Late Breaking Results: RL-LPO: Reinforcement Learning Based Leakage Power Optimization Framework with Graph Neural Network

Leakage power optimization based on threshold voltage (V
th
) assignment poses great challenge in circuit design due to its tremendous solution space. In this paper, a Reinforcement Learning-based Leakage Power Optimization framework (RL-LPO) is first-ever proposed to formulate V
th
assignment as a reinforcement learning (RL) process by learning timing and physical characteristics of each circuit instance with Graph Neural Networks (GNN). The proposed RL-LPO was validated by the IWLS2005 and Opencores benchmark circuits with TSMC 28nm technology and experimental results demonstrate that our work achieves better leakage power optimization by additional 3% reduction on average than the commercial tool PrimeTime with 6.7× speed up when being transferred to unseen circuits with negligible timing degradation.