Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring

2026-05-11

AI Summary

This work addresses the inefficiency in hardware verification caused by excessive redundancy in automatically generated assertions, which severely degrades simulation performance. To tackle this challenge, the authors propose Arcane, a novel framework that uniquely integrates semantic clustering with Monte Carlo Tree Search (MCTS). Arcane first applies a two-level semantic clustering technique to precisely group semantically similar assertions and then employs MCTS to explore an optimal sequence of assertion reduction rules. Experimental evaluation on AssertionBench demonstrates that Arcane reduces the number of assertions by up to 76.2%, accelerates simulation by 2.6× to 6.1×, and fully preserves formal coverage and bug-detection capabilities.

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/.

Problem

• Assertion-based Verification
• Redundant Assertions
• Simulation Efficiency
• Assertion Reduction

Innovation

• Assertion Reduction
• Semantic Clustering
• Monte Carlo Tree Search (MCTS)
• Assertion-based Verification
• Rule Exploration