Security and Reliability Challenges in Machine Learning for EDA: Latest Advances

Zhiyao Xie, Yifeng Peng, Tong Zhang

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

Abstract

The growing IC complexity has led to a compelling need for design efficiency improvement through new electronic design automation (EDA) methodologies. In recent years, many innovative machine learning (ML)-based solutions have been proposed for EDA applications. While these ML solutions demonstrate great potential in the circuit design flow, however, the hidden security and model reliability problems are rarely discussed until recently. In this paper, we present some latest research advances in the security and reliability challenges in ML for EDA.

Original languageEnglish (US)
Title of host publicationProceedings of the 24th International Symposium on Quality Electronic Design, ISQED 2023
PublisherIEEE Computer Society
ISBN (Electronic)9798350334753
DOIs
StatePublished - 2023
Externally publishedYes
Event24th International Symposium on Quality Electronic Design, ISQED 2023 - San Francisco, United States
Duration: Apr 5 2023Apr 7 2023

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
Volume2023-April
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Conference

Conference24th International Symposium on Quality Electronic Design, ISQED 2023
Country/TerritoryUnited States
CitySan Francisco
Period4/5/234/7/23

ASJC Scopus subject areas

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

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