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.

中文翻译：

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ReGraphX：支持NoC的3D异构ReRAM架构，用于训练图形神经网络

图神经网络（GNN）是对图进行操作的深度神经网络（DNN）的变体。但是，与传统DNN相比，GNN更复杂，因为它们同时具有DNN和图形应用程序的功能。结果，为DNN或图形应用程序专门优化的体系结构不适合GNN训练。在这项工作中，我们提出了一种用于片上GNN训练的3D异构多核架构，以解决此问题。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的体系结构通过启用多播的3D NoC进行了补充，以改善总体可实现的性能。我们证明ReGraphX的性能比传统GPU高3倍。