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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.02510 |
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| _version_ | 1866914857216901120 |
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| author | Zheng, Xuan Blackmore, Tim Buckingham, James Eder, Kerstin |
| author_facet | Zheng, Xuan Blackmore, Tim Buckingham, James Eder, Kerstin |
| contents | Novel test selectors have demonstrated their effectiveness in accelerating the closure of functional coverage for various industrial digital designs in simulation-based verification. The primary advantages of these test selectors include performance that is not impacted by coverage holes, straightforward implementation, and relatively low computational expense. However, the detection of stimuli with novel temporal patterns remains largely unexplored. This paper introduces two novel test selectors designed to identify such stimuli. The experiments reveal that both test selectors can accelerate the functional coverage for a commercial bus bridge, compared to random test selection. Specifically, one selector achieves a 26.9\% reduction in the number of simulated tests required to reach 98.5\% coverage, outperforming the savings achieved by two previously published test selectors by factors of 13 and 2.68, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02510 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure Zheng, Xuan Blackmore, Tim Buckingham, James Eder, Kerstin Software Engineering Machine Learning Novel test selectors have demonstrated their effectiveness in accelerating the closure of functional coverage for various industrial digital designs in simulation-based verification. The primary advantages of these test selectors include performance that is not impacted by coverage holes, straightforward implementation, and relatively low computational expense. However, the detection of stimuli with novel temporal patterns remains largely unexplored. This paper introduces two novel test selectors designed to identify such stimuli. The experiments reveal that both test selectors can accelerate the functional coverage for a commercial bus bridge, compared to random test selection. Specifically, one selector achieves a 26.9\% reduction in the number of simulated tests required to reach 98.5\% coverage, outperforming the savings achieved by two previously published test selectors by factors of 13 and 2.68, respectively. |
| title | Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2407.02510 |