Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability challenges when it comes to real-world applications that have numerous data and strict latency requirements. Many studies have been conducted on how to accelerate GNNs in an effort to address these challenges. These acceleration techniques touch on various aspects of the GNN pipeline, from smart training and inference algorithms to efficient systems and customized hardware. As the amount of research on GNN acceleration has grown rapidly, there lacks a systematic treatment to provide a unified view and address the complexity of relevant works. In this survey, we provide a taxonomy of GNN acceleration, review the existing approaches, and suggest future research directions. Our taxonomic treatment of GNN acceleration connects the existing works and sets the stage for further development in this area.

翻译：图神经网络（GNN）正成为图结构数据机器学习研究的新兴方法。尽管GNN在众多任务中取得了最先进的性能，但在处理包含海量数据且具有严格延迟要求的实际应用时，面临可扩展性挑战。为应对这些挑战，学界围绕如何加速GNN开展了大量研究。这些加速技术涉及GNN管线的多个层面，从智能训练与推理算法到高效系统及定制化硬件。随着GNN加速研究成果的快速增长，目前缺乏系统性的综合研究来提供统一视角并厘清相关工作的复杂性。本综述提出GNN加速的分类体系，梳理现有方法，并展望未来研究方向。该分类学框架将现有研究相连接，为该领域的进一步发展奠定基础。