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Evolutionary optimization of neural networks with heterogeneous computation: study and implementation

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Evolutionary optimization of neural networks with heterogeneous computation: study and implementation

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Fe, JD.; Aliaga Varea, RJ.; Gadea Gironés, R. (2015). Evolutionary optimization of neural networks with heterogeneous computation: study and implementation. The Journal of Supercomputing. 71(8):2944-2962. doi:10.1007/s11227-015-1419-7

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/64230

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Title: Evolutionary optimization of neural networks with heterogeneous computation: study and implementation
Author: Fe, Jorge Deolindo Aliaga Varea, Ramón José Gadea Gironés, Rafael
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
Issued date:
Abstract:
In the optimization of artificial neural networks (ANNs) via evolutionary algorithms and the implementation of the necessary training for the objective function, there is often a trade-off between efficiency and flexibility. ...[+]
Subjects: Evolutionary computation , Embedded system , FPGA , Neural networks
Copyrigths: Reserva de todos los derechos
Source:
The Journal of Supercomputing. (issn: 0920-8542 )
DOI: 10.1007/s11227-015-1419-7
Publisher:
Springer Netherlands
Publisher version: http://dx.doi.org/10.1007/s11227-015-1419-7
Project ID:
Universitat Politecnica de Valencia, Spain
Thanks:
The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain.
Type: Artículo

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