Hardware Solutions for Low-Power Smart Edge Computing
The edge computing paradigm for Internet-of-Things brings computing closer to data sources, such as environmental sensors and cameras, using connected smart devices. Over the last few years, research in this area has been both interesting and timely. Typical services like analysis, decision, and con...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Journal of Low Power Electronics and Applications |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9268/12/4/61 |
| _version_ | 1797456926570708992 |
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| author | Lucas Martin Wisniewski Jean-Michel Bec Guillaume Boguszewski Abdoulaye Gamatié |
| author_facet | Lucas Martin Wisniewski Jean-Michel Bec Guillaume Boguszewski Abdoulaye Gamatié |
| author_sort | Lucas Martin Wisniewski |
| collection | DOAJ |
| description | The edge computing paradigm for Internet-of-Things brings computing closer to data sources, such as environmental sensors and cameras, using connected smart devices. Over the last few years, research in this area has been both interesting and timely. Typical services like analysis, decision, and control, can be realized by edge computing nodes executing full-fledged algorithms. Traditionally, low-power smart edge devices have been realized using resource-constrained systems executing machine learning (ML) algorithms for identifying objects or features, making decisions, etc. Initially, this paper discusses recent advances in embedded systems that are devoted to energy-efficient ML algorithm execution. A survey of the mainstream embedded computing devices for low-power IoT and edge computing is then presented. Finally, CYSmart is introduced as an innovative smart edge computing system. Two operational use cases are presented to illustrate its power efficiency. |
| first_indexed | 2024-03-09T16:14:52Z |
| format | Article |
| id | doaj.art-60dd97b0f3fa43c59e55e1db9c5de5bb |
| institution | Directory Open Access Journal |
| issn | 2079-9268 |
| language | English |
| last_indexed | 2024-03-09T16:14:52Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Low Power Electronics and Applications |
| spelling | doaj.art-60dd97b0f3fa43c59e55e1db9c5de5bb2023-11-24T15:53:31ZengMDPI AGJournal of Low Power Electronics and Applications2079-92682022-11-011246110.3390/jlpea12040061Hardware Solutions for Low-Power Smart Edge ComputingLucas Martin Wisniewski0Jean-Michel Bec1Guillaume Boguszewski2Abdoulaye Gamatié3CYLEONE S.A.S. Company, 34090 Montpellier, FranceCYLEONE S.A.S. Company, 34090 Montpellier, FranceCYLEONE S.A.S. Company, 34090 Montpellier, FranceLIRMM, University Montpellier, CNRS, 34095 Montpellier, FranceThe edge computing paradigm for Internet-of-Things brings computing closer to data sources, such as environmental sensors and cameras, using connected smart devices. Over the last few years, research in this area has been both interesting and timely. Typical services like analysis, decision, and control, can be realized by edge computing nodes executing full-fledged algorithms. Traditionally, low-power smart edge devices have been realized using resource-constrained systems executing machine learning (ML) algorithms for identifying objects or features, making decisions, etc. Initially, this paper discusses recent advances in embedded systems that are devoted to energy-efficient ML algorithm execution. A survey of the mainstream embedded computing devices for low-power IoT and edge computing is then presented. Finally, CYSmart is introduced as an innovative smart edge computing system. Two operational use cases are presented to illustrate its power efficiency.https://www.mdpi.com/2079-9268/12/4/61smart edge computingenergy-efficiencyInternet-of-Thingslow-power embedded systemsmachine learningCYSmart |
| spellingShingle | Lucas Martin Wisniewski Jean-Michel Bec Guillaume Boguszewski Abdoulaye Gamatié Hardware Solutions for Low-Power Smart Edge Computing Journal of Low Power Electronics and Applications smart edge computing energy-efficiency Internet-of-Things low-power embedded systems machine learning CYSmart |
| title | Hardware Solutions for Low-Power Smart Edge Computing |
| title_full | Hardware Solutions for Low-Power Smart Edge Computing |
| title_fullStr | Hardware Solutions for Low-Power Smart Edge Computing |
| title_full_unstemmed | Hardware Solutions for Low-Power Smart Edge Computing |
| title_short | Hardware Solutions for Low-Power Smart Edge Computing |
| title_sort | hardware solutions for low power smart edge computing |
| topic | smart edge computing energy-efficiency Internet-of-Things low-power embedded systems machine learning CYSmart |
| url | https://www.mdpi.com/2079-9268/12/4/61 |
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