N-AquaRAM: A Cost-Efficient Deep Learning Accelerator for Real-Time Aquaponic Monitoring

Siddique, A., Iqbal, M.A., Sun, J. et al. (3 more authors) (2024) N-AquaRAM: A Cost-Efficient Deep Learning Accelerator for Real-Time Aquaponic Monitoring. Agricultural Research. ISSN 2249-720X

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Item Type: Article
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© The Author(s) 2024. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: Aquaculture; Deep learning accelerator; Field programmable gate arrays (FPGAs); Fish size estimation; Giga operations per second (GOPS); Smart aquaponics
Dates:
  • Published (online): 20 September 2024
  • Accepted: 30 August 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > SWJTU Joint School (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 20 Sep 2024 12:39
Last Modified: 20 Sep 2024 12:42
Status: Published online
Publisher: Springer
Identification Number: 10.1007/s40003-024-00788-6
Open Archives Initiative ID (OAI ID):

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