Using Machine Learning Clustering To Find Large Coverage Holes
- Resource Type
- Conference
- Authors
- Gal, Raviv; Simchoni, Giora; Ziv, Avi
- Source
- 2020 ACM/IEEE 2nd Workshop on Machine Learning for CAD (MLCAD) Machine Learning for CAD (MLCAD), 2020 ACM/IEEE 2nd Workshop on. :139-144 Nov, 2020
- Subject
- Components, Circuits, Devices and Systems
Computing and Processing
Solid modeling
Analytical models
Conferences
Machine learning
Servers
functional coverage
hole analysis
clustering
- Language
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.