文章基本信息

标题：Performance analysis of local exit for distributed deep neural networks over cloud and edge computing
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作者：Changsik Lee ; Seungwoo Hong ; Sungback Hong 等
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2020
卷号：42
期号：5
页码：658-668
DOI：10.4218/etrij.2020-0112
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end‐device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation‐intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge‐computing environment. Our test results show that a single‐exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi‐exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single‐exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.