Author, as appears in the article.: Padilla, Daniel; Rashwan, Hatem A; Savi Puig, Domenec
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Padilla Carrasco, Daniel / Puig Valls, Domènec Savi
Keywords: Deep learning Dsp Embedded systems Fpga Gpu So Soc
Abstract: Deep learning (DL) networks have proven to be crucial in commercial solutions with computer vision challenges due to their abilities to extract high-level abstractions of the image data and their capabilities of being easily adapted to many applications. As a result, DL methodologies had become a de facto standard for computer vision problems yielding many new kinds of research, approaches and applications. Recently, the commercial sector is also driving to use of embedded systems to be able to execute DL models, which has caused an important change on the DL panorama and the embedded systems themselves. Consequently, in this paper, we attempt to study the state of the art of embedded systems, such as GPUs, FPGAs and Mobile SoCs, that are able to use DL techniques, to modernize the stakeholders with the new systems available in the market. Besides, we aim at helping them to determine which of these systems can be beneficial and suitable for their applications in terms of upgradeability, price, deployment and performance.
Thematic Areas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: domenec.puig@urv.cat daniel.padilla@estudiants.urv.cat daniel.padilla@estudiants.urv.cat hatem.abdellatif@urv.cat
Author identifier: 0000-0002-0562-4205 0000-0001-5421-1637
Record's date: 2024-09-21
Papper version: info:eu-repo/semantics/publishedVersion
Papper original source: Frontiers In Artificial Intelligence And Applications. 339 285-294
APA: Padilla, Daniel; Rashwan, Hatem A; Savi Puig, Domenec (2021). On Determining Suitable Embedded Devices for Deep Learning Models. Amsterdam: IOS Press
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Proceedings Paper
Repositori URV