MdpoPlanner: Mask-Driven Floorplan via Reinforcement Learning-Based Placement Order

Floorplanning has long been a critical task in physical design due to the large search space. Recent approaches have made progress by leveraging reinforcement learning (RL) to guide the sequential placement of blocks on the chip canvas. However, these methods remain susceptible to suboptimal solutions, as the placement order is still determined by handcrafted heuristic rules rather than jointly optimized with the placement decisions. This paper proposes a reinforcement learning based mask-driven floorplanner called MdpoPlanner, enabling joint optimization of placement sequence and spatial arrangement. Based on the connection between blocks and layout, an agent is trained to identify the optimal sequence of blocks for placement. Once a macro is selected, the proposed mask-driven floorplanner determines its optimal location by encoding spatial constraints and inter-macro connectivity into a dynamic mask, enabling legal placements while minimizing wire length and enhancing overall floorplan quality. Compared to the state-of-the-art floorplanner, MdpoPlanner yielded an average HPWL improvement of 42.61% on fixed-outline MCNC benchmarks and 6.75% on rescaled-outline GSRC benchmarks.