

Original languageEnglish (US)
Title of host publicationEPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages311-313
Number of pages3
ISBN (Electronic)9781538693032
DOIs
StatePublished - Nov 13 2018
Event27th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2018 - San Jose, United States
Duration: Oct 14 2018Oct 17 2018

Publication series

NameEPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems

Conference

Conference27th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2018
Country/TerritoryUnited States
CitySan Jose
Period10/14/1810/17/18

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Electronic, Optical and Magnetic Materials

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