ChatSVA achieves 96.12% functional pass rate and 82.5% coverage in SVA generation on 24 RTL designs, delivering 33 percentage point gains and 11x better coverage than prior state-of-the-art.
Chip-Chat: Challenges and opportunities in conversational hardware design
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3roles
background 1polarities
background 1representative citing papers
A-IC3 uses multi-armed bandits to adaptively pick inductive generalization strategies in IC3, solving 26-50 more HWMCC cases than baselines with improved PAR-2 scores.
Workshop report recommends NSF investments in AI-EDA collaboration, data infrastructure, compute resources, and workforce development to accelerate hardware design.
citing papers explorer
-
ChatSVA: Bridging SVA Generation for Hardware Verification via Task-Specific LLMs
ChatSVA achieves 96.12% functional pass rate and 82.5% coverage in SVA generation on 24 RTL designs, delivering 33 percentage point gains and 11x better coverage than prior state-of-the-art.
-
A-IC3: Learning-Guided Adaptive Inductive Generalization for Hardware Model Checking
A-IC3 uses multi-armed bandits to adaptively pick inductive generalization strategies in IC3, solving 26-50 more HWMCC cases than baselines with improved PAR-2 scores.
-
Report for NSF Workshop on AI for Electronic Design Automation
Workshop report recommends NSF investments in AI-EDA collaboration, data infrastructure, compute resources, and workforce development to accelerate hardware design.