Detail publikace

Optimizing Convolutional Neural Networks for Embedded Systems By Means of Neuroevolution

BADÁŇ, F. SEKANINA, L.

Originální název

Optimizing Convolutional Neural Networks for Embedded Systems By Means of Neuroevolution

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

BADÁŇ, F.; SEKANINA, L.

Vydáno

22. 11. 2019

Nakladatel

Springer International Publishing

Místo

Cham

ISBN

978-3-030-34499-3

Kniha

Theory and Practice of Natural Computing

Edice

LNCS 11934

Strany od

109

Strany do

121

Strany počet

13

URL

https://www.fit.vut.cz/research/publication/12045/

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/"
}