Using Machine Learning Clustering To Find Large Coverage Holes

Identifying large and important coverage holes is a time-consuming process that requires expertise in the design and its verification environment. This paper describes a novel machine learning-based technique for finding large coverage holes when the coverage events are individually defined. The tec...

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Published in:Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD pp. 139 - 144
Main Authors: Gal, Raviv, Simchoni, Giora, Ziv, Avi
Format: Conference Proceeding
Language:English
Published: ACM 16.11.2020
Subjects:
Analytical models
clustering
Conferences
functional coverage
hole analysis
Machine learning
Servers
Solid modeling
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Abstract Identifying large and important coverage holes is a time-consuming process that requires expertise in the design and its verification environment. This paper describes a novel machine learning-based technique for finding large coverage holes when the coverage events are individually defined. The technique is based on clustering the events according to their names and mapping the clusters into cross-products. Our proposed technique is being used in the verification of high-end servers. It has already improved the quality of coverage analysis and helped identify several environment problems.
AbstractList Identifying large and important coverage holes is a time-consuming process that requires expertise in the design and its verification environment. This paper describes a novel machine learning-based technique for finding large coverage holes when the coverage events are individually defined. The technique is based on clustering the events according to their names and mapping the clusters into cross-products. Our proposed technique is being used in the verification of high-end servers. It has already improved the quality of coverage analysis and helped identify several environment problems.
Author Gal, Raviv
Simchoni, Giora
Ziv, Avi
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  givenname: Avi
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  email: aziv@il.ibm.com
  organization: IBM Research - Haifa,Haifa,Israel
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Snippet Identifying large and important coverage holes is a time-consuming process that requires expertise in the design and its verification environment. This paper...
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StartPage 139
SubjectTerms Analytical models
clustering
Conferences
functional coverage
hole analysis
Machine learning
Servers
Solid modeling
Title Using Machine Learning Clustering To Find Large Coverage Holes
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