Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-Of-Memory (OOM) problem limit the successful application of GNN on edge computing platforms. To tackle these problems, a feature decomposition approach is proposed for memory efficiency optimization of GNN inference. The proposed approach could achieve outstanding optimization on various GNN models, covering a wide range of datasets, which speeds up the inference by up to 3x. Furthermore, the proposed feature decomposition could significantly reduce the peak memory usage (up to 5x in memory efficiency improvement) and mitigate OOM problems during GNN inference.

翻译：然而,GNN的推论效率低下,而且经常发生失灵问题,限制了GNN在边缘计算平台上的成功应用。为了解决这些问题,建议了一种特征分解方法,以优化GNN的推论的记忆效率。提议的方法可以实现各种GNN模型的出色优化,涵盖广泛的数据集,加速推论速度达3x。此外,拟议的特征分解可以大大减少峰值内存用量(在提高记忆效率方面高达5x),并在GNN推论期间减轻OOM问题。