Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring
2026-05-11 • Artificial Intelligence
Artificial IntelligenceHardware Architecture
AI summaryⓘ
The authors address the problem that automatic tools for generating hardware test assertions often create many similar or unnecessary checks, which slows down testing. They introduce Arcane, a system that groups similar assertions and uses a smart search method (Monte Carlo Tree Search) to find the best way to reduce redundant assertions without losing important coverage. Their experiments show Arcane can cut the number of assertions by over 75% and speed up testing by 2.6 to 6 times while keeping accuracy. This helps make hardware verification more efficient.
Assertion-based VerificationHardware Design VerificationAssertion ReductionMonte Carlo Tree SearchAssertion ClusteringSimulation EfficiencyFormal CoverageMutation DetectionAssertionbench
Authors
Hongqin Lyu, Yonghao Wang, Zhiteng Chao, Tiancheng Wang, Huawei Li
Abstract
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/.