Hardware Accelerator for Ethanol Detection in Water Media based on Machine Learning Techniques

Mariño Andrés, Rodrigo and Quintero Moreno, Sergio Andrés and Lanza-Gutiérrez, José Manuel and Riesgo Alcaide, Teresa and Holgado Bolaños, Miguel and Portilla Berrueco, Jorge and Torre Arnanz, Eduardo de la (2019). Hardware Accelerator for Ethanol Detection in Water Media based on Machine Learning Techniques. In: "DCIS 2019 XXXIV Conference on Design of Circuits and Integrated Systems", 20-22 noviembre 2019, Bilbao, Spain. ISBN 978-1-7281-5458-9. pp. 1-6. https://doi.org/10.1109/DCIS201949030.2019.8959937.

Description

Title: Hardware Accelerator for Ethanol Detection in Water Media based on Machine Learning Techniques
Author/s:
  • Mariño Andrés, Rodrigo
  • Quintero Moreno, Sergio Andrés
  • Lanza-Gutiérrez, José Manuel
  • Riesgo Alcaide, Teresa
  • Holgado Bolaños, Miguel
  • Portilla Berrueco, Jorge
  • Torre Arnanz, Eduardo de la
Item Type: Presentation at Congress or Conference (Article)
Event Title: DCIS 2019 XXXIV Conference on Design of Circuits and Integrated Systems
Event Dates: 20-22 noviembre 2019
Event Location: Bilbao, Spain
Title of Book: 2019 XXXIV Conference on Design of Circuits and Integrated Systems (DCIS)
Date: 2019
ISBN: 978-1-7281-5458-9
Subjects:
Freetext Keywords: Smart Farming; Optical Sensing; MachineLearning; Feature Extraction; SoPC
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2017-86722-C4-2-RPLATINOUnspecifiedPLATAFORMA HW/SW DISTRIBUIDA PARA EL PROCESAMIENTO INTELIGENTE DE INFORMACION SENSORIAL HETEROGENEA EN APLICACIONES DE SUPERVISION DE GRANDES ESPACIOS NATURALES
Government of SpainTEC2017-84846-RHERONUnspecifiedTransductores avanzados, biochips y plataformas de lectura para biosensores de alto rendimiento, detección de líquidos y monitorización de células
Universidad Politécnica de MadridRR24/2017UnspecifiedUnspecifiedUnspecified

More information

Item ID: 64959
DC Identifier: http://oa.upm.es/64959/
OAI Identifier: oai:oa.upm.es:64959
DOI: 10.1109/DCIS201949030.2019.8959937
Official URL: https://dcis2019.org/
Deposited by: Memoria Investigacion
Deposited on: 26 Oct 2020 17:21
Last Modified: 26 Oct 2020 17:21
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