TFR-GCN: A GCN Accelerator with Tile-Fusing Strategy
Shengjun Xu, Wenjin Huang, Yihua Huang · 2022
Graph convolutional networks(GCN) are widely used because of their superior ability in the graph data processing. However, the irregular data access and the computing intensity in GCN inference bring great challenges to the existing hardware accelerators. Hence, this paper proposes an accelerator using a tiles-fusing rings array (TFR-GCN) in the aggregation phase to achieve regular memory access and improve computing resource utilization. Moreover, this paper proposes a backpressure index calculation unit to optimize sparse-dense high-dimensional vector multiplication in the combination phase. Compared with the performance of software accelerators running on CPU and GPU, this accelerator achieved 208×, 50 × performance improvement on average. Compared with the existing FPGA-based accelerators, this accelerator achieved a performance improvement of (1.1-147)×.