Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices
Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features ena...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/5/600 |
| _version_ | 1797414147511549952 |
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| author | Gianluca Cornetta Abdellah Touhafi |
| author_facet | Gianluca Cornetta Abdellah Touhafi |
| author_sort | Gianluca Cornetta |
| collection | DOAJ |
| description | Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers. |
| first_indexed | 2024-03-09T05:28:54Z |
| format | Article |
| id | doaj.art-04560fae79b14959a6060e981ecbfbcb |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T05:28:54Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-04560fae79b14959a6060e981ecbfbcb2023-12-03T12:34:19ZengMDPI AGElectronics2079-92922021-03-0110560010.3390/electronics10050600Design and Evaluation of a New Machine Learning Framework for IoT and Embedded DevicesGianluca Cornetta0Abdellah Touhafi1Department of Information Engineering, San Pablo-CEU University, Boadilla del Monte, 28668 Madrid, SpainDepartment of Engineering Technology (INDI), Vrije Universiteit Brussel, 1050 Brussels, BelgiumLow-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.https://www.mdpi.com/2079-9292/10/5/600supervised learningunsupervised learningsemi-supervised learningreinforcement learningclassifiersdecision trees |
| spellingShingle | Gianluca Cornetta Abdellah Touhafi Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices Electronics supervised learning unsupervised learning semi-supervised learning reinforcement learning classifiers decision trees |
| title | Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices |
| title_full | Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices |
| title_fullStr | Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices |
| title_full_unstemmed | Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices |
| title_short | Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices |
| title_sort | design and evaluation of a new machine learning framework for iot and embedded devices |
| topic | supervised learning unsupervised learning semi-supervised learning reinforcement learning classifiers decision trees |
| url | https://www.mdpi.com/2079-9292/10/5/600 |
| work_keys_str_mv | AT gianlucacornetta designandevaluationofanewmachinelearningframeworkforiotandembeddeddevices AT abdellahtouhafi designandevaluationofanewmachinelearningframeworkforiotandembeddeddevices |