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

TL;DR

Arcane is a novel assertion reduction framework that combines semantic clustering and MCTS-guided rule exploration to significantly reduce assertions and accelerate hardware simulation without losing coverage.

Contribution

It introduces a two-tier semantic clustering and MCTS-based rule exploration approach for assertion reduction, improving efficiency and effectiveness over existing methods.

Findings

Achieves up to 76.2% assertion reduction while maintaining coverage.

Speeds up simulation time by 2.6x to 6.1x.

Successfully applied on Assertionbench with positive results.

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/.