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...

Full description

Saved in:

Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 23; no. 3; p. 1595
Main Authors:	Fanariotis, Anastasios, Orphanoudakis, Theofanis, Kotrotsios, Konstantinos, Fotopoulos, Vassilis, Keramidas, George, Karkazis, Panagiotis
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.02.2023 MDPI
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!