  While there have been many studies on hardware acceleration for deep learning
on images, there has been a rather limited focus on accelerating deep learning
applications involving graphs. The unique characteristics of graphs, such as
the irregular memory access and dynamic parallelism, impose several challenges
when the algorithm is mapped to a CPU or GPU. To address these challenges while
exploiting all the available sparsity, we propose a flexible architecture
called SPA-GCN for accelerating Graph Convolutional Networks (GCN), the core
computation unit in deep learning algorithms on graphs. The architecture is
specialized for dealing with many small graphs since the graph size has a
significant impact on design considerations. In this context, we use SimGNN, a
neural-network-based graph matching algorithm, as a case study to demonstrate
the effectiveness of our architecture. The experimental results demonstrate
that SPA-GCN can deliver a high speedup compared to a multi-core CPU
implementation and a GPU implementation, showing the efficiency of our design.

Machine Learning;Hardware Architecture;Distributed, Parallel, and Cluster Computing