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A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have be...

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Autores principales: Biglari, Amin, Tang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959746/
https://www.ncbi.nlm.nih.gov/pubmed/36850729
http://dx.doi.org/10.3390/s23042131
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author Biglari, Amin
Tang, Wei
author_facet Biglari, Amin
Tang, Wei
author_sort Biglari, Amin
collection PubMed
description Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.
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spelling pubmed-99597462023-02-26 A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme Biglari, Amin Tang, Wei Sensors (Basel) Review Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness. MDPI 2023-02-14 /pmc/articles/PMC9959746/ /pubmed/36850729 http://dx.doi.org/10.3390/s23042131 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Biglari, Amin
Tang, Wei
A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme
title A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme
title_full A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme
title_fullStr A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme
title_full_unstemmed A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme
title_short A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme
title_sort review of embedded machine learning based on hardware, application, and sensing scheme
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959746/
https://www.ncbi.nlm.nih.gov/pubmed/36850729
http://dx.doi.org/10.3390/s23042131
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