@inproceedings{d591f05cf3ef42179d4c6435affa3c06,
title = "Brief industry paper: Optimizing memory efficiency of graph neural networks on edge computing platforms",
abstract = "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 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.",
keywords = "Edge Computing, Feature Decomposition, Graph Neural Network, Memory Efficiency",
author = "Ao Zhou and Jianlei Yang and Yeqi Gao and Tong Qiao and Yingjie Qi and Xiaoyi Wang and Yunli Chen and Pengcheng Dai and Weisheng Zhao and Chunming Hu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 27th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2021 ; Conference date: 18-05-2021 Through 21-05-2021",
year = "2021",
month = may,
doi = "10.1109/RTAS52030.2021.00048",
language = "英语",
series = "Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "445--448",
booktitle = "Proceedings - 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium, RTAS 2021",
address = "美国",
}