MRH-GCN: An Efficient GCN Accelerator for Multi-Relation Heterogeneous Graph

Heterogeneous Graph Convolutional Networks (HGCNs) enhance GCN’ representation learning on diverse graphs by effectively capturing complex relation information. However, the hardware deployment of HGCN remains a challenge. First, the complex network model, massive volume of data, and cross-type message passing of HGCN pose challenges to storage and computational efficiency. Additionally, existing GCN accelerators struggle to handle HGCN for multi-relation modeling and fail to efficiently schedule data. To address this, we proposed a novel hardware accelerator for GCN inference called MRH-GCN for heterogeneous graph data and multirelation HGCN models. Firstly, the proposed MRH-GCN adopted a configurable multiple adjacency matrices fusion strategy to reduce the dimensionality of matrices. Simultaneously, it utilized a sparse-matrix-tile space search compression algorithm to minimize sparse data in the adjacency and feature matrices, reducing data sparsity and enhancing indexing efficiency. Secondly, a regular array pipeline based on data reuse was proposed for scheduling three execution stages in HGCN, matching memory access and computation time, and reducing on-chip memory pressure. Additionally, We designed an SpMM systolic array to enhance computational efficiency, and relation-awareness was introduced in the combination stage to reduce unnecessary computations. Experimental results show that the accelerators produced by our framework achieve significant speedup compared with state-of-the-art DGL implementations on CPU (16.56 × and 30.69 ×), GPU (2.71× and 2.30×) for R-GCN and CompGCN model, respectively.