TinyML: Tools, applications, challenges, and future research directions
Kallimani, Rakhee; Pai, Krishna; Raghuwanshi, Prasoon; Iyer, Sridhar; López, Onel L.A. (2023-09-09)
Springer Science+Business Media
09.09.2023
Kallimani, R., Pai, K., Raghuwanshi, P. et al. TinyML: Tools, applications, challenges, and future research directions. Multimed Tools Appl 83, 29015–29045 (2024). https://doi.org/10.1007/s11042-023-16740-9
https://rightsstatements.org/vocab/InC/1.0/
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11042-023-16740-9.
https://rightsstatements.org/vocab/InC/1.0/
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11042-023-16740-9.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202406194749
https://urn.fi/URN:NBN:fi:oulu-202406194749
Tiivistelmä
Abstract
In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has limited their use mainly to high-capability devices such as network nodes. However, with many advancements in technologies such as the Internet of Things (IoT) and edge computing, it is desirable to incorporate ML techniques into resource-constrained embedded devices for distributed and ubiquitous intelligence. This has motivated the emergence of the TinyML paradigm which is an embedded ML technique that enables ML applications on multiple cheap, resource- and power-constrained devices. However, during this transition towards appropriate implementation of the TinyML technology, multiple challenges such as processing capacity optimisation, improved reliability, and maintenance of learning models’ accuracy require timely solutions. In this article, various avenues available for TinyML implementation are reviewed. Firstly, a background of TinyML is provided, followed by detailed discussions on various tools supporting TinyML. Then, state-of-art applications of TinyML using advanced technologies are detailed. Lastly, detailed prospects are presented which include various research challenges and identification of future directions.
In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has limited their use mainly to high-capability devices such as network nodes. However, with many advancements in technologies such as the Internet of Things (IoT) and edge computing, it is desirable to incorporate ML techniques into resource-constrained embedded devices for distributed and ubiquitous intelligence. This has motivated the emergence of the TinyML paradigm which is an embedded ML technique that enables ML applications on multiple cheap, resource- and power-constrained devices. However, during this transition towards appropriate implementation of the TinyML technology, multiple challenges such as processing capacity optimisation, improved reliability, and maintenance of learning models’ accuracy require timely solutions. In this article, various avenues available for TinyML implementation are reviewed. Firstly, a background of TinyML is provided, followed by detailed discussions on various tools supporting TinyML. Then, state-of-art applications of TinyML using advanced technologies are detailed. Lastly, detailed prospects are presented which include various research challenges and identification of future directions.
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