Power Efficient Machine Learning Models Deployment on Edge IoT Devices

Computing has undergone a significant transformation over the past two decades, shifting from a machine-based approach to a human-centric, virtually invisible service known as ubiquitous or pervasive computing. This change has been achieved by incorporating small embedded devices into a larger compu...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 23; číslo 3; s. 1595
Hlavní autori:	Fanariotis, Anastasios, Orphanoudakis, Theofanis, Kotrotsios, Konstantinos, Fotopoulos, Vassilis, Keramidas, George, Karkazis, Panagiotis
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 01.02.2023 MDPI
Predmet:	Accuracy Algorithms Analysis Artificial intelligence autonomous systems Computational linguistics Computer vision edge computing Efficiency Embedded systems Energy use Internet of Things Language processing Machine learning Machine vision Natural language interfaces Neural networks neural networks compression Optimization techniques Power power efficiency Smartphones United States New Jersey autonomous systems machine learning edge computing neural networks compression power efficiency
ISSN:	1424-8220, 1424-8220
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