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）正在成为图结构数据机器学习研究的新兴方向。尽管GNN在诸多任务上取得了最优性能，但在处理包含海量数据且对延迟有严格要求的实际应用时，仍面临可扩展性挑战。为应对这些挑战，大量研究聚焦于如何加速GNN。这些加速技术涉及GNN管线的多个层面，涵盖智能训练推理算法、高效系统方案以及定制化硬件。随着GNN加速领域的研究成果迅速增长，目前尚缺乏系统性的处理方法来提供统一视角并阐释相关工作的复杂性。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。我们提出的分类学处理将现有工作有机联结，为这一领域的后续发展奠定基础。