Presentation
Late Breaking Results: Customized Diffusion Model Empowered by Heterogeneous Graph Network for Effective Floorplanning
DescriptionIn 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.
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.
Event Type
Late Breaking Results
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby