Presentation
PiHG: A Redundancy-Free Heterogeneous Graph Neural Network Accelerator via a Pivot-Centric Approach
DescriptionHeterogeneous Graph Neural Networks (HGNNs) are widely used to capture structural and semantic information in heterogeneous graphs based on sequences of vertex and edge types (i.e., metapaths). To support HGNNs, several solutions have been proposed and adopt a source-centric approach that can concurrently process metapath instances sharing the same source vertex. However, the vertices and edges, which are shared by different metapath instances and beyond the first hop of the same source vertex, may be repeatedly processed, incurring irregular memory accesses and redundant edge computations. In this work, we observe that HGNN inference exhibits substantial vertex and edge overlaps across metapath instances, and the metapaths sharing a common pivot vertex (i.e., the center vertex bridging multiple metapaths) exhibit strong spatial similarity that effectively captures these overlaps. Based on these observations, we propose a pivot-centric accelerator named PiHG to effectively support HGNN inference. Specifically, PiHG introduces a novel pivot-centric execution model into accelerator design to concentrate feature accesses and computations around pivot vertices, which enables reusing the feature vectors of overlapped vertices and edges across metapaths and thus eliminating redundant computations and reducing irregular off-chip memory accesses. Experimental results show that, compared with the state-of-the-art software and hardware solutions, PiHG achieves 13.1×∼236.8×, 2.2×∼11.7× speedups and 15.5×∼216.8×, 2.4×∼12.7× energy savings, respectively.
Event Type
Research Manuscript
TimeWednesday, July 2910:30am - 10:43am PDT
LocationMtg Room 201B