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Analyzing Embedded AIoT Devices for Deep Learning Purposes

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Book cover Artificial Intelligence Trends in Systems (CSOC 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 502))

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Abstract

In this article, we focused on the artificial intelligence of things (AIoT) embedded devices that are characterized by higher computing power. These devices are designed directly to work with the implementation of artificial intelligence - deep learning. We mentioned the importance of deep learning applications and their current use in various industry applications. They also meet their role as IoT devices. Therefore, they can be included in a separate AIoT category. In the article, we described the role of IoT in Industry 4.0 and we also focused on AIoT devices and their use in industry. The aim of the article was to analyze and verify Jetson equipment from the point of view of heat dissipation during operation time and to visually inspect printed circuit boards for the installation of these equipment, taking into account the thermal requirements for the design of electrical equipment where Jetson embedded devices will be physically installed.

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Acknowledgements

This publication was supported by the project of the project Young Scientist 1360 (2021–2022): “Applying machine learning at industry environment” supported by internal grant Young Scientist at Slovak University of Technology in Bratislava.

This paper was supported under the project of Operational Programme Integrated Infrastructure: Independent research and development of technological kits based on wearable electronics products, as tools for raising hygienic standards in a society ex-posed to the virus causing the COVID-19 disease, ITMS2014+ code 313011ASK8. The project is co-funding by European Regional Development Fund.

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Authors and Affiliations

  1. Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics, Slovak University of Technology in Bratislava, Bratislava, Slovakia

    Roman Budjac, Martin Barton & Peter Schreiber

  2. Research Centre, University of Zilina, Univerzitna 8215/1, 010 26, Zilina, Slovakia

    Martin Skovajsa

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  1. Roman Budjac
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  2. Martin Barton
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  4. Martin Skovajsa
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Correspondence to Roman Budjac .

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  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Radek Silhavy

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Budjac, R., Barton, M., Schreiber, P., Skovajsa, M. (2022). Analyzing Embedded AIoT Devices for Deep Learning Purposes. In: Silhavy, R. (eds) Artificial Intelligence Trends in Systems. CSOC 2022. Lecture Notes in Networks and Systems, vol 502. Springer, Cham. https://doi.org/10.1007/978-3-031-09076-9_39

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