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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20206 - Computer hardware and architecture

Result continuities

  • Project

  • 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

  • e-ISSN

  • Number of pages

    4

  • Pages from-to

  • 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