Flexible Multiple-Objective Reinforcement Learning for Chip Placement
This is an incremental improvement for chip design engineers, enabling adaptable placements without retraining.
The paper tackles the problem of generating diverse chip placements under varying objective weights by proposing flexible multiple-objective reinforcement learning, which uses a single pretrained model to produce a Pareto frontier for metrics like wirelength and congestion.
Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, congestion, and timing) are fixed during pretraining. However, fixed-weighed models cannot generate the diversity of placements required for engineers to accommodate changing requirements as they arise. This paper proposes flexible multiple-objective reinforcement learning (MORL) to support objective functions with inference-time variable weights using just a single pretrained model. Our macro placement results show that MORL can generate the Pareto frontier of multiple objectives effectively.