FastTuner: Transferable Physical Design Parameter Optimization using Fast Reinforcement Learning

Current state-of-the-art Design Space Exploration (DSE) methods in Physical Design (PD), including Bayesian optimization (BO) and Ant Colony Optimization (ACO), mainly rely on black-boxed rather than parametric (e.g., neural networks) approaches to improve end-of-flow Power, Performance, and Area (PPA) metrics, which often fail to generalize across unseen designs as netlist features are not properly leveraged. To overcome this issue, in this paper, we develop a Reinforcement Learning (RL) agent that leverages Graph Neural Networks (GNNs) and Transformers to perform "fast" DSE on unseen designs by sequentially encoding netlist features across different PD stages. Particularly, an attention-based encoder-decoder framework is devised for "conditional" parameter tuning, and a PPA estimator is introduced to predict end-of-flow PPA metrics for RL reward estimation. Extensive studies across 7 industrial designs under the TSMC 28nm technology node demonstrate that the proposed framework FastTuner, significantly outperforms existing state-of-the-art DSE techniques in both optimization quality and runtime. where we observe improvements up to 79.38% in Total Negative Slack (TNS), 12.22% in total power, and 50x in runtime.