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 ReRAMbased 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.
3D Network-on-Chip (3D NoC) enables design of high-performance and energy-efficient manycore computing platforms. Two of the commonly used vertical interconnection technologies are: through silicon via (TSV) and near-field inductive coupling (NFIC). Both TSV- and NFIC-based links introduce additional area overhead. One of the possible ways to reduce the area overhead is to design partially connected 3D NoC with minimal effect on overall performance. The achievable performance of the partially connected 3D NoCs depends on the area, energy, and bandwidth of the vertical links. Moreover, the electromigration-induced failure of TSV is more severe than the misalignment-induced error in NFIC. By considering all these pertinent factors, we demonstrate that it is indeed possible to design a partially connected NFIC-based 3D NoC that performs as good as a fully connected TSV-based counterpart when the data rate is below a certain limit. For higher data rates, the partially connected TSV-based 3D NoC is a viable solution. However, NFIC-based 3D NoC is always more robust than the TSV-based counterpart.
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