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    A Re-configurable Software-Hardware CNN Framework for Automatic Detection of Respiratory Symptoms

    Files
    Chapter 42 A Re-configurable Software-Hardware CNN Framework for Automatic Detection of Respiratory Symptoms (1).pdf (4.000Mb)
    Links to Files
    https://link.springer.com/chapter/10.1007/978-3-031-10031-4_4
    Permanent Link
    https://doi.org/10.1007/978-3-031-10031-4_4
    http://hdl.handle.net/11603/26365
    Collections
    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
    • UMBC Information Systems Department
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    Metadata
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    Author/Creator
    Rashid, Hasib-Al
    Ren, Haoran
    Mazumder, Arnab
    Sajadi, Mohammad M.
    Mohsenin, Tinoosh
    Author/Creator ORCID
    https://orcid.org/0000-0002-9983-6929
    https://orcid.org/0000-0002-9550-7917
    Date
    2022-10-30
    Type of Work
    20 pages
    Text
    book chapters
    Citation of Original Publication
    Rashid, HA., Ren, H., Mazumder, A.N., Sajadi, M.M., Mohsenin, T. (2022). A Re-configurable Software-Hardware CNN Framework for Automatic Detection of Respiratory Symptoms. In: Adibi, S., Rajabifard, A., Shariful Islam, S.M., Ahmadvand, A. (eds) The Science behind the COVID Pandemic and Healthcare Technology Solutions. Springer Series on Bio- and Neurosystems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-031-10031-4_4
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Access to this file will begin on 10-30-2024
    Abstract
    Detection of respiratory symptoms has long been an area of extensive research to expedite the process of machine aided diagnosis for various respiratory conditions. This chapter attempts to address the early diagnosis of respiratory conditions using low power scalable software and hardware involving end-to-end convolutional neural networks (CNNs). We propose RespiratorNet, a scalable multimodal CNN software hardware architecture that can take audio recordings, speech information, and other sensor modalities belonging to patient demographic or symptom information as input to classify different respiratory symptoms. We analyze four different publicly available datasets and use them as case studies as part of our experiment to classify respiratory symptoms. With regards to fitting the network architecture to the hardware framework, we perform windowing, low bit-width quantization, and hyperparameter optimization on the software side. As per our analysis, detection accuracy goes up by 5% when patient demographic information is included in the network architecture. The hardware prototype is designed using Verilog HDL on Xilinx Artix-7 100t FPGA with hardware scalability extending to accommodate different numbers of processing engines for parallel processing. The proposed hardware implementation has a low power consumption of only 245 mW and achieves an energy efficiency of 7.3 GOPS/W which is 4.3× better than the state-of the-art accelerator implementations. In addition, RespiratorNet TensorFlow model is implemented on NVIDIA Jetson TX2 SoC (CPU + GPU) and compared to TX2 single-core CPU and GPU implementations to provide scalability in terms of off-the-shelf platform implementations.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.