ReGraphX：用于训练图神经网络的NoC-enabled三维异构ReRAM结构
ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks
摘要：
图神经网络（GNN）是在图上操作的深度神经网络（DNN）的变体。然而，与传统的DNN相比，GNN更复杂，因为它们同时表现出DNN和图形应用程序的功能。因此，专门为DNN或图形应用优化的架构不适合GNN训练。在这项工作中，我们提出了一个3D异构众核架构的片上GNN训练，以解决这个问题。所提出的架构ReGraphX涉及异构ReRAM交叉开关，以同时满足DNN和图形计算的不同要求。基于ReRAM的架构与支持多播的3D NoC相辅相成，以提高整体可实现的性能。我们证明了ReGraphX在执行时间方面比传统GPU高出3.5倍（平均3倍），同时将能耗降低了11倍。
英文摘要：
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.
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