Engineering change orders (ECOs) in late stages make minimal design fixes to recover from timing shifts due to excessive IR drops. This paper integrates IR-drop-aware timing analysis and ECO timing optimization using reinforcement learning (RL). The method operates after physical design and power grid synthesis, and rectifies IR-drop-induced timing degradation through gate sizing. It incorporates the Lagrangian relaxation (LR) technique into a novel RL framework, which trains a relational graph convolutional network (R-GCN) agent to sequentially size gates to fix timing violations. The R-GCN agent outperforms a classical LR-only algorithm: in an open 45nm technology, it (a) moves the Pareto front of the delay-area tradeoff curve to the left and (b) saves runtime over the classical method by running fast inference using trained models at iso-quality. The RL model is transferable across timing specifications, and transferable to unseen designs with zero-shot learning or fine tuning.

翻译：工程变更单（ECO）在后期阶段通过最小限度的设计修正来恢复因过大IR压降导致的时序偏移。本文整合了考虑IR压降的时序分析与基于强化学习（RL）的ECO时序优化方法。该方法在物理设计与电源网络综合之后运行，通过门尺寸调整来修复IR压降引发的时序退化。它将拉格朗日松弛（LR）技术融入新颖的强化学习框架中，该框架训练关系图卷积网络（R-GCN）智能体以顺序调整门尺寸来修复时序违规。相较于经典纯LR算法，R-GCN智能体在开放45nm工艺下展现出优势：（a）将延迟-面积帕累托前沿向左推移；（b）通过使用训练好的模型进行快速推理，在同等质量下节省了运行时开销。该RL模型可跨时序指标迁移，并通过零样本学习或微调迁移至未见过的设计。