A Generalization Performance Study Using Deep Learning Networks in Embedded Systems
Open accessAbstract
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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Inference of DL in edge devices is not yet prepared enough to be fully executed.
“However, inference of DL in edge devices is not yet prepared enough to be fully executed [3,4,6].”
From TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications (2022) · cites this paper as [6]
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Jebali, F. (2023) · theses.fr (ABES) · doi:10.70675/b7632edfz3807z4b8fzb462z31a6dcf54161
State-of-the-art in automated image-based species identification systems
Weber, L., Hussen, M. M., Veeraraghavan, B., Meijer, E., Gütlin, Y., Guerrero-López, A., Brüningk, S. C., Nolte, O., Jutzeler, C. R., & Egli, A. (2026) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2026.1818023
- Authors
- Gorospe, J., Mulero, R., Arbelaitz, O., Muguerza, J., & Antón, M. Á.
- Affiliations
- Mondragon Unibertsitatea (ES)
- Tecnalia (ES)
- University of the Basque Country (ES)
- TECNALIA, Basque Research and Technology Alliance (BRTA), Astondo Bidea Building 700, 48160 Derio, Spain
- Corresponding author
- Gorospe, Joseba
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-02-03
- Volume
- 21
- Issue
- 4
- Pages
- 1031
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7913276
- OpenAlex
- W3128426802
- Cited by
- 32
- References
- 63
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 8 · 2023: 3 · 2022: 8 · 2021: 6
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Communities in ADDI (University of the Basque Country) (submittedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- eRepository Mondragon University (Mondragon University) (submittedVersion, cc-by)
- Hispana (submittedVersion, cc-by)
- Funders
- European Regional Development Fund (TIN2017-85409-P)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Embedded Systems and FPGA Design
- Keywords
- edge computing, TensorFlow Lite, microcontroller, real-time decision making, low-power inference
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