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.
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Falbo, V. et al. (2020). Analyzing Machine Learning on Mainstream Microcontrollers. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2019. Lecture Notes in Electrical Engineering, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-37277-4_12
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DOI: https://doi.org/10.1007/978-3-030-37277-4_12
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