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English(EN) StateTune: Transforming LLM-Assisted EDA Flow Tuning into a Stateful, Closed-Loop Process

StateTune 通过持久化内存增强 LLM 辅助的 EDA 流调优

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 StateTune，一种优化电子设计自动化 (EDA) 流的新方法。与将 LLM 视为外部工具的先前方法不同，StateTune 将 LLM 集成到一个有状态的闭环过程中。该系统利用持久化优化内存，该内存会在每次评估时更新，并在候选生成和预算分配之间共享。StateTune 在六个基准模块上展示了卓越的性能，实现了最强的最终超体积，并在最差负松弛、面积和功耗等其他关键指标上达到或超过了基线。 AI

影响 这项研究通过实现更复杂和自动化的参数调优，有可能显著提高硬件设计的效率和有效性。

排序理由 该集群描述了一篇详细介绍 LLM 辅助 EDA 流调优新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

StateTune 通过持久化内存增强 LLM 辅助的 EDA 流调优

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该集群描述了一篇详细介绍 LLM 辅助 EDA 流调优新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Kunlong Li, Shangshang Yao, Su Zheng, Lingli Wang ·

    StateTune：将 LLM 辅助 EDA 流调优转变为有状态的闭环流程

    arXiv:2608.23601v1 Announce Type: cross Abstract: EDA flow parameter tuning is critical for quality-of-results~(QoR), yet the parameter space is large, tightly coupled, and full evaluations are prohibitively expensive. Prior LLM-assisted tuners mainly use the LLM as an external p…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Lingli Wang ·

    StateTune：将 LLM 辅助 EDA 流调优转变为有状态的闭环流程

    EDA flow parameter tuning is critical for quality-of-results~(QoR), yet the parameter space is large, tightly coupled, and full evaluations are prohibitively expensive. Prior LLM-assisted tuners mainly use the LLM as an external proposer with transient working context; we instead…

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