Optimizing Convolutional Neural Networks for Embedded Systems By Means of Neuroevolution

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134959" target="_blank" >RIV/00216305:26230/19:PU134959 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/publication/12045/" target="_blank" >https://www.fit.vut.cz/research/publication/12045/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-34500-6_7" target="_blank" >10.1007/978-3-030-34500-6_7</a>

Result language
angličtina
Original language name
Optimizing Convolutional Neural Networks for Embedded Systems By Means of Neuroevolution
Original language description
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.
Czech name
—
Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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