Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918494067490816 |
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| author | Lyu, Hongqin Wang, Yonghao Chao, Zhiteng Wang, Tiancheng Li, Huawei |
| author_facet | Lyu, Hongqin Wang, Yonghao Chao, Zhiteng Wang, Tiancheng Li, Huawei |
| contents | Assertion-based Verification (ABV) is essential for ensuring that hardware designs conform to their intended specifications. However, existing automated assertion-generation approaches, such as LLM-based frameworks, often generate large numbers of redundant assertions, which significantly degrade simulation efficiency. To mitigate the simulation overhead caused by redundant assertions, this paper proposes Arcane, an efficient assertion reduction framework. It integrates a two-tier assertion clustering approach for accurate semantic classification of large assertion sets, and employs Monte Carlo Tree Search (MCTS) to explore optimal rule-application sequences for efficient assertion reduction. The experimental results on Assertionbench [20] show that Arcane achieves a reduction of up to 76.2% in the assertion count while fully preserving formal coverage and mutation-detection ability. Further simulation studies demonstrate a speedup of 2.6x to 6.1x speedup in simulation time. The proposed framework is released at https://anonymous.4open.science/r/Arcane1-0A6F/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10107 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring Lyu, Hongqin Wang, Yonghao Chao, Zhiteng Wang, Tiancheng Li, Huawei Artificial Intelligence Hardware Architecture Assertion-based Verification (ABV) is essential for ensuring that hardware designs conform to their intended specifications. However, existing automated assertion-generation approaches, such as LLM-based frameworks, often generate large numbers of redundant assertions, which significantly degrade simulation efficiency. To mitigate the simulation overhead caused by redundant assertions, this paper proposes Arcane, an efficient assertion reduction framework. It integrates a two-tier assertion clustering approach for accurate semantic classification of large assertion sets, and employs Monte Carlo Tree Search (MCTS) to explore optimal rule-application sequences for efficient assertion reduction. The experimental results on Assertionbench [20] show that Arcane achieves a reduction of up to 76.2% in the assertion count while fully preserving formal coverage and mutation-detection ability. Further simulation studies demonstrate a speedup of 2.6x to 6.1x speedup in simulation time. The proposed framework is released at https://anonymous.4open.science/r/Arcane1-0A6F/. |
| title | Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring |
| topic | Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2605.10107 |