Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring
📰 ArXiv cs.AI
Learn how Arcane reduces redundant assertions in hardware design verification using semantic clustering and MCTS-guided rule exploration, improving simulation efficiency
Action Steps
- Apply semantic clustering to group similar assertions
- Use MCTS-guided rule exploration to identify redundant assertions
- Implement Arcane framework to reduce assertion set
- Evaluate simulation efficiency gains
- Integrate Arcane with existing ABV tools
Who Needs to Know This
This research benefits verification engineers and hardware designers who need to optimize their assertion-based verification workflows, reducing simulation time and improving overall design quality
Key Insight
💡 Arcane framework combines semantic clustering and MCTS-guided rule exploration to efficiently reduce redundant assertions, improving simulation efficiency in hardware design verification
Share This
🚀 Arcane: reducing redundant assertions in hardware design verification with semantic clustering & MCTS-guided rule exploration 💡
Key Takeaways
Learn how Arcane reduces redundant assertions in hardware design verification using semantic clustering and MCTS-guided rule exploration, improving simulation efficiency
Full Article
Title: Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring
Abstract:
arXiv:2605.10107v1 Announce Type: new 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 fr
Abstract:
arXiv:2605.10107v1 Announce Type: new 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 fr
DeepCamp AI