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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/" }