Feasibility of a Neural Network with Linearly Approximated Functions on Zynq FPGA
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00362892" target="_blank" >RIV/68407700:21240/22:00362892 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICECS202256217.2022.9970813" target="_blank" >http://dx.doi.org/10.1109/ICECS202256217.2022.9970813</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICECS202256217.2022.9970813" target="_blank" >10.1109/ICECS202256217.2022.9970813</a>
Alternative languages
Result language
angličtina
Original language name
Feasibility of a Neural Network with Linearly Approximated Functions on Zynq FPGA
Original language description
This paper is focused on the feasibility of a neural network with linearly approximated functions on modern FPGA. An approximate multiplier and linearly approximated activation functions were used for a neural network implemented on Zynq FPGA. We proposed a novel architecture for a fully functional, layered, and configurable neural network.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20206 - Computer hardware and architecture
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
ISBN
978-1-6654-8823-5
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
New York
Event location
Glasgow
Event date
Oct 24, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000913346300023