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

Abstract

Graph Neural Networks (GNNs) apply deep learning to inputs represented as graphs. They use fully-connected layers to extract features from the nodes/edges of a graph and aggregate these features using message passing between nodes, thereby combining two distinct computational patterns: dense, regular computations and sparse, irregular computations. To address the computational challenges posed by GNNs, we propose GNNE R A T O R, an accelerator with heterogeneous compute engines optimized for these two patterns. Further, we propose feature-blocking, a novel GNN dataflow that beneficially trades off irregular memory accesses during aggregation for regular memory accesses during feature extraction. We show that GNNE R A T O R 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.

GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing

“…Narrow shard based data reuse. To further reduce DRAM access, we explore data reuse through sharding strategies [21], [43], [66], [67]. In Figure 7, DIMM-0 computes partial reduction of destination vertexes v 1 -v 8 .…”