A Generalization Performance Study Using Deep Learning Networks in Embedded Systems
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2021-02-03
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Multidisciplinary Digital Publishing Institute (MDPI)
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Abstract
Deep learning techniques are being increasingly used in the scientific community as a
consequence of the high computational capacity of current systems and the increase in the amount
of data available as a result of the digitalisation of society in general and the industrial world in
particular. In addition, the immersion of the field of edge computing, which focuses on integrating
artificial intelligence as close as possible to the client, makes it possible to implement systems that act
in real time without the need to transfer all of the data to centralised servers. The combination of these
two concepts can lead to systems with the capacity to make correct decisions and act based on them
immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this
integration, so the possibility of being able to integrate them into a wide range of micro-controllers
can be a great advantage. This paper contributes with the generation of an environment based on
Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing
the introduction of deep learning architectures. The experiments herein prove that the proposed
system is competitive if compared to other commercial systems.
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Gorospe, Joseba, Rubén Mulero, Olatz Arbelaitz, Javier Muguerza, and Miguel Ángel Antón. “A Generalization Performance Study Using Deep Learning Networks in Embedded Systems.” Sensors 21, no. 4 (February 3, 2021): 1031. doi:10.3390/s21041031.