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Performance/resources comparison of hardware implementations on fully connected network inference

Autor(es) y otros:
Lozada, R.; Ruiz, J.; González, M. L.; Sedano, J.; Villar Flecha, José RamónAutoridad Uniovi; García Vico, Á. M.; Skibinsky Gitlin, E. S.
Fecha de publicación:
2022
Versión del editor:
http://dx.doi.org/10.1007/978-3-031-21753-1_34
Citación:
Lozada, R., Ruiz, J., González, M. L., Sedano, J., Villar Flecha, J. R., García Vico, A. M., Skibinsky Gitlin, E. S. (2022). Performance/resources comparison of hardware implementations on fully connected network inference. En: H. Yin, D. Camacho, P. Tino (eds.). Intelligent data engineering and automated learning – IDEAL 2022 (pp. 348-358). Cham : Springer
Serie:

Lecture Notes in Computer Science book series (LNCS); v. 13756

Descripción física:
p. 348-358
Descripción:

Intelligent Data Engineering and Automated Learning – IDEAL 2022 (23rd. 2022. Manchester)

URI:
http://hdl.handle.net/10651/68163
ISBN:
9783031217524
ISSN:
0302-9743
DOI:
10.1007/978-3-031-21753-1_34
Patrocinado por:

This work has been founded by the following institutions: the Ministry of Science and Innovation under CERVERA Excellence Network project CER-20211003 (IBERUS), Missions Science and Innovation project MIG-20211008 (INMERBOT), European Union’s Horizon 2020 research and innovation programme (project DIH4CPS) under the Grant Agreement no 872548, CDTI (Centro para el Desarrollo Tecnológico Industrial) under projects CER-20211022, ICE (Junta de Castilla y León) under project CCTT3/20/BU/0002, the Spanish Ministry of Economics and Industry under the grant PID2020-112726RB-I00, the Principado de Asturias under the grant SV-PA-21-AYUD/2021/50994 and the Regional Government of Andalusia, program “Personal Investigador Doctor”, reference DOC_00235.

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