ChatModel: Automating Reference Model Design and Verification with LLMs
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Abstract (arXiv)
As the complexity of integrated circuit designs continues to escalate, functional verification becomes increasingly challenging. Reference models, critical for accelerating the verification process, are themselves becoming more intricate and time-consuming to develop. Despite the promise shown by large language models (LLMs) in code programming, effectively generating complex reference models remains a significant hurdle. Therefore, we introduce ChatModel, an LLM-aided agile reference model generation and verification platform. ChatModel streamlines the transition from design specifications to fully functional reference models by integrating design standardization and hierarchical agile modeling. Employing a building-block generation strategy, it not only enhances the design capabilities of LLMs for reference models but also significantly boosts verification efficiency. We evaluated ChatModel on 300 designs of varying complexity, demonstrating substantial improvements in both efficiency and quality of reference model generation. ChatModel achieved a peak performance improvement of 58.99% compared to alternative methods, with notable enhancements in generation stability, and delivered a 9.18x increase in its capacity to produce reference model designs. Moreover, ChatModel accelerates the reference model design and validation cycles by an average of 7.11x over traditional manual approaches. These results highlight the potential of ChatModel to significantly advance the automation of reference model generation and validation.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jun 2025.
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Citing papers per year: 2025: 4; 2026: 2.
6 papers.
- 2025TokenPowerBench: Benchmarking the Power Consumption of LLM Inferencecited by 22
- 2025Revolution or Hype? Seeking the Limits of Large Models in Hardware Designcited by 9
- 2025Large Language Model for Verilog Code Generation: Literature Review and the Road Aheadcited by 8
- 2025ChipMind: Retrieval-Augmented Reasoning for Long-Context Circuit Design Specificationscited by 4
- 2026RefEvo: Agentic Design with Co-Evolutionary Verification for Agile Reference Model Generationcited by 1
- 2026BEHAVE: Functional Behavior Modeling Enables Self-Improving Agents for Hardware Design and Verificationcited by 0
- 2026BEHAVE: Functional Behavior Modeling Enables Self-Improving Agents for Hardware Design and Verification28 Sep 2026
- 2026RefEvo: Agentic Design with Co-Evolutionary Verification for Agile Reference Model Generation27 Apr 2026
- 2025ChipMind: Retrieval-Augmented Reasoning for Long-Context Circuit Design Specifications5 Dec 2025
- 2025TokenPowerBench: Benchmarking the Power Consumption of LLM Inference2 Dec 2025
- 2025Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead29 Oct 2025
- 2025Revolution or Hype? Seeking the Limits of Large Models in Hardware Design5 Sep 2025