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Abstract
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| In the last years, the Industry 4.0 paradigm is gaining relevance in the agro-food industry, leading to Smart Farming. One of the applications in the Smart Farming domain is the advanced chemical analysis in process monitoring using distributed, low-cost embedded systems. Optical sensing technology is used in conjunction with machine learning techniques for this advanced analysis. From the embedded system perspective, it might be required to propose a method for the implementation of machine learning techniques in heterogeneous platforms. This paper focuses on implementing Machine Learning techniques in a System on Programmable Chip, based on an FPGA and ARM processors. As a use case, we mimic water pollution by ethanol. Thus, the application might determine the percentage of ethanol of the water during run-time. As a result, this paper provides a methodology for implementing a machine learning technique for ethanol prediction using an FPGA, and the study of its parameters as resource utilization and accelerator latency for the architecture proposed. | |
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International
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Si |
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Congress
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XXXIV Conference on Design of Circuits and Integrated Systems (DCIS) |
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960 |
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Place
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Bilbao, Spain, Spain |
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Reviewers
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Si |
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ISBN/ISSN
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2471-6170 |
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10.1109/DCIS201949030.2019.8959937 |
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Start Date
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20/11/2019 |
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End Date
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22/11/2019 |
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From page
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1 |
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To page
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6 |
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Hardware Accelerator for Ethanol Detection in Water Media based on Machine Learning Techniques |