Performance analysis of local exit for distributed deep neural networks over cloud and edge computing
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 ban...
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2020-10-01
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Online Access: | https://doi.org/10.4218/etrij.2020-0112 |
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doaj-f38d608e32a743379a9311fe544aad552021-01-05T05:15:53ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632020-10-0142565866810.4218/etrij.2020-011210.4218/etrij.2020-0112Performance analysis of local exit for distributed deep neural networks over cloud and edge computingChangsik LeeSeungwoo HongSungback HongTaeyeon KimIn 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.https://doi.org/10.4218/etrij.2020-0112convolutional neural networksdeep neural networksedge computinginference performancemulti‐exitsingle‐exit |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Changsik Lee Seungwoo Hong Sungback Hong Taeyeon Kim |
spellingShingle |
Changsik Lee Seungwoo Hong Sungback Hong Taeyeon Kim Performance analysis of local exit for distributed deep neural networks over cloud and edge computing ETRI Journal convolutional neural networks deep neural networks edge computing inference performance multi‐exit single‐exit |
author_facet |
Changsik Lee Seungwoo Hong Sungback Hong Taeyeon Kim |
author_sort |
Changsik Lee |
title |
Performance analysis of local exit for distributed deep neural networks over cloud and edge computing |
title_short |
Performance analysis of local exit for distributed deep neural networks over cloud and edge computing |
title_full |
Performance analysis of local exit for distributed deep neural networks over cloud and edge computing |
title_fullStr |
Performance analysis of local exit for distributed deep neural networks over cloud and edge computing |
title_full_unstemmed |
Performance analysis of local exit for distributed deep neural networks over cloud and edge computing |
title_sort |
performance analysis of local exit for distributed deep neural networks over cloud and edge computing |
publisher |
Electronics and Telecommunications Research Institute (ETRI) |
series |
ETRI Journal |
issn |
1225-6463 |
publishDate |
2020-10-01 |
description |
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. |
topic |
convolutional neural networks deep neural networks edge computing inference performance multi‐exit single‐exit |
url |
https://doi.org/10.4218/etrij.2020-0112 |
work_keys_str_mv |
AT changsiklee performanceanalysisoflocalexitfordistributeddeepneuralnetworksovercloudandedgecomputing AT seungwoohong performanceanalysisoflocalexitfordistributeddeepneuralnetworksovercloudandedgecomputing AT sungbackhong performanceanalysisoflocalexitfordistributeddeepneuralnetworksovercloudandedgecomputing AT taeyeonkim performanceanalysisoflocalexitfordistributeddeepneuralnetworksovercloudandedgecomputing |
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1724348607957565440 |