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English(EN) NeuroAbs: A Neuro-Symbolic RTL Abstraction Framework for Property Checking Acceleration

新的神经符号化框架提高了硬件验证效率

两篇新研究论文介绍了一种用于加速硬件验证的新型神经符号化框架。第一篇,NeuroAssertion,使用 LLM 和形式化方法来生成更全面、更可靠的 RTL 断言,与传统方法相比,断言数量和变异覆盖率提高了一倍。第二篇,NeuroAbs,采用 LLM 辅助分析和可满足性模理论 (SMT) 来创建 RTL 设计的抽象,通过反例引导的细化显著提高了属性检查的效率。 AI

影响 这些框架可能通过利用 AI 进行断言生成和抽象来显著加快硬件设计验证的速度,从而有可能缩短开发周期和发现错误的时间。

排序理由 两篇在 arXiv 上发表的学术论文,介绍了用于硬件验证的新型框架。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的神经符号化框架提高了硬件验证效率

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhiyuan Yan, Ziyue Zheng, Hongce Zhang ·

    基于覆盖率驱动的 RTL 断言生成,结合形式化探索与神经符号精炼

    arXiv:2608.18482v1 Announce Type: cross Abstract: Hardware functional verification relies on high-quality assertions to expose design bugs and establish confidence in Register Transfer Level (RTL) designs. Yet existing assertion mining methods still struggle to produce complete a…

  2. arXiv cs.AI TIER_1 English(EN) · Zhiyuan Yan, Xiaofeng Zhou, Ziyue Zheng, Ziyi Yang, Wenbin Che, Wei Zhang, Yangdi Lyu, Hongce Zhang ·

    NeuroAbs:用于属性检查加速的神经符号RTL抽象框架

    arXiv:2608.17304v1 Announce Type: cross Abstract: Formal verification is a crucial technique for ensuring the functional correctness of hardware designs. In the context of property checking, a key challenge is how to efficiently prove a user-specified property in the face of incr…