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Developing a Machine Learning Library for Microcontrollers

  • Andrea Parodi
  • Francesco BellottiEmail author
  • Riccardo Berta
  • Alessandro De Gloria
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 573)

Abstract

With the appearance of tools to support the emerging paradigm of edge computing, we expect that low cost microcontrollers will become appealing execution platforms also for machine learning. To explore this field, we implemented Machine Learning eMbedded Library (ML)2 and tested it in a simple case (classifying human movement as normal or not) and with a benchmark dataset to have a first comparison in performance with other implemented algorithms. Results—in terms of accuracy and of execution time, both for training and classification—are promising, and encourage the next steps of our work, in the direction of extending the set of implemented algorithms and going more in depth with the testing. In any case, we believe that these preliminary results should spur the Internet of Things research community in devising distributed computing algorithms able to support ML computation as close as possible to the source.

References

  1. 1.
    Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)CrossRefGoogle Scholar
  2. 2.
    Shi, W., Dustdar, S.: The promise of edge computing. Computer 49(5), 78–81 (2016)CrossRefGoogle Scholar
  3. 3.
    Arias, P., Kelley, C., Mason, J., Bryant, K. and Roy, K.: Classification of user movement data, ICDSP 2018, February 25–27, 2018, Tokyo, JapanGoogle Scholar
  4. 4.
  5. 5.
  6. 6.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Andrea Parodi
    • 1
  • Francesco Bellotti
    • 1
    Email author
  • Riccardo Berta
    • 1
  • Alessandro De Gloria
    • 1
  1. 1.DITEN, Università degli Studi di GenovaGenoaItaly

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