Miracle: Multi-Action Reinforcement Learning-Based Chip Floorplanning Reasoner

Floorplanning is one of the most critical but time-consuming tasks in the chip design process. Machine learning techniques, especially reinforcement learning, have provided a promising direction for floorplanning design. In this paper, an end-to-end reinforcement learning (RL) framework is proposed to learn a policy for floorplanning automatically, in the combination of edge-augmented graph attention network (EGAT), position-wise multi-layer perceptron, and gated self-attention mechanism. We formulate floorplanning as a Markov Decision Process (MDP) model, where a multi-action mechanism and a dense reward function are developed to adapt the floorplanning problem. In addition, in order to make full use of prior knowledge, we further propose a supervised learning approach on the generated synthetic netlist-floorplan dataset. Experimental results demonstrate that, compared with state-of-the-art floorplanners, the proposed end-to-end framework significantly reduces wirelength with a smaller area.