发表机构
New York University（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
针对高密度布局布线收敛难的问题，提出一种历史感知的离线强化学习策略，利用LSTM和额外特征预测迭代成本权重，平均减少92%设计规则违规并降低10%运行时间。
AI 中文摘要
详细布线由于设计规则日益复杂，仍然是物理设计中的主要运行时瓶颈。现代布线器在密集工作条件下可能难以解决持续存在的违规问题。虽然近期工作利用强化学习（RL）动态选择每次布线迭代的成本，但我们发现该技术在高密度设计中难以奏效，因为在这些设计中布线解决方案显著更加困难。为解决此问题，我们提出了一种历史感知的离线RL策略，该策略通过利用布线器中现成的特征，预测这些密集区域中的迭代成本权重，以改善跨布局密度的收敛性。我们的策略与先前工作类似地使用保守Q学习；然而，我们的关键洞察是，添加轻量级LSTM架构和额外特征可以保留序列上下文，并改善跨多种密度和布线引导质量的布线收敛性。我们的策略可以以最小的流水线改动集成到任何基于成本的布线器中，因为它不干扰核心搜索算法。我们在保留的密度和调整设置上评估了我们的策略，包括由密集布局和低引导质量引起的困难工作点。与顶级公开基线相比，我们的策略平均将设计规则违规（DRV）减少了92%，同时将运行时间减少了10%。
英文摘要
Detailed routing remains a dominant runtime bottleneck in physical design due to increasing complexity of design rules. Modern routers can struggle to resolve persistent violations under dense operating conditions. While recent work leverages reinforcement learning (RL) to dynamically select costs for each routing iteration, we find that this technique struggles with high-density designs where routing solutions are significantly harder. To address this, we present a history-aware offline RL policy which predicts iterative cost weights in these dense regimes to improve convergence across placement densities by utilizing readily available features from the router. Our policy uses conservative Q-learning similarly to prior work; however, our key insight is that addition of a lightweight LSTM architecture and additional features can retain sequence context and improve routing convergence across multiple densities and route guide qualities. Our policy can be integrated into any cost-based router with minimal pipeline changes, as it does not interfere with the core search algorithm. We evaluate our policy on held-out density and adjustment settings, including difficult operating points induced by dense placement and low guide quality. Our policy reduces design rule violations (DRVs) by an average of 92% over the top public baseline while simultaneously reducing runtime by 10%.
CommentsAccepted for publication at ICCAD 2026
