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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.00688 |
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| _version_ | 1866915645388488704 |
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| author | Peng, Weijie Li, Nanbing Luo, Jin Wang, Shuai Li, Yihui Fang, Jun Yun Liang |
| author_facet | Peng, Weijie Li, Nanbing Luo, Jin Wang, Shuai Li, Yihui Fang, Jun Yun Liang |
| contents | Functional verification relies on large simulation-based regressions. Traditional test selection relies on static test features and overlooks actual coverage behavior, wasting substantial simulation time, while constrained random stimuli generation depends on manually crafted distributions that are difficult to design and often ineffective. We present NOVA, a framework that coordinates coverage-aware test selection with Bayes-optimized constrained randomization. NOVA extracts fine-grained coverage features to filter redundant tests and modifies the constraint solver to expose parameterized decision strategies whose settings are tuned via Bayesian optimization to maximize coverage growth. Across multiple RTL designs, NOVA achieves up to a 2.82$\times$ coverage convergence speedup without requiring human-crafted heuristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NOVA: Coordinated Test Selection and Bayes-Optimized Constrained Randomization for Accelerated Coverage Closure Peng, Weijie Li, Nanbing Luo, Jin Wang, Shuai Li, Yihui Fang, Jun Yun Liang Methodology Hardware Architecture Functional verification relies on large simulation-based regressions. Traditional test selection relies on static test features and overlooks actual coverage behavior, wasting substantial simulation time, while constrained random stimuli generation depends on manually crafted distributions that are difficult to design and often ineffective. We present NOVA, a framework that coordinates coverage-aware test selection with Bayes-optimized constrained randomization. NOVA extracts fine-grained coverage features to filter redundant tests and modifies the constraint solver to expose parameterized decision strategies whose settings are tuned via Bayesian optimization to maximize coverage growth. Across multiple RTL designs, NOVA achieves up to a 2.82$\times$ coverage convergence speedup without requiring human-crafted heuristics. |
| title | NOVA: Coordinated Test Selection and Bayes-Optimized Constrained Randomization for Accelerated Coverage Closure |
| topic | Methodology Hardware Architecture |
| url | https://arxiv.org/abs/2512.00688 |