GoodFloorplan: Graph Convolutional Network and Reinforcement Learning-Based Floorplanning

Abstract

Electronic design automation (EDA) comprises a series of computationally difficult optimization problems that require substantial specialized knowledge as well as a considerable amount of trial-and-error efforts. However, open challenges, including long simulation runtime and lack of generalization, continue to restrict the applications of the existing EDA tools. Recently, learning-based algorithms, especially reinforcement learning (RL), have been successfully applied to handle various combinatorial optimization problems by automatically acquiring knowledge from the past experience. In this article, we formulate the floorplanning problem, the first stage of the physical design flow, as a Markov decision process (MDP). An end-to-end learning-based floorplanning framework GoodFloorplan is proposed to explore the design space, which combines graph convolutional network (GCN) and RL. Experimental results demonstrate that compared with state-of-the-art heuristic-based floorplanners, the proposed GoodFloorplan can provide better area and wirelength.