  Graph convolutional networks (GCNs) have shown remarkable learning
capabilities when processing graph-structured 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
communication-aware 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 105x
improvement in energy consumption compared to state-of-the-art GCN accelerator.
