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Hyperparameters Optimization Using GridSearchCV Method for TinyML Models

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Progress on Pattern Classification, Image Processing and Communications (CORES 2023, IP&C 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 766))

  • 53 Accesses

Abstract

The anticipated expansion of TinyML-related technologies will require methods for efficient ML model development. In this paper, we present a method for obtaining a set of ML models for TinyML systems that satisfies the assumption that a more efficient model is also more complex and therefore consumes more energy. The results show that our method is capable of providing numerous diversified sets of ML models.

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Author information

Authors and Affiliations

  1. Department of Systems and Computer Networks, Wrocław University of Science and Technology, Wrocław, Poland

    Tobiasz Puślecki & Krzysztof Walkowiak

Authors
  1. Tobiasz Puślecki
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  2. Krzysztof Walkowiak
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Corresponding author

Correspondence to Tobiasz Puślecki .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Robert Burduk

  2. University of Technology and Life Science, Bydgoszcz, Poland

    Michał Choraś

  3. University of Technology and Life Science, Bydgoszcz, Poland

    Rafał Kozik

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Paweł Ksieniewicz

  5. University of Technology and Life Science, Bydgoszcz, Poland

    Tomasz Marciniak

  6. Wrocław University of Science and Technology, Wrocław, Poland

    Paweł Trajdos

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Puślecki, T., Walkowiak, K. (2023). Hyperparameters Optimization Using GridSearchCV Method for TinyML Models. In: Burduk, R., Choraś, M., Kozik, R., Ksieniewicz, P., Marciniak, T., Trajdos, P. (eds) Progress on Pattern Classification, Image Processing and Communications. CORES IP&C 2023 2023. Lecture Notes in Networks and Systems, vol 766. Springer, Cham. https://doi.org/10.1007/978-3-031-41630-9_7

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