Graph convolutional networks (GCNs) are becoming increasingly popular as they overcome the limited applicability of prior neural networks. A GCN takes as input an arbitrarily structured graph and executes a series of layers which exploit the graph's structure to calculate their output features. One recent trend in GCNs is the use of deep network architectures. As opposed to the traditional GCNs which only span around two to five layers deep, modern GCNs now incorporate tens to hundreds of layers with the help of residual connections. From such deep GCNs, we find an important characteristic that they exhibit very high intermediate feature sparsity. We observe that with deep layers and residual connections, the number of zeros in the intermediate features sharply increases. This reveals a new opportunity for accelerators to exploit in GCN executions that was previously not present. In this paper, we propose SGCN, a fast and energy-efficient GCN accelerator which fully exploits the sparse intermediate features of modern GCNs. SGCN suggests several techniques to achieve significantly higher performance and energy efficiency than the existing accelerators. First, SGCN employs a GCN-friendly feature compression format. We focus on reducing the off-chip memory traffic, which often is the bottleneck for GCN executions. Second, we propose microarchitectures for seamlessly handling the compressed feature format. Third, to better handle locality in the existence of the varying sparsity, SGCN employs sparsity-aware cooperation. Sparsity-aware cooperation creates a pattern that exhibits multiple reuse windows, such that the cache can capture diverse sizes of working sets and therefore adapt to the varying level of sparsity. We show that SGCN achieves 1.71x speedup and 43.9% higher energy efficiency compared to the existing accelerators.

翻译：由于GCN克服了先前神经网络的有限适用性,GCN越来越受欢迎。GCN作为输入一个任意结构化的图形,并使用一系列层次来利用图形结构来计算其输出特征。GCN最近的一个趋势是使用深层次的网络结构。与传统的GCN相比,现代GCN仅仅分布在两至五层深处,而现代GCN则包含数十至数百层,并借助于剩余连接。从这种深层次的GCN中,我们发现一个重要特征,即它们表现出非常高的中间特征。我们观察到,随着深层和残余的连接,中间特征中的零数量急剧增加。这揭示了加速器利用GCN过去没有出现的GCN处决的新机会。在本文中,我们建议SGCN是一个快速和节能的GCN加速器,它充分利用了现代GCN的稀薄中间特征。 SGCN认为,一些技术可以大大提高业绩和能源效率,比现有的加速度高得多。首先,SGNSGSGSG的中位数字化速度,因此,S-C的运行过程往往以GCN为G-C格式。