Learning placement order for constructive floorplanning

Abstract

Floorplanning is an early and essential task of physical design. Recently, there has been a surge in the application of learning-based methods to tackle floorplanning problem. A prevalent approach involves training a reinforcement learning (RL) agent to sequentially place blocks on a chip canvas. However, existing methods mainly focus on learning block placement, relying on heuristic rules for placement order determination. In contrast to previous approaches, we propose an RL-based method to determine the placement order. Based on block features and states, an agent is trained to select the block for placement. Once a block is selected, we enumerate all potential relative positions captured by sequence pairs and select the optimal placement. After establishing the layout topology, we further optimize wirelength through linear programming. Experimental results demonstrate the effectiveness of our proposed method. On the original-outline MCNC benchmarks, our method achieves a notable 25.2% average improvement in wirelength compared to a recent learning-based method. Additionally, when applied to rescaled-outline benchmarks from MCNC and GSRC, our method outperforms state-of-the-art results, resulting in an average wirelength reduction of 12.5%.