Publication Details

Optimizing Convolutional Neural Networks for Embedded Systems By Means of Neuroevolution

BADÁŇ, F.; SEKANINA, L. Optimizing Convolutional Neural Networks for Embedded Systems By Means of Neuroevolution. In Theory and Practice of Natural Computing. LNCS 11934. Cham: Springer International Publishing, 2019. p. 109-121. ISBN: 978-3-030-34499-3.
Czech title
Optimalizace konvolučních neuronových sítí pro vestavěné systémy s využitím neuroevoluce
Type
conference paper
Language
English
Authors
Badáň Filip, Ing.
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Keywords

Evolutionary Algorithm, Convolutional neural network, Neuroevolution,  Embedded Systems, Energy Efficiency

Abstract

Automated design methods for convolutional neural networks (CNNs) have recently been developed in order to increase the design productivity. We propose a neuroevolution method capable of evolving and optimizing CNNs with respect to the classification error and CNN complexity (expressed as the number of tunable CNN parameters), in which the inference phase can partly be executed using fixed point operations to further reduce power consumption. Experimental results are obtained with TinyDNN framework and presented using two common image classification benchmark problems - MNIST and CIFAR-10.

Published
2019
Pages
109–121
Proceedings
Theory and Practice of Natural Computing
Series
LNCS 11934
ISBN
978-3-030-34499-3
Publisher
Springer International Publishing
Place
Cham
DOI
10.1007/978-3-030-34500-6_7
UT WoS
000611522600007
EID Scopus
2-s2.0-85077000581
BibTeX 
@inproceedings{BUT161459,
  author="Filip {Badáň} and Lukáš {Sekanina}",
  title="Optimizing Convolutional Neural Networks for Embedded Systems By Means of Neuroevolution",
  booktitle="Theory and Practice of Natural Computing",
  year="2019",
  series="LNCS 11934",
  pages="109--121",
  publisher="Springer International Publishing",
  address="Cham",
  doi="10.1007/978-3-030-34500-6\{_}7",
  isbn="978-3-030-34499-3",
  url="https://www.fit.vut.cz/research/publication/12045/"
}
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