Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

Joel Öhrling; Sébastien Lafond; Dragos Truscan

doi:https://doi.org/10.48550/arXiv.2112.10121

Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

Joel Öhrling, Sébastien Lafond, Dragos Truscan

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Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

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Abstract

In this paper we evaluate the use of system identification methods to build a thermal prediction model of heterogeneous SoC platforms that can be used to quickly predict the temperature of difierent configurations without the need of hardware. Specifically, we focus on modeling approaches that can predict the temperature based on the clock frequency and the utilization percentage of each core.
We investigate three methods with respect to their prediction accuracy: a linear state-space identification approach using polynomial regressors, a NARX neural network approach and a recurrent neural network approach configured in an FIR model structure. We evaluate the methods on an Odroid-XU4 board featuring an Exynos 5422 SoC. The results show that the model based on polynomial regressors significantly outperformed the other two models when trained with 1 hour and 6 hours of data.

Original language	English
Title of host publication	Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021.
Publisher	Springer
Pages	144-160
Number of pages	17
ISBN (Print)	978-3-031-04579-0
DOIs	https://doi.org/10.48550/arXiv.2112.10121 https://doi.org/10.1007/978-3-031-04580-6_10
Publication status	Published - 2022
MoE publication type	A4 Article in a conference publication
Event	21st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021 - Virtual, Online Duration: 4 Jul 2021 → 8 Jul 2021

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	13227
ISSN (Print)	0302-9743

Conference

Conference	21st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021
City	Virtual, Online
Period	04/07/21 → 08/07/21

Keywords

energy dissipation
Temperature
Multicore processing
System identification

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https://urn.fi/URN:NBN:fi-fe2023042739008

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Öhrling, J., Lafond, S., & Truscan, D. (2022). Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs. In Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. (pp. 144-160). ( Lecture Notes in Computer Science; Vol. 13227). Springer. https://doi.org/10.48550/arXiv.2112.10121, https://doi.org/10.1007/978-3-031-04580-6_10

@inproceedings{2cacc30d0fbe4c94bc6ff164a283e777,

title = "Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs",

abstract = "In this paper we evaluate the use of system identification methods to build a thermal prediction model of heterogeneous SoC platforms that can be used to quickly predict the temperature of difierent configurations without the need of hardware. Specifically, we focus on modeling approaches that can predict the temperature based on the clock frequency and the utilization percentage of each core.We investigate three methods with respect to their prediction accuracy: a linear state-space identification approach using polynomial regressors, a NARX neural network approach and a recurrent neural network approach configured in an FIR model structure. We evaluate the methods on an Odroid-XU4 board featuring an Exynos 5422 SoC. The results show that the model based on polynomial regressors significantly outperformed the other two models when trained with 1 hour and 6 hours of data.",

keywords = "energy dissipation, Temperature, Multicore processing, System identification",

author = "Joel {\"O}hrling and S{\'e}bastien Lafond and Dragos Truscan",

note = "Funding Information: Part of this work was carried out with financial support from the Nordic Master programme (contract NMP-2016/10169) and ECSEL-JU AIDOaRt project (grant agreement No. 101007350). Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 21st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021 ; Conference date: 04-07-2021 Through 08-07-2021",

year = "2022",

doi = "https://doi.org/10.48550/arXiv.2112.10121",

language = "English",

isbn = "978-3-031-04579-0",

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publisher = "Springer",

pages = "144--160",

booktitle = "Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021.",

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Öhrling, J, Lafond, S & Truscan, D 2022, Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs. in Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. . Lecture Notes in Computer Science, vol. 13227, Springer, pp. 144-160, 21st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021, Virtual, Online, 04/07/21. https://doi.org/10.48550/arXiv.2112.10121, https://doi.org/10.1007/978-3-031-04580-6_10

Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs. / Öhrling, Joel; Lafond, Sébastien ; Truscan, Dragos.
Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. . Springer, 2022. p. 144-160 ( Lecture Notes in Computer Science; Vol. 13227).

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

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T1 - Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

AU - Öhrling, Joel

AU - Lafond, Sébastien

AU - Truscan, Dragos

N1 - Funding Information: Part of this work was carried out with financial support from the Nordic Master programme (contract NMP-2016/10169) and ECSEL-JU AIDOaRt project (grant agreement No. 101007350). Publisher Copyright: © 2022, Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - In this paper we evaluate the use of system identification methods to build a thermal prediction model of heterogeneous SoC platforms that can be used to quickly predict the temperature of difierent configurations without the need of hardware. Specifically, we focus on modeling approaches that can predict the temperature based on the clock frequency and the utilization percentage of each core.We investigate three methods with respect to their prediction accuracy: a linear state-space identification approach using polynomial regressors, a NARX neural network approach and a recurrent neural network approach configured in an FIR model structure. We evaluate the methods on an Odroid-XU4 board featuring an Exynos 5422 SoC. The results show that the model based on polynomial regressors significantly outperformed the other two models when trained with 1 hour and 6 hours of data.

AB - In this paper we evaluate the use of system identification methods to build a thermal prediction model of heterogeneous SoC platforms that can be used to quickly predict the temperature of difierent configurations without the need of hardware. Specifically, we focus on modeling approaches that can predict the temperature based on the clock frequency and the utilization percentage of each core.We investigate three methods with respect to their prediction accuracy: a linear state-space identification approach using polynomial regressors, a NARX neural network approach and a recurrent neural network approach configured in an FIR model structure. We evaluate the methods on an Odroid-XU4 board featuring an Exynos 5422 SoC. The results show that the model based on polynomial regressors significantly outperformed the other two models when trained with 1 hour and 6 hours of data.

KW - energy dissipation

KW - Temperature

KW - Multicore processing

KW - System identification

UR - https://arxiv.org/abs/2112.10121

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DO - https://doi.org/10.48550/arXiv.2112.10121

M3 - Conference contribution

AN - SCOPUS:85129800655

SN - 978-3-031-04579-0

T3 - Lecture Notes in Computer Science

SP - 144

EP - 160

BT - Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021.

PB - Springer

T2 - 21st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021

Y2 - 4 July 2021 through 8 July 2021

ER -

Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

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Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

Abstract

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Keywords

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