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Detail publikačního výsledku
BADÁŇ, F.; SEKANINA, L.
Originální název
Optimizing Convolutional Neural Networks for Embedded Systems By Means of Neuroevolution
Anglický název
Druh
Stať ve sborníku v databázi WoS či Scopus
Originální abstrakt
Automateddesign methods for convolutional neural networks (CNNs) have recently beendeveloped in order to increase the design productivity. We propose aneuroevolution method capable of evolving and optimizing CNNs with respect tothe classification error and CNN complexity (expressed as the number of tunableCNN parameters), in which the inference phase can partly be executed usingfixed point operations to further reduce power consumption. Experimentalresults are obtained with TinyDNN framework and presented using two common imageclassification benchmark problems - MNIST and CIFAR-10.
Anglický abstrakt
Klíčová slova
Evolutionary Algorithm, Convolutional neural network, Neuroevolution, Embedded Systems, Energy Efficiency
Klíčová slova v angličtině
Autoři
Rok RIV
2020
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/" }
Dokumenty
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