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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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
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Summary: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 computational system, connected through networking and referred to as edge devices. When these devices are also connected to the Internet, they are generally named Internet-of-Thing (IoT) devices. Developing Machine Learning (ML) algorithms on these types of devices allows them to provide Artificial Intelligence (AI) inference functions such as computer vision, pattern recognition, etc. However, this capability is severely limited by the device’s resource scarcity. Embedded devices have limited computational and power resources available while they must maintain a high degree of autonomy. While there are several published studies that address the computational weakness of these small systems-mostly through optimization and compression of neural networks- they often neglect the power consumption and efficiency implications of these techniques. This study presents power efficiency experimental results from the application of well-known and proven optimization methods using a set of well-known ML models. The results are presented in a meaningful manner considering the “real world” functionality of devices and the provided results are compared with the basic “idle” power consumption of each of the selected systems. Two different systems with completely different architectures and capabilities were used providing us with results that led to interesting conclusions related to the power efficiency of each architecture.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23031595