Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

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Sammanfattning

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.
OriginalspråkEngelska
Titel på värdpublikationEmbedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021.
FörlagSpringer
Sidor144-160
Antal sidor17
ISBN (tryckt)978-3-031-04579-0
DOI
StatusPublicerad - 2022
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemang21st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021 - Virtual, Online
Varaktighet: 4 juli 20218 juli 2021

Publikationsserier

Namn Lecture Notes in Computer Science
FörlagSpringer
Volym13227
ISSN (tryckt)0302-9743

Konferens

Konferens21st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021
OrtVirtual, Online
Period04/07/2108/07/21

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