Presentation
TDH-GNN: An Efficient Topology-Driven Accelerator for Dynamic Heterogeneous GNN
DescriptionDynamic Heterogeneous Graph Neural Networks(DHGNNs) effectively capture complex and evolving structural and semantic information through metapath learning. Despite notable advancements, current solutions still suffer from redundant metapath matching. To overcome these challenges, we introduce TDH-GNN, a topology-driven accelerator tailored for high-performance DHGNN inference. Specifically, we propose an efficient hyperedge-centric incremental execution approach into accelerator design, utilizing the concept of hyperedge to encapsulate dependencies among metapath instances, enabling incremental metapath updates and reducing redundant matching and computation. Implemented on a Xilinx U280, TDH-GNN delivers average speedups of 4.3x and 2.8x, along with 5.9x and 3.7x energy savings, over leading HGNN accelerators.
Event Type
Research Manuscript
TimeMonday, July 2710:43am - 10:56am PDT
LocationMtg Room 101B