Edge 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 paper, 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 featur
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