RoutePlacer：一种基于图神经网络的端到端布线感知布局器
RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network
AI总结：
提出端到端布线感知布局方法RoutePlacer，通过训练定制图神经网络RouteGNN作为布线性的可微近似，实现基于梯度的布线性优化，在保持线长的同时显著降低Total Overflow。
摘要：
布局是现代芯片设计中关键且具挑战性的步骤，布线性是衡量布局质量的重要指标。当前面向布线性的布局器通常采用迭代的两阶段方法，第一阶段生成布局方案，第二阶段提供不可微的布线结果以启发式地改善方案质量。该方法阻碍了在布局过程中联合优化布线性。为解决此问题，本工作提出端到端布线感知布局方法RoutePlacer。它训练定制图神经网络RouteGNN，通过捕获并融合布局的几何与拓扑表示来高效准确预测布线性。训练好的RouteGNN作为布线性的可微近似，实现端到端基于梯度的布线性优化。此外，RouteGNN可作为即插即用替代外部布线器来改进两阶段布局器。在开源AI4EDA平台DREAMPlace上的实验表明，与最先进方法相比，RoutePlacer在保持布线线长的同时可将Total Overflow降低多达16%；将RouteGNN集成到两阶段布局器中，在不影响线长的情况下使Total Overflow减少44%。
英文摘要：
Placement is a critical and challenging step of modern chip design, with routability being an essential indicator of placement quality. Current routability-oriented placers typically apply an iterative two-stage approach, wherein the first stage generates a placement solution, and the second stage provides non-differentiable routing results to heuristically improve the solution quality. This method hinders jointly optimizing the routability aspect during placement. To address this problem, this work introduces RoutePlacer, an end-to-end routability-aware placement method. It trains RouteGNN, a customized graph neural network, to efficiently and accurately predict routability by capturing and fusing geometric and topological representations of placements. Well-trained RouteGNN then serves as a differentiable approximation of routability, enabling end-to-end gradient-based routability optimization. In addition, RouteGNN can improve two-stage placers as a plug-and-play alternative to external routers. Our experiments on DREAMPlace, an open-source AI4EDA platform, show that RoutePlacer can reduce Total Overflow by up to 16% while maintaining routed wirelength, compared to the state-of-the-art; integrating RouteGNN within two-stage placers leads to a 44% reduction in Total Overflow without compromising wirelength.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
