Hardware Acceleration of Convolution Neural Network for AI-Enabled Realtime Biomedical System

Hardware Acceleration of Convolution Neural Network for AI-Enabled Realtime Biomedical System

COVID-19 is currently on the rage all over the world and has become a pandemic. To efficiently handle it, accurate diagnosis and prompt reporting are essential. The AI-Enabled Real-time Biomedical System (AIRBiS) research project aims to develop a system that handles diagnosis using chest X-ray imag...

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Main Authors: Yuuki Okada, Wang Jiangkun, Mark Ikechukwu Ogbodo, Ben Abdallah Abderazek
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:SHS Web of Conferences
Online Access:https://www.shs-conferences.org/articles/shsconf/pdf/2021/13/shsconf_etltc2021_04019.pdf
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spelling doaj-4bab54333b874a609f12ab0def436c642021-05-04T12:25:01ZengEDP SciencesSHS Web of Conferences2261-24242021-01-011020401910.1051/shsconf/202110204019shsconf_etltc2021_04019Hardware Acceleration of Convolution Neural Network for AI-Enabled Realtime Biomedical SystemYuuki Okada0Wang Jiangkun1Mark Ikechukwu Ogbodo2Ben Abdallah Abderazek3The University of Aizu, School of Computer Science and Engineering, Adaptive Systems LaboratoryThe University of Aizu, School of Computer Science and Engineering, Adaptive Systems LaboratoryThe University of Aizu, School of Computer Science and Engineering, Adaptive Systems LaboratoryThe University of Aizu, School of Computer Science and Engineering, Adaptive Systems LaboratoryCOVID-19 is currently on the rage all over the world and has become a pandemic. To efficiently handle it, accurate diagnosis and prompt reporting are essential. The AI-Enabled Real-time Biomedical System (AIRBiS) research project aims to develop a system that handles diagnosis using chest X-ray images. The project is divided into UI, network, software and hardware. This work focuses on the hardware, which uses CNN technology to create a model that determines the presence of pneumonia. This CNN model is designed on an FPGA to speed up diagnostic results. The FPGA increases the flexibility of circuit design, allowing us to optimize the computational processing during data transfer and CNN implementation, reducing the diagnostic measurement time for a single image.https://www.shs-conferences.org/articles/shsconf/pdf/2021/13/shsconf_etltc2021_04019.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Yuuki Okada
Wang Jiangkun
Mark Ikechukwu Ogbodo
Ben Abdallah Abderazek
spellingShingle Yuuki Okada
Wang Jiangkun
Mark Ikechukwu Ogbodo
Ben Abdallah Abderazek
Hardware Acceleration of Convolution Neural Network for AI-Enabled Realtime Biomedical System
SHS Web of Conferences
author_facet Yuuki Okada
Wang Jiangkun
Mark Ikechukwu Ogbodo
Ben Abdallah Abderazek
author_sort Yuuki Okada
title Hardware Acceleration of Convolution Neural Network for AI-Enabled Realtime Biomedical System
title_short Hardware Acceleration of Convolution Neural Network for AI-Enabled Realtime Biomedical System
title_full Hardware Acceleration of Convolution Neural Network for AI-Enabled Realtime Biomedical System
title_fullStr Hardware Acceleration of Convolution Neural Network for AI-Enabled Realtime Biomedical System
title_full_unstemmed Hardware Acceleration of Convolution Neural Network for AI-Enabled Realtime Biomedical System
title_sort hardware acceleration of convolution neural network for ai-enabled realtime biomedical system
publisher EDP Sciences
series SHS Web of Conferences
issn 2261-2424
publishDate 2021-01-01
description COVID-19 is currently on the rage all over the world and has become a pandemic. To efficiently handle it, accurate diagnosis and prompt reporting are essential. The AI-Enabled Real-time Biomedical System (AIRBiS) research project aims to develop a system that handles diagnosis using chest X-ray images. The project is divided into UI, network, software and hardware. This work focuses on the hardware, which uses CNN technology to create a model that determines the presence of pneumonia. This CNN model is designed on an FPGA to speed up diagnostic results. The FPGA increases the flexibility of circuit design, allowing us to optimize the computational processing during data transfer and CNN implementation, reducing the diagnostic measurement time for a single image.
url https://www.shs-conferences.org/articles/shsconf/pdf/2021/13/shsconf_etltc2021_04019.pdf
work_keys_str_mv AT yuukiokada hardwareaccelerationofconvolutionneuralnetworkforaienabledrealtimebiomedicalsystem
AT wangjiangkun hardwareaccelerationofconvolutionneuralnetworkforaienabledrealtimebiomedicalsystem
AT markikechukwuogbodo hardwareaccelerationofconvolutionneuralnetworkforaienabledrealtimebiomedicalsystem
AT benabdallahabderazek hardwareaccelerationofconvolutionneuralnetworkforaienabledrealtimebiomedicalsystem
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