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Comparison of performance of optimized HSI CNN models on desktop and embedded platforms

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00009863" target="_blank" >RIV/46747885:24220/21:00009863 - isvavai.cz</a>

  • Alternative codes found

    RIV/46747885:24220/21:00008992

  • Result on the web

    <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9542900" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9542900</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.23919/AE51540.2021.9542900" target="_blank" >10.23919/AE51540.2021.9542900</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Comparison of performance of optimized HSI CNN models on desktop and embedded platforms

  • Original language description

    We compare different platforms for inference of convolutional neural networks in this paper. We trained various neural networks to determine the material in the source hyperspectral cube. Then we convert them to inference format and compare the inference results. We used tools under Xilinx Vitis AI for FPGA implementation. We try to minimize the size of the proposed networks by pruning them and provide further comparisons. FPGA platforms show to be energy efficient but still slower than a graphics card in terms of performance.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10200 - Computer and information sciences

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    International Conference on Applied Electronics

  • ISBN

    978-802610972-3

  • ISSN

    18037232

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

  • Publisher name

    IEEE

  • Place of publication

  • Event location

    Pilsen

  • Event date

    Jan 1, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article