SpGCN: An FPGA-Based Graph Convolutional Network Accelerator for Sparse Graphs

Graph convolutional network(GCN) has become one of the most popular graph neural network(GNN) models today. Because the calculation-intensive operations and the memory access-intensive operations during GNN inference, existing GNN accelerators utilize many computing and storage resources but the computing efficiency is still low when the feature matrix or the adjacency matrix is extremely sparse. Therefore, we propose SpGCN, a computationally efficient GCN accelerator suitable for sparse graphs. We design corresponding optimization methods of scheduling, caching and indexing in both combination stage and aggregation stage, aiming at fewer congestion and higher efficiency. Experimental results show that the total computing efficiency is 68.7% on average. Compared with peer work, SpGCN can achieve lower inference delay by using only 18% computing resources and 50 % storage resources.