Derived from the fusion of graph traversal and neural networks, graph convolutional neural networks (GCNs) have achieved state-of-the-art performance in graph learning. However, the hybrid execution pattern, caused by the opposite characteristics of graph traversal based phase and neural network based transformation phase, poses huge challenges to the efficient execution of traditional architectures. Although GCN accelerators have emerged to address these challenges, they fail to harvest both bidirectional execution and inter-phase fusion opportunities exposed by the alternate execution phases in GCNs. Previous works either concentrate on a single execution direction or exchange the execution order of phases without inter-phase fusion, hence failing to further improve performance and efficiency. Therefore, we propose a novel hardware unit named BiFusion, which can be easily applied to existing GCN accelerators with hybrid architecture in order to harvest both of the above opportunities. BiFusion enables dynamic direction selection and inter-phase fusion, and helps significantly reduce the amounts of data access and computation. Experiments show that integrating the BiFusion unit helps the state-of-the-art GCN accelerator achieve 2× speedup on average.

中文翻译：

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通过双向融合实现 GCN 的硬件加速

源自图遍历和神经网络的融合，图卷积神经网络 (GCN) 在图学习方面取得了最先进的性能。然而，基于图遍历的阶段和基于神经网络的转换阶段的相反特征导致的混合执行模式对传统架构的高效执行提出了巨大挑战。尽管 GCN 加速器已经出现以应对这些挑战，但它们未能同时收获 GCN 中交替执行阶段所暴露的双向执行和阶段间融合机会。以往的工作要么专注于单一的执行方向，要么在没有相间融合的情况下交换阶段的执行顺序，因此未能进一步提高性能和效率。所以，我们提出了一种名为 BiFusion 的新型硬件单元，它可以轻松应用于具有混合架构的现有 GCN 加速器，以收获上述两个机会。BiFusion 支持动态方向选择和相间融合，有助于显着减少数据访问和计算量。实验表明，集成 BiFusion 单元有助于最先进的 GCN 加速器平均实现 2 倍的加速。