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Authors: Supadchaya Puangpontip and Rattikorn Hewett

Affiliation: Department of Computer Science, Texas Tech University, Lubbock, U.S.A.

Keyword(s): Green Computing, Energy-Aware, Energy Modelling, Smart Cyber-Physical Systems, Edge Deep Learning.

Abstract: Today's green computing has to deal with prevalent Cyber-Physical Systems (CPSs), engineered systems that tightly integrate computation and physical components. Green CPS aims to use electronic/computer devices and resources to perform operations as efficiently and eco-friendly as possible. With the rise of smart technology combining with Artificial Intelligence Deep Learning (DL) in Internet of Things and CPSs, continuing use of these compute intensive CPS software like DL can negatively impact energy resources and environments. Much research has advanced green hardware and physical component development. Our research aims to develop green CPSs by making them energy aware. To do this, we propose an analytical modelling approach to quantifying energy consumption of software artifacts in the CPS. The paper describes the approach through energy consumption modelling of DL in distributed CPS due to the popular deployment of DL in many modern CPSs. However, the approach is general and ca n be applied to any CPS. The paper illustrates the application of our approach for energy management in scaling and designing smart farming CPS that monitors crop health. (More)

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Paper citation in several formats:
Puangpontip, S. and Hewett, R. (2022). Energy-Aware Deep Learning for Green Cyber-Physical Systems. In Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS; ISBN 978-989-758-572-2; ISSN 2184-4968, SciTePress, pages 32-43. DOI: 10.5220/0011035500003203

@conference{smartgreens22,
author={Supadchaya Puangpontip. and Rattikorn Hewett.},
title={Energy-Aware Deep Learning for Green Cyber-Physical Systems},
booktitle={Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS},
year={2022},
pages={32-43},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011035500003203},
isbn={978-989-758-572-2},
issn={2184-4968},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS
TI - Energy-Aware Deep Learning for Green Cyber-Physical Systems
SN - 978-989-758-572-2
IS - 2184-4968
AU - Puangpontip, S.
AU - Hewett, R.
PY - 2022
SP - 32
EP - 43
DO - 10.5220/0011035500003203
PB - SciTePress

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