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Deep learning has achieved remarkable successes in various areas such as computer vision and natural language processing. Many sophisticated models have been proposed to improve performance by designing a significant number of layers of neurons. As an emerging research area, edge intelligence tries to bring intelligence to the network edge by integrating edge computing and AI technologies and it has gained wide attention for its lower latency and better privacy preservation features. Nevertheless, training and inferencing deep neural networks require intensive computation power and time, making it quite challenging to run the models on the resource-constrained edge devices. In this paper, we propose a deep learning model, namely TreeNet, based on task decomposition. After obtaining a task, we would not fit the entire task but decompose the task into disjoint sub-tasks to reduce the complexity of the required deep learning model (it could be divided multiple times if necessary). We firs ....

Deep Learning , Edge Computing , Edge Intelligence , Odel Acceleration , Model Compression , Resource Constrained ,