Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition
Embedded applications are increasingly prevalent in various domains, from consumer electronics to industrial automation and smart cities. With the advances in integrated circuit manufacturing technologies, low-power chips can now execute complex algorithms, including machine learning models. Howeve...
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| Format: | Article |
| Language: | English |
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Universidade do Porto
2023-11-01
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| Series: | U.Porto Journal of Engineering |
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| Online Access: | https://journalengineering.fe.up.pt/index.php/upjeng/article/view/2156 |
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| author | João Carlos Bittencourt Walber Conceição de Jesus Rocha |
| author_facet | João Carlos Bittencourt Walber Conceição de Jesus Rocha |
| author_sort | João Carlos Bittencourt |
| collection | DOAJ |
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Embedded applications are increasingly prevalent in various domains, from consumer electronics to industrial automation and smart cities. With the advances in integrated circuit manufacturing technologies, low-power chips can now execute complex algorithms, including machine learning models. However, the computational constraints of embedded devices require compact and efficient neural network models, as well as software frameworks and optimisation techniques tailored to their hardware resources. This study investigates the implementation of Convolutional Neural Network (CNN) models for gesture recognition on an STM32F4 microcontroller, by exploring the impact of freezing layers, fine-tuning and pruning techniques on pre-trained CNN models. The results demonstrate that fine-tuning and freezing layers improve accuracy by up to 18%. Additionally, freezing layers by 10% and 20% improved the accuracy. Finally, we demonstrate that pruning reduced the model size by 90%, enabling it to perform gesture recognition on small devices. These findings are significant for developing software and optimisation techniques for embedded systems, particularly in the context of the Internet of Things.
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| format | Article |
| id | doaj-art-3a80331ced05451b8f57b706fca31f77 |
| institution | Kabale University |
| issn | 2183-6493 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Universidade do Porto |
| record_format | Article |
| series | U.Porto Journal of Engineering |
| spelling | doaj-art-3a80331ced05451b8f57b706fca31f772025-08-20T03:50:16ZengUniversidade do PortoU.Porto Journal of Engineering2183-64932023-11-019510.24840/2183-6493_009-005_002156Optimisation Techniques for Compact CNN on Embedded Systems for Gesture RecognitionJoão Carlos Bittencourt0https://orcid.org/0000-0002-4540-512XWalber Conceição de Jesus Rocha1University of Porto, Faculty of EngineeringFederal University of Recôncavo da Bahia, Centre for Exact and Technological Sciences Embedded applications are increasingly prevalent in various domains, from consumer electronics to industrial automation and smart cities. With the advances in integrated circuit manufacturing technologies, low-power chips can now execute complex algorithms, including machine learning models. However, the computational constraints of embedded devices require compact and efficient neural network models, as well as software frameworks and optimisation techniques tailored to their hardware resources. This study investigates the implementation of Convolutional Neural Network (CNN) models for gesture recognition on an STM32F4 microcontroller, by exploring the impact of freezing layers, fine-tuning and pruning techniques on pre-trained CNN models. The results demonstrate that fine-tuning and freezing layers improve accuracy by up to 18%. Additionally, freezing layers by 10% and 20% improved the accuracy. Finally, we demonstrate that pruning reduced the model size by 90%, enabling it to perform gesture recognition on small devices. These findings are significant for developing software and optimisation techniques for embedded systems, particularly in the context of the Internet of Things. https://journalengineering.fe.up.pt/index.php/upjeng/article/view/2156Embedded systemsTinyMLInternet of ThingsConvolutional Neural NetworksMobileNetEfficientNet |
| spellingShingle | João Carlos Bittencourt Walber Conceição de Jesus Rocha Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition U.Porto Journal of Engineering Embedded systems TinyML Internet of Things Convolutional Neural Networks MobileNet EfficientNet |
| title | Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition |
| title_full | Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition |
| title_fullStr | Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition |
| title_full_unstemmed | Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition |
| title_short | Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition |
| title_sort | optimisation techniques for compact cnn on embedded systems for gesture recognition |
| topic | Embedded systems TinyML Internet of Things Convolutional Neural Networks MobileNet EfficientNet |
| url | https://journalengineering.fe.up.pt/index.php/upjeng/article/view/2156 |
| work_keys_str_mv | AT joaocarlosbittencourt optimisationtechniquesforcompactcnnonembeddedsystemsforgesturerecognition AT walberconceicaodejesusrocha optimisationtechniquesforcompactcnnonembeddedsystemsforgesturerecognition |