RT Journal Article
T1 A Generalization Performance Study Using Deep Learning Networks in Embedded Systems
A1 Gorospe, Joseba
A1 Mulero, Rubén
A1 Arbelaitz, Olatz
A1 Muguerza, Javier
A1 Antón, Miguel Ángel
AB Deep learning techniques are being increasingly used in the scientific community as aconsequence of the high computational capacity of current systems and the increase in the amountof data available as a result of the digitalisation of society in general and the industrial world inparticular. In addition, the immersion of the field of edge computing, which focuses on integratingartificial intelligence as close as possible to the client, makes it possible to implement systems that actin real time without the need to transfer all of the data to centralised servers. The combination of thesetwo concepts can lead to systems with the capacity to make correct decisions and act based on themimmediately and in situ. Despite this, the low capacity of embedded systems greatly hinders thisintegration, so the possibility of being able to integrate them into a wide range of micro-controllerscan be a great advantage. This paper contributes with the generation of an environment based onMbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowingthe introduction of deep learning architectures. The experiments herein prove that the proposedsystem is competitive if compared to other commercial systems.
PB Multidisciplinary Digital Publishing Institute (MDPI)
YR 2021
FD 2021-02-03
LK http://hdl.handle.net/11556/1078
UL http://hdl.handle.net/11556/1078
LA eng
NO 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.
NO This research was supported by Tecnalia, Basque Research, and the ERDF/SpanishMinistry of Science, Innovation and Universities–National Research Agency/PhysComp project underGrant Number TIN2017-85409-P, in collaboration with the University of the Basque Country.
DS TECNALIA Publications
RD 1 jul 2024