Explaining and interpreting machine learning CAD decisions: An IC testing case study

Prashanth Krishnamurthy, Animesh Basak Chowdhury, Benjamin Tan, Farshad Khorrami, Ramesh Karri

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

Abstract

We provide a methodology to explain and interpret machine learning decisions in Computer-Aided Design (CAD) flows. We demonstrate the efficacy of the methodology to the VLSI testing case. Such a tool will provide designers with insight into the "black box" machine learning models/classifiers through human readable sentences based on normally understood design rules or new design rules. The methodology builds on an intrinsically explainable, rule-based ML framework, called Sentences in Feature Subsets (SiFS), to mine human readable decision rules from empirical data sets. SiFS derives decision rules as compact Boolean logic sentences involving subsets of features in the input data. The approach is applied to test point insertion problem in circuits and compared to the ground truth and traditional design rules.

Original languageEnglish (US)
Title of host publicationMLCAD 2020 - Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD
PublisherAssociation for Computing Machinery, Inc
Pages129-134
Number of pages6
ISBN (Electronic)9781450375191
DOIs
StatePublished - Nov 16 2020
Event2nd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2020 - Virtual, Online, Iceland
Duration: Nov 16 2020Nov 20 2020

Publication series

NameMLCAD 2020 - Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD

Conference

Conference2nd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2020
CountryIceland
CityVirtual, Online
Period11/16/2011/20/20

Keywords

  • IC Testing
  • Interpretable Machine Learning
  • Test-Point Insertion

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design

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