发表机构
University of Alberta; New York University（阿尔伯塔大学; 纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对分析布局器替代指标与下游质量不一致问题，提出基于大语言模型的CoEvoP&R框架自动进化布局目标，经实验验证该方法能有效降低线长、拥塞及时序负松弛，提升布局效果。
AI 中文摘要
分析布局器依靠可微目标函数指导布局，常结合半周长线长（HPWL）和单元密度惩罚等中间替代指标。但这些布局阶段的替代指标与下游布线和时序质量仍不一致。先前工作用人工设计术语或学习的黑箱替代指标缩小差距，但前者需专家重新调整，后者难以解释、调试或应用于分析布局流程。CoEvoP&R用基于大语言模型的框架解决这些局限，自动进化分析布局目标。在每一代，提示结合受限目标接口、基线上下文、存档的先前候选方案以及来自布局、时序代理和布线工具的与路由相关的反馈。大语言模型提出可读的可微目标，在DREAMPlace中嵌入并验证，通过时序代理和实际布线器评估，并存储其反馈以指导后代。在八个ChiP-Bench Nangate45设计和三个种子上，CoEvoP&R使布线后线长和拥塞分别降低16.9%和36.7%……
英文摘要
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 routed and timing quality. Prior work reduces this gap with human-designed terms or learned black-box surrogates, but the former requires expert retuning and the latter is difficult to explain, debug, or deploy in analytical placement flows. CoEvoP&R addresses these limitations with a large language model (LLM)-based framework that automatically evolves analytical placement objectives. At each generation, the prompt combines the restricted objective interface, baseline context, and archived prior candidates with routing-related feedback from placement, timing proxy, and routing tools. The LLM proposes readable differentiable objectives, which are embedded and validated in DREAMPlace, evaluated through a timing proxy and an actual router, and stored with their feedback to guide later generations. Across eight ChiP-Bench Nangate45 designs and three seeds, CoEvoP&R reduces post-route routed wirelength and congestion by 16.9% and 36.7%, with gains of 0.70 ns in worst negative slack and a 912 ns reduction in total negative slack magnitude over native DREAMPlace. Across eight ICCAD 2015 Superblue designs, it reduces post-route routed wirelength and congestion by 5.4% and 23.2%. Code is available at https://github.com/FCHXWH823/CoEvoP-R.git.
Comments7 pages, 4 figures, 3 tables. Corresponding author: Weihua Xiao
