PANG: A Pattern-Aware GCN Accelerator for Universal Graphs

Graph convolutional neural network (GCN) extends deep learning to process graph data and demonstrates superior performance. However, due to the irregularity, graphs show inconsistent patterns across different regions, which leads to distinctions in data reusability and edge processing activity, and consequently poses impacts on hardware efficiency and resource utility. Prior accelerators seldom explore the distinct patterns across graph regions and adopt a fixed strategy for the whole graph without consideration for region-specific characteristics. In this paper, we identify the inconsistent patterns of graphs and characterize the distinctions between graph regions. Then, we propose an adaptive dataflow to adapt the region-specific patterns. Third, we implement PANG, a pattern-aware accelerator that can dynamically adjust the dataflow to exploit the reusability and alleviate the frequent destination switching. Evaluated on real-world datasets, PANG achieves significant performance improvement.