  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.
