An Edge Intelligence Framework for Resource Constrained Community Area Network
2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS)(2020)
摘要
Edge intelligence, Artificial Intelligence (AI) on the edge can have a significant impact on modern Community Area Network (CAN). This paper proposes an edge intelligence method that utilizes deep learning, object detection, and multi-label multi-classification to perform monitoring and actuation tasks without resorting to high-end edge servers. The proposed method contains a resource-constrained node as an edge device. For the edge server, it utilizes a special-purpose ASIC (Intel's Movidius) interfaced with a node-level edge device. To further the idea of limited bandwidth availability in CAN, pseudo D2D communication is employed. SSD-MobileNet and customized multi-label-multi-classification based GoogLeNet models are hosted on the edge server, The proposed methodology can achieve about 5.26 FPS for complete bi-directional communication.
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关键词
Edge Intelligence,Community Area Network,Convolution Neural Network,IoT,Multi-label,Multi-classification
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