English(EN) EGGPlacer: Energy-Guided Generative Chip Placement with Diffusion Models

能量引导的扩散模型提升芯片布局效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了EGGPlacer，一个利用能量反馈引导的扩散模型的创新芯片布局框架。该方法整合了分析布局和热模型来优化宏单元布局，解决了现有方法常忽略标准单元连通性和热效应的局限性。在公开基准上的实验表明，EGGPlacer在宏单元半周长线长（mHPWL）方面取得了有竞争力的结果，并在布线线长和峰值温度的端到端评估中优于其他方法。 AI

影响这项研究可能通过在布局阶段更好地优化线长和热行为，从而实现更高效的芯片设计。

排序理由该项目是一篇研究论文，详细介绍了一种使用扩散模型进行芯片布局的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Qi Zhou, Guojun Liu, Juanhong Zhao, Guangzhi Qi, Zehao Chen, Ming Lu · 2026-10-27 04:00

EGGPlacer：基于扩散模型的能量引导生成式芯片布局

arXiv:2610.33099v1 Announce Type: cross Abstract: Chip placement strongly affects routed wirelength and thermal behavior, yet these downstream metrics are difficult to evaluate during macro placement. Learning-based macro placers commonly optimize the macro-only half-perimeter wi…

本文如何被排名

报道来源 [1]

EGGPlacer：基于扩散模型的能量引导生成式芯片布局

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