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English(EN) CoEvoP&R: Co-Evolving Placement Objectives with Routing Feedback via Large Language Models

LLM 演进芯片放置目标，改善布线和时序

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了 CoEvoP&R，一个利用大型语言模型 (LLM) 为分析式放置器自动演进放置目标的新框架。该方法解决了传统放置阶段的代理指标与下游布线和时序质量之间的不匹配问题。通过生成可读、可微分的目标，并利用路由反馈进行验证，CoEvoP&R 与现有方法相比，显著改善了布线后线长、拥塞和时序指标。 AI

影响 这项研究可能通过 LLM 驱动的目标演进来提高放置算法的准确性，从而实现更高效的芯片设计。

排序理由 该集群包含一篇研究论文，详细介绍了使用 LLM 演进放置目标的新方法。

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LLM 演进芯片放置目标，改善布线和时序

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Kabir Murjani, Mishri Bhavsar, Manish I. Patel, Jonti Talukdar ·

    AlphaRoute：大型语言模型作为多目标路由的语义优化器

    arXiv:2607.19768v1 Announce Type: new Abstract: Very Large Scale Integration (VLSI) global routing is an NP-hard combinatorial optimization problem requiring signal net assignment across capacity-constrained 3D grids while minimizing congestion, wirelength, and via transitions. B…

  2. arXiv cs.AI TIER_1 English(EN) · Ruogu Chen, Weihua Xiao, Ramesh Karri, Jie Han ·

    CoEvoP&R：利用大型语言模型通过路由反馈共同演进放置目标

    arXiv:2607.17398v1 Announce Type: cross Abstract: Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics such as half-perimeter wirelength (HPWL) and cell-density penalties. However, these placement-sta…

  3. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Jie Han ·

    CoEvoP&R：通过大型语言模型与路由反馈共同演进放置目标

    Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics such as half-perimeter wirelength (HPWL) and cell-density penalties. However, these placement-stage surrogates remain misaligned with downstream ro…

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