Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices
The application of Artificial Intelligence to the industrial world and its appliances has recently grown in popularity. Indeed, AI techniques are now becoming the <i>de-facto</i> technology for the resolution of complex tasks concerning computer vision, natural language processing and ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/24/11957 |
| _version_ | 1797506740872282112 |
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| author | Andrea Agiollo Andrea Omicini |
| author_facet | Andrea Agiollo Andrea Omicini |
| author_sort | Andrea Agiollo |
| collection | DOAJ |
| description | The application of Artificial Intelligence to the industrial world and its appliances has recently grown in popularity. Indeed, AI techniques are now becoming the <i>de-facto</i> technology for the resolution of complex tasks concerning computer vision, natural language processing and many other areas. In the last years, most of the the research community efforts have focused on increasing the performance of most common AI techniques—e.g., Neural Networks, etc.—at the expenses of their complexity. Indeed, many works in the AI field identify and propose hyper-efficient techniques, targeting high-end devices. However, the application of such AI techniques to devices and appliances which are characterised by limited computational capabilities, remains an open research issue. In the industrial world, this problem heavily targets low-end appliances, which are developed focusing on saving costs and relying on—computationally—constrained components. While some efforts have been made in this area through the proposal of AI-simplification and AI-compression techniques, it is still relevant to study which available AI techniques can be used in modern constrained devices. Therefore, in this paper we propose a load classification task as a case study to analyse which state-of-the-art NN solutions can be embedded successfully into constrained industrial devices. The presented case study is tested on a simple microcontroller, characterised by very poor computational performances—i.e., FLOPS –, to mirror faithfully the design process of low-end appliances. A handful of NN models are tested, showing positive outcomes and possible limitations, and highlighting the complexity of AI embedding. |
| first_indexed | 2024-03-10T04:37:07Z |
| format | Article |
| id | doaj.art-dfd51f33a7ed4823b6220bab65dcc6b7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:37:07Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dfd51f33a7ed4823b6220bab65dcc6b72023-11-23T03:41:00ZengMDPI AGApplied Sciences2076-34172021-12-0111241195710.3390/app112411957Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded DevicesAndrea Agiollo0Andrea Omicini1Department of Computer Science and Engineering (DISI), Alma Mater Studiorum—Università di Bologna, 47522 Cesena, ItalyDepartment of Computer Science and Engineering (DISI), Alma Mater Studiorum—Università di Bologna, 47522 Cesena, ItalyThe application of Artificial Intelligence to the industrial world and its appliances has recently grown in popularity. Indeed, AI techniques are now becoming the <i>de-facto</i> technology for the resolution of complex tasks concerning computer vision, natural language processing and many other areas. In the last years, most of the the research community efforts have focused on increasing the performance of most common AI techniques—e.g., Neural Networks, etc.—at the expenses of their complexity. Indeed, many works in the AI field identify and propose hyper-efficient techniques, targeting high-end devices. However, the application of such AI techniques to devices and appliances which are characterised by limited computational capabilities, remains an open research issue. In the industrial world, this problem heavily targets low-end appliances, which are developed focusing on saving costs and relying on—computationally—constrained components. While some efforts have been made in this area through the proposal of AI-simplification and AI-compression techniques, it is still relevant to study which available AI techniques can be used in modern constrained devices. Therefore, in this paper we propose a load classification task as a case study to analyse which state-of-the-art NN solutions can be embedded successfully into constrained industrial devices. The presented case study is tested on a simple microcontroller, characterised by very poor computational performances—i.e., FLOPS –, to mirror faithfully the design process of low-end appliances. A handful of NN models are tested, showing positive outcomes and possible limitations, and highlighting the complexity of AI embedding.https://www.mdpi.com/2076-3417/11/24/11957load classificationNeural Networksembeddinghyper-constrained devices |
| spellingShingle | Andrea Agiollo Andrea Omicini Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices Applied Sciences load classification Neural Networks embedding hyper-constrained devices |
| title | Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices |
| title_full | Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices |
| title_fullStr | Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices |
| title_full_unstemmed | Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices |
| title_short | Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices |
| title_sort | load classification a case study for applying neural networks in hyper constrained embedded devices |
| topic | load classification Neural Networks embedding hyper-constrained devices |
| url | https://www.mdpi.com/2076-3417/11/24/11957 |
| work_keys_str_mv | AT andreaagiollo loadclassificationacasestudyforapplyingneuralnetworksinhyperconstrainedembeddeddevices AT andreaomicini loadclassificationacasestudyforapplyingneuralnetworksinhyperconstrainedembeddeddevices |