Graph neural networks (GNNs) have shown significant accuracy improvements in
a variety of graph learning domains, sparking considerable research interest.
To translate these accuracy improvements into practical applications, it is
essential to develop high-performance and efficient hardware acceleration for
GNN models. However, designing GNN accelerators faces two fundamental
challenges: the high bandwidth requirement of GNN models and the diversity of
GNN models. Previous works have addressed the first challenge by using more
expensive memory interfaces to achieve higher bandwidth. For the second
challenge, existing works either support specific GNN models or have generic
designs with poor hardware utilization.
In this work, we tackle both challenges simultaneously. First, we identify a
new type of partition-level operator fusion, which we utilize to internally
reduce the high bandwidth requirement of GNNs. Next, we introduce
partition-level multi-threading to schedule the concurrent processing of graph
partitions, utilizing different hardware resources. To further reduce the extra
on-chip memory required by multi-threading, we propose fine-grained graph
partitioning to generate denser graph partitions. Importantly, these three
methods make no assumptions about the targeted GNN models, addressing the
challenge of model variety. We implement these methods in a framework called
SwitchBlade, consisting of a compiler, a graph partitioner, and a hardware
accelerator. Our evaluation demonstrates that SwitchBlade achieves an average
speedup of $1.85\times$ and energy savings of $19.03\times$ compared to the
NVIDIA V100 GPU. Additionally, SwitchBlade delivers performance comparable to
state-of-the-art specialized accelerators.

本文介绍了一种名为 SwitchBlade 的框架，通过新型分区级操作融合、分区级多线程和细粒度图分区等方法，实现了对图神经网络的高性能和高效能加速，相较于 NVIDIA V100 GPU，SwitchBlade 平均速度提升 1.85 倍，节能 19.03 倍，并且在性能上与最先进的专用加速器相媲美。