  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.
