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arxiv: 2506.15066 · v5 · pith:LIQBTGATnew · submitted 2025-06-18 · 💻 cs.AR · cs.MA

ChatModel: Automating Reference Model Design and Verification with LLMs

classification 💻 cs.AR cs.MA
keywords referencechatmodelmodeldesigngenerationmodelsverificationdesigns
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. RefEvo: Agentic Design with Co-Evolutionary Verification for Agile Reference Model Generation

    cs.SE 2026-04 unverdicted novelty 6.0

    RefEvo achieves 95% pass rate on 20 hardware modules for SystemC reference model generation using dynamic multi-agent planning, co-evolutionary verification, and spec anchoring, with 71% token reduction.

  2. ChatHLS: Towards Systematic Design Automation and Optimization for High-Level Synthesis

    cs.AR 2025-07 unverdicted novelty 6.0

    ChatHLS presents a multi-agent LLM system for HLS error debugging via adaptive case expansion and QoR-aware directive optimization, reporting 32.6% better debugging than Gemini-3-pro plus speedups on kernels and accelerators.