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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Veröffentlicht in:Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD S. 139 - 144
Hauptverfasser: Gal, Raviv, Simchoni, Giora, Ziv, Avi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 16.11.2020
Schlagworte:
Analytical models
clustering
Conferences
functional coverage
hole analysis
Machine learning
Servers
Solid modeling
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Zusammenfassung: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.
DOI:10.1145/3380446.3430621