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Graph neural networks (GNN) have achieved state-of-the-art performance onvarious industrial tasks. However, the poor efficiency of GNN inference andfrequent Out-Of-Memory (OOM) problem limit the successful application of GNN onedge computing platforms. To tackle these problems, a feature decompositionapproach is proposed for memory efficiency optimization of GNN inference. Theproposed 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 thepeak memory usage (up to 5x in memory efficiency improvement) and mitigate OOMproblems during GNN inference.

Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms