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
Online-Zugang:	Volltext
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