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Analyzing Machine Learning on Mainstream Microcontrollers

  • Vincenzo Falbo
  • Tommaso Apicella
  • Daniele Aurioso
  • Luisa Danese
  • Francesco Bellotti
  • Riccardo BertaEmail author
  • Alessandro De Gloria
Conference paper
  • 13 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 627)

Abstract

Machine learning in embedded systems has become a reality, with the first tools for neural network firmware development already being made available for ARM microcontroller developers. This paper explores the use of one of such tools, namely the STM X-Cube-AI, on mainstream ARM Cortex-M microcontrollers, analyzing their performance, and comparing support and performance of other two common supervised ML algorithms, namely Support Vector Machines (SVM) and k-Nearest Neighbours (k-NN). Results on three datasets show that X-Cube-AI provides quite constant good performance even with the limitations of the embedded platform. The workflow is well integrated with mainstream desktop tools, such as Tensorflow and Keras.

Keywords

Edge computing Machine learning Artificial neural networks Microcontrollers X-Cube-AI 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Vincenzo Falbo
    • 1
  • Tommaso Apicella
    • 1
  • Daniele Aurioso
    • 1
  • Luisa Danese
    • 1
  • Francesco Bellotti
    • 1
  • Riccardo Berta
    • 1
    Email author
  • Alessandro De Gloria
    • 1
  1. 1.DITEN, Università degli Studi di GenovaGenoaItaly

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