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
Abstract
Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to increase crop production. Though it offers a lot of advantages over traditional techniques, including chemical-free and soil-less farming, its commercial application suffers from some problems such as the lack of experienced manpower. To operate a stable smart aquaponic system, it is critical to estimate the fish size properly. In this context, the use of dedicated hardware for real-time aquaponic monitoring can greatly resolve the issue of inexperienced handlers. In this article, we present a complete methodology to train a deep neural network to perform fish size estimation in real time. To achieve high accuracy, a novel implementation of swish function is presented. This novel version is far more hardware efficient than the original one, while being extremely accurate. Moreover, we present a deep learning accelerator that can classify 40 million fish samples in a second. The dedicated real-time system is about 1600 times faster than the one based on general-purpose computers. The proposed neuromorphic accelerator consumes about 2600 slice registers on a low-end model of Virtex 6 FPGA series.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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: |
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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): | oai:eprints.whiterose.ac.uk:217389 |