Comparison of Collaborative versus Extended Artificial Neural Networks for PDN Design

Morten Schierholz, Cheng Yang, Kallol Roy, Madhavan Swaminathan, Christian Schuster

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

4 Scopus citations

Abstract

Currently machine learning (ML) tools are not capable to provide analysis solutions for complex printed circuit boards. It is unknown how to prepare the data and how to determine the optimal architecture of the ML process. We show that both collaborative and extended artificial neural networks (ANNs) are capable to compensate drops in accuracies for predicting target impedance violations in an extended design space. It is proven that the extended ANN has the advantage of requiring less samples during the training process compared with the collaborative approach. The necessity of either approach is highly depending on the design space and the influence of the variation on the power delivery network.

Original languageEnglish (US)
Title of host publicationSPI 2020 - 24th IEEE Workshop On Signal and Power Integrity, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728142043
DOIs
StatePublished - May 2020
Event24th IEEE Workshop On Signal and Power Integrity, SPI 2020 - Cologne, Germany
Duration: May 17 2020May 20 2020

Publication series

NameSPI 2020 - 24th IEEE Workshop On Signal and Power Integrity, Proceedings

Conference

Conference24th IEEE Workshop On Signal and Power Integrity, SPI 2020
Country/TerritoryGermany
CityCologne
Period5/17/205/20/20

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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