IR-Aware ECO Timing Optimization Using Reinforcement Learning
This work addresses timing optimization for electronic design automation, specifically for engineers dealing with ECOs, but it is incremental as it builds on existing methods like Lagrangian relaxation with a novel RL framework.
The paper tackles the problem of timing degradation due to IR drops in late-stage engineering change orders (ECOs) by integrating IR-drop-aware timing analysis with reinforcement learning for gate sizing optimization. It results in improved delay-power tradeoffs, runtime savings, and reduced placement perturbation compared to classical methods, with specific gains demonstrated in a 45nm technology.
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-power tradeoff curve to the left (b) saves runtime over the prior approaches by running fast inference using trained models, and (c) reduces the perturbation to placement by sizing fewer cells. The RL model is transferable across timing specifications and to unseen designs with fine tuning.