Late Breaking Results: Customized Diffusion Model Empowered by Heterogeneous Graph Network for Effective Floorplanning

Floorplanning is a critical phase in VLSI physical design, focusing on determining block positions while optimizing wirelength under specified area constraints. However, classical analytical-based floorplanners are highly sensitive to the quality of initial solutions and existing learningbased methods often suffer from high computational inefficiency and complexity. In this paper, we propose a customized diffusion model to directly generate high-quality initial floorplans. By leveraging a classical analytical-based floorplanner on top of this initial floorplan, the final floorplanning results are significantly improved. To enhance feature extraction, a heterogeneous graph neural network (HGNN) is developed to explicitly incorporate block-to-block and pin-to-block relationships from the netlist during the diffusion process. Additionally, a novel guidance sampling function is introduced to optimize both wirelength and overlap, effectively reducing the required sampling steps while maintaining competitive initial solutions. Experimental results demonstrate that integrating our proposed diffusion model with an advanced analytical-based floorplanner achieves at least 4.8% reduction in runtime and 3.0% reduction in HPWL compared to the original floorplanner and other diffusion-based methods.