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TinyML: Tools, applications, challenges, and future research directions

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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.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, KLE Technological University Dr. M.S. Sheshgiri Campus, 590008, Belagavi, Karnataka, India

    Rakhee Kallimani

  2. Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, 90014, Finland

    Prasoon Raghuwanshi & Onel L. A. López

  3. Department of CSE(AI), KLE Technological University Dr. M.S. Sheshgiri Campus, 590008, Belagavi, Karnataka, India

    Sridhar Iyer

  4. Department of Electronics and Communication Engineering, KLE Technological University Dr. M.S. Sheshgiri Campus, 590008, Karnataka, Belagavi, India

    Krishna Pai

Authors
  1. Rakhee Kallimani
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  2. Krishna Pai
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  3. Prasoon Raghuwanshi
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  4. Sridhar Iyer
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  5. Onel L. A. López
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Contributions

All the Authors contributed equally to the study and approved the final manuscript

Corresponding author

Correspondence to Krishna Pai.

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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

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