Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Badan:2019:TPNC,
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author = "Filip Badan and Lukas Sekanina",
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title = "Optimizing Convolutional Neural Networks for Embedded
Systems by Means of Neuroevolution",
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booktitle = "International Conference on Theory and Practice of
Natural Computing, TPNC 2019",
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year = "2019",
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editor = "Carlos Martin-Vide and Geoffrey Pond and
Miguel A. Vega-Rodriguez",
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volume = "11934",
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series = "LNCS",
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pages = "109--121",
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address = "Kingston, ON, Canada",
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month = "9-11 " # dec,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Evolutionary
Algorithm Convolutional neural network Neuroevolution
Embedded Systems Energy Efficiency",
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isbn13 = "978-3-030-34499-3",
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DOI = "doi:10.1007/978-3-030-34500-6_7",
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size = "13 pages",
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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.",
- }
Genetic Programming entries for
Filip Badan
Lukas Sekanina
Citations