TreeNet Based Fast Task Decomposition for Resource-Constrained Edge Intelligence

Article


Lu, Dong, Zhai, Yanlong, Shen, Jun, Fahmideh, Mahdi, Wu, Jianqing, Tchaye-Kondi, Jude and Zhu, Liehuang. 2023. "TreeNet Based Fast Task Decomposition for Resource-Constrained Edge Intelligence." IEEE Transactions on Services Computing. 16 (3), pp. 2254-2266.
Article Title

TreeNet Based Fast Task Decomposition for Resource-Constrained Edge Intelligence

ERA Journal ID36548
Article CategoryArticle
AuthorsLu, Dong, Zhai, Yanlong, Shen, Jun, Fahmideh, Mahdi, Wu, Jianqing, Tchaye-Kondi, Jude and Zhu, Liehuang
Journal TitleIEEE Transactions on Services Computing
Journal Citation16 (3), pp. 2254-2266
Number of Pages13
Year2023
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1939-1374
Web Address (URL)https://ieeexplore.ieee.org/document/9810481
AbstractEdge intelligence is an emerging technology that integrates edge computing and deep learning to bring AI to the network's edge. It has gained wide attention for its lower network latency and better privacy preservation abilities. However, the inference of deep neural networks is computationally demanding and results in poor real-time performance, making it challenging for resource-constrained edge devices. In this article, we propose a hierarchical deep learning model based on TreeNet to reduce the computational cost for edge devices. Based on the similarity of the classification categories, we decompose a given task into disjoint sub-tasks to reduce the complexity of the required model. Then a lightweight binary classifier is proposed for evaluating the sub-task inference result. If the inference result of a sub-task is unreliable, our system will forward the input samples to the cloud server for further processing. We also proposed a new strategy for finding and sharing common features across sub-tasks to improve training speed and accuracy. The experimental results on several popular datasets demonstrate the effectiveness of our approach in speeding up inferences while processing most of the input data with a low error rate.
Keywordsdeep learning; model acceleration; edge intelligence; Edge computing; resource-constrained; model compression
ANZSRC Field of Research 2020460905. Information systems development methodologies and practice
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Byline AffiliationsBeijing Institute of Technology, China
University of Wollongong
School of Business
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