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 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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:102, p 04019 |
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| Enthalten in: |
SHS Web of Conferences - 102, p 04019(2021) |
| Sprache: |
Englisch ; Französisch |
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| Beteiligte Personen: |
Yuuki Okada [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
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| doi: |
10.1051/shsconf/202110204019 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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DOAJ002753812 |
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