optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms

Abstract

Graph neural networks (GNN) have achieved stateof-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-OfMemory (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 3×. Furthermore, the proposed feature decomposition could significantly reduce the peak memory usage (up to 5× in memory efficiency improvement) and mitigate OOM problems during GNN inference.