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Elements of TinyML on Constrained Resource Hardware

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Advances in Computing and Data Sciences (ICACDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1614))

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Abstract

The next phase of intelligent computing could be entirely reliant on the Internet of Things (IoT). The IoT is critical in changing industries into smarter entities capable of providing high-quality services and products. The widespread adoption of IoT devices raises numerous issues concerning the privacy and security of data gathered and retained by these services. This concern increases exponentially when such data is generated by healthcare applications. To develop genuinely intelligent devices, data must be transferred to the cloud for processing due to the computationally costly nature of current Neural Network implementations. Tiny Machine Learning (TinyML) is a new technology that has been presented by the scientific community as a means of developing autonomous and secure devices that can gather, process, and provide output without transferring data to remote third party organizations. This work presents three distinct TinyML applications to cope with the aforementioned issues and open the road for intelligent machines that provide tailored results to their users.

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Acknowledgment

We acknowledge support of this work by the project “ParICT_CENG: Enhancing ICT research infrastructure in Central Greece to enable processing of Big data from sensor stream, multimedia content, and complex mathematical modeling and simulations” (MIS 5047244) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund).

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Authors and Affiliations

  1. Intelligent Systems Laboratory, Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

    Vasileios Tsoukas, Anargyros Gkogkidis & Athanasios Kakarountas

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  1. Vasileios Tsoukas
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  2. Anargyros Gkogkidis
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  3. Athanasios Kakarountas
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Correspondence to Athanasios Kakarountas .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Tsoukas, V., Gkogkidis, A., Kakarountas, A. (2022). Elements of TinyML on Constrained Resource Hardware. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_26

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  • DOI: https://doi.org/10.1007/978-3-031-12641-3_26

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