HR-GCN: An Efficient GCN Accelerator for Heterogeneous Graph Data and R-GCN Model

GCN hardware accelerators currently cater to homogeneous graphs with single connection relationships. Real-world graphs, more diverse in node and connection relationships, benefit from heterogeneous graph models like R-GCN. However, R-GCN's complex architecture and numerous weights result in higher computational and memory demands. Customized R-GCN accelerators are scarce, and running R-GCN on existing GCN accelerators proves highly inefficient. To address this issue, a novel hardware accelerator, HR-GCN, was developed specifically for GCN inference in scenarios involving heterogeneous graph data and the R-GCN Model. First, HR-GCN adopts an adjacency matrix tile-fusing strategy for multiple connection relationships, reducing data dimensions and sparseness in sliced data. Additionally, it utilizes a hybrid inner and outer product tile strategy to significantly enhance the on-chip data reuse rate. Then, to optimize irregular data scheduling and calculation in the aggregation stage, a compressed ring computing array efficiently executes aggregation calculations with a regular ring data flow while compressing the sparse space within the tiles. Finally, for the computation-intensive combination stage, a connection-aware combination processing element is designed to introduce the connection relationship between nodes in advance, reducing the intensive calculation of non-contributing nodes in the tile and reducing computational resource overhead. Compared with the PyG and DGL acceleration frameworks on the 10700K and 1080Ti platforms, HR-GCN achieves an average performance improvement of 56.97× and 1.7× on three public datasets.