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.
@article{arxiv.2102.07959,
title = {ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks},
author = {Aqeeb Iqbal Arka and Biresh Kumar Joardar and Janardhan Rao Doppa and Partha Pratim Pande and Krishnendu Chakrabarty},
journal= {arXiv preprint arXiv:2102.07959},
year = {2021}
}
Comments
This paper has been accepted and presented at Design Automation and Test in Europe (DATE) 2021