GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks

Abstract

Graph Neural Networks (GNNs) use a fully-connected layer to extract features from the nodes of a graph and aggregate these features using message passing between nodes, combining two distinct computational patterns: dense, regular computations and sparse, irregular computations. To address this challenge, we propose GNNerator, an accelerator with heterogeneous compute engines optimized for these two patterns. Further, GNNerator implements feature-blocking, a novel GNN dataflow that beneficially trades off irregular memory accesses during aggregation for regular memory accesses during feature extraction. We show GNNerator achieves speedups of 5.7-37x over an NVIDIA RTX 2080-Ti, and 2.3x-3.8x over HyGCN, a state-of-the-art GNN accelerator.