XGBIR: An XGBoost-based IR Drop Predictor for Power Delivery Network

Chi Hsien Pao, An Yu Su, Yu Min Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

This work utilizes the XGBoost to build a machine-learning-based IR drop predictor, XGBIR, for the power grid. To capture the behavior of power grid, we extract its several features and employ its locality property to save the extraction time. XGBIR can be effectively applied to large designs and the average error of predicted IR drops is less than 6 mV.

Original languageEnglish
Title of host publicationProceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
EditorsGiorgio Di Natale, Cristiana Bolchini, Elena-Ioana Vatajelu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1307-1310
Number of pages4
ISBN (Electronic)9783981926347
DOIs
StatePublished - Mar 2020
Event2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020 - Grenoble, France
Duration: 9 Mar 202013 Mar 2020

Publication series

NameProceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020

Conference

Conference2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
Country/TerritoryFrance
CityGrenoble
Period9/03/2013/03/20

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