Sampling is an important process in many GNN structures in order to train larger datasets with a smaller computational complexity. However, compared to other processes in GNN (such as aggregate, backward propagation), the sampling process still costs tremendous time, which limits the speed of training. To reduce the time of sampling, hardware acceleration is an ideal choice. However, state of the art GNN acceleration proposal did not specify how to accelerate the sampling process. What's more, directly accelerating traditional sampling algorithms will make the structure of the accelerator very complicated. In this work, we made two contributions: (1) Proposed a new neighbor sampler: CONCAT Sampler, which can be easily accelerated on hardware level while guaranteeing the test accuracy. (2) Designed a CONCAT-sampler-accelerator based on FPGA, with which the neighbor sampling process boosted to about 300-1000 times faster compared to the sampling process without it.
翻译:取样是许多GNN结构中的一个重要过程,目的是培训计算复杂程度较小的较大数据集。然而,与GNN的其他过程(如综合、后向传播)相比,取样过程仍然花费大量时间,限制了培训速度。为缩短取样时间,硬件加速是一种理想的选择。然而,最先进的GNN加速建议并未具体说明如何加速取样过程。此外,直接加速的传统取样算法将使加速器结构变得非常复杂。在这项工作中,我们作出了两项贡献:(1) 提议一个新的邻居取样器:COCAT采样器,在硬件水平上很容易加速,同时保证测试准确性。(2) 设计了一个基于FPGA的CONCAT-Sampler-加速器。 邻居采样程序比没有取样程序加快了300-1000倍。