Graph convolutional network (GCN) emerges as a promising direction to learn
the inductive representation in graph data commonly used in widespread
applications, such as E-commerce, social networks, and knowledge graphs.
However, learning from graphs is non-trivial because of its mixed computation
model involving both graph analytics and neural network computing. To this end,
we decompose the GCN learning into two hierarchical paradigms: graph-level and
node-level computing. Such a hierarchical paradigm facilitates the software and
hardware accelerations for GCN learning.
We propose a lightweight graph reordering methodology, incorporated with a
GCN accelerator architecture that equips a customized cache design to fully
utilize the graph-level data reuse. We also propose a mapping methodology aware
of data reuse and task-level parallelism to handle various graphs inputs
effectively. Results show that Rubik accelerator design improves energy
efficiency by 26.3x to 1375.2x than GPU platforms across different datasets and
GCN models.

该论文提出使用分层图级与节点级计算范式，结合轻量级图重排序方法以及自定义缓存设计与任务级并行映射方法，能够更有效地处理各种图输入数据并提高学习效率。实验中 Rubik 加速器设计能够比 GPU 平台提高 26.3 倍至 1375.2 倍的能效比，适用于不同的数据集和 GCN 模型。