Learning to Floorplan like Human Experts via Reinforcement Learning

Abstract

Deep reinforcement learning (RL) has gained popularity for automatically generating placements in modern chip design. However, the visual style of the floorplans generated by these RL models is significantly different from the manual layouts’ style, for RL placers usually only adopt metrics like wirelength and routing congestion as the reward in reinforcement learning, ignoring the complex and fine-grained layout experience of human experts. In this paper, we propose a placement scorer to rate the quality of layouts and apply abnormal detection to the floorplanning task. In addition, we add the output of this scorer as a part of the reward for reinforcement learning of the placement process. Experimental results on ISPD 2005 benchmark show that our proposed placement quality scorer can evaluate the layouts according to human craft style efficiently, and that adding this scorer into reinforcement learning reward helps generating placements with shorter wirelength than previous methods for some circuit designs.