arXiv:2604.02811cs.ARcs.AI2026-04中稿 · DAC 2026被引 7

用专用大模型自动生成硬件验证断言,准确率超96%。

ChatSVA: Bridging SVA Generation for Hardware Verification via Task-Specific LLMs

论文配图:ChatSVA: Bridging SVA Generation for Hardware Verification via Task-Specific LLMs
图 1 · 摘自论文原文
  • 基于多智能体框架构建数据生成系统,解决领域数据稀缺问题。
  • 在24个RTL设计中实现98.66%语法通过率、96.12%功能通过率。
  • 适合芯片验证工程师快速生成高质量断言,提升调试效率。

功能验证占集成电路开发周期的50%以上,SystemVerilog断言(SVAs)是形式化属性验证和增强仿真调试的关键。然而,手动编写SVAs耗时且易出错。尽管大语言模型(LLMs)有潜力,但其直接应用受限于功能准确性低及领域数据严重匮乏。为此,我们提出ChatSVA,一个基于多智能体框架的端到端SVA生成系统。核心的AgentBridge平台通过系统化生成高纯度数据集,克服了少样本场景下的数据稀缺问题。在24个RTL设计上评估,ChatSVA达到98.66%语法通过率和96.12%功能通过率,每设计生成139.5条SVAs,功能覆盖率达82.50%。相比前代最优方法,功能正确率提升33.3个百分点,功能覆盖提升超11倍。ChatSVA不仅创下自动化SVA生成新纪录,还为少样本、领域特定的长链推理问题提供了稳健框架。在线服务已公开发布:https://www.nctieda.com/CHATDV.html。

原文摘要 · Abstract (English)

Functional verification consumes over 50% of the IC development lifecycle, where SystemVerilog Assertions (SVAs) are indispensable for formal property verification and enhanced simulation-based debugging. However, manual SVA authoring is labor-intensive and error-prone. While Large Language Models (LLMs) show promise, their direct deployment is hindered by low functional accuracy and a severe scarcity of domain-specific data. To address these challenges, we introduce ChatSVA, an end-to-end SVA generation system built upon a multi-agent framework. At its core, the AgentBridge platform enables this multi-agent approach by systematically generating high-purity datasets, overcoming the data scarcity inherent to few-shot scenarios. Evaluated on 24 RTL designs, ChatSVA achieves 98.66% syntax and 96.12% functional pass rates, generating 139.5 SVAs per design with 82.50% function coverage. This represents a 33.3 percentage point improvement in functional correctness and an over 11x enhancement in function coverage compared to the previous state-of-the-art (SOTA). ChatSVA not only sets a new SOTA in automated SVA generation but also establishes a robust framework for solving long-chain reasoning problems in few-shot, domain-specific scenarios. An online service has been publicly released at https://www.nctieda.com/CHATDV.html.

硬件验证大模型断言生成少样本学习

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