  Graph Neural Network (GNN) is a variant of Deep Neural Networks (DNNs)
operating on graphs. However, GNNs are more complex compared to traditional
DNNs as they simultaneously exhibit features of both DNN and graph
applications. As a result, architectures specifically optimized for either DNNs
or graph applications are not suited for GNN training. In this work, we propose
a 3D heterogeneous manycore architecture for on-chip GNN training to address
this problem. The proposed architecture, ReGraphX, involves heterogeneous ReRAM
crossbars to fulfill the disparate requirements of both DNN and graph
computations simultaneously. The ReRAM-based architecture is complemented with
a multicast-enabled 3D NoC to improve the overall achievable performance. We
demonstrate that ReGraphX outperforms conventional GPUs by up to 3.5X (on an
average 3X) in terms of execution time, while reducing energy consumption by as
much as 11X.
