Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme

The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows tha...

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Autores principales:	Olutosin Ajibola Ademola, Mairo Leier, Eduard Petlenkov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep neural network compression compression method evaluation weighted score-based ranking embedded deep learning edge computing Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/348a22621e7447a08b4c016aa14abe64
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spelling	oai:doaj.org-article:348a22621e7447a08b4c016aa14abe642021-11-25T18:57:15ZEvaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme10.3390/s212275291424-8220https://doaj.org/article/348a22621e7447a08b4c016aa14abe642021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7529https://doaj.org/toc/1424-8220The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows that the models need to be optimized for the hardware without performance degradation. There exist several model compression methods; however, determining the most efficient method is of major concern. Our goal was to rank the performance of these methods using our application as a case study. We aimed to develop a real-time vehicle tracking system for cargo ships. To address this, we developed a weighted score-based ranking scheme that utilizes the model performance metrics. We demonstrated the effectiveness of this method by applying it on the baseline, compressed, and micro-CNN models trained on our dataset. The result showed that quantization is the most efficient compression method for the application, having the highest rank, with an average weighted score of 9.00, followed by binarization, having an average weighted score of 8.07. Our proposed method is extendable and can be used as a framework for the selection of suitable model compression methods for edge devices in different applications.Olutosin Ajibola AdemolaMairo LeierEduard PetlenkovMDPI AGarticledeep neural network compressioncompression method evaluationweighted score-based rankingembedded deep learningedge computingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7529, p 7529 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep neural network compression compression method evaluation weighted score-based ranking embedded deep learning edge computing Chemical technology TP1-1185
spellingShingle	deep neural network compression compression method evaluation weighted score-based ranking embedded deep learning edge computing Chemical technology TP1-1185 Olutosin Ajibola Ademola Mairo Leier Eduard Petlenkov Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme
description	The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows that the models need to be optimized for the hardware without performance degradation. There exist several model compression methods; however, determining the most efficient method is of major concern. Our goal was to rank the performance of these methods using our application as a case study. We aimed to develop a real-time vehicle tracking system for cargo ships. To address this, we developed a weighted score-based ranking scheme that utilizes the model performance metrics. We demonstrated the effectiveness of this method by applying it on the baseline, compressed, and micro-CNN models trained on our dataset. The result showed that quantization is the most efficient compression method for the application, having the highest rank, with an average weighted score of 9.00, followed by binarization, having an average weighted score of 8.07. Our proposed method is extendable and can be used as a framework for the selection of suitable model compression methods for edge devices in different applications.
format	article
author	Olutosin Ajibola Ademola Mairo Leier Eduard Petlenkov
author_facet	Olutosin Ajibola Ademola Mairo Leier Eduard Petlenkov
author_sort	Olutosin Ajibola Ademola
title	Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme
title_short	Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme
title_full	Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme
title_fullStr	Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme
title_full_unstemmed	Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme
title_sort	evaluation of deep neural network compression methods for edge devices using weighted score-based ranking scheme
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/348a22621e7447a08b4c016aa14abe64
work_keys_str_mv	AT olutosinajibolaademola evaluationofdeepneuralnetworkcompressionmethodsforedgedevicesusingweightedscorebasedrankingscheme AT mairoleier evaluationofdeepneuralnetworkcompressionmethodsforedgedevicesusingweightedscorebasedrankingscheme AT eduardpetlenkov evaluationofdeepneuralnetworkcompressionmethodsforedgedevicesusingweightedscorebasedrankingscheme
_version_	1718410515292618752

Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme

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