ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks
Aqeeb Iqbal Arka1,a, Biresh Kumar Joardar2,a, Janardhan Rao Doppa1,b, Partha Pratim Pande1,c and Krishnendu Chakrabarty2,b
1School of EECS, Washington State University Pullman, WA 99164, USA
aaqeebiqbal.arka@wsu.edu
bjana.doppa@wsu.edu
cpande@wsu.edu
2Department of ECE, Duke University Durham, NC 27708, USA
abireshkumar.joardar@duke.edu
bkrish@duke.edu
ABSTRACT
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.
Keywords: GNNs, ReRAM, 3D, NoC, Heterogeneous.
