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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| Vydáno v: | Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD s. 139 - 144 |
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16.11.2020
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Raviv surname: Gal fullname: Gal, Raviv email: ravivg@il.ibm.com organization: IBM Research - Haifa,Haifa,Israel – sequence: 2 givenname: Giora surname: Simchoni fullname: Simchoni, Giora email: gsimchoni@gmail.com – sequence: 3 givenname: Avi surname: Ziv fullname: Ziv, Avi 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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| 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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