COIN: Communication-Aware In-Memory Acceleration for Graph Convolutional Networks

Abstract

Graph convolutional networks (GCNs) have shown remarkable learning capabilities when processing graphstructured data found inherently in many application areas. GCNs distribute the outputs of neural networks embedded in each vertex over multiple iterations to take advantage of the relations captured by the underlying graphs. Consequently, they incur a significant amount of computation and irregular communication overheads, which call for GCN-specific hardware accelerators. To this end, this paper presents a communicationaware in-memory computing architecture (COIN) for GCN hardware acceleration. Besides accelerating the computation using custom compute elements (CE) and in-memory computing, COIN aims at minimizing the intra-and inter-CE communication in GCN operations to optimize the performance and energy efficiency. Experimental evaluations with widely used datasets show up to 105× improvement in energy consumption compared to state-of-the-art GCN accelerator.