Most modern mobile embedded devices have the ability to increase their computational power typically at the cost of increased heat dissipation. This may result in temperatures above the design limit, especially if active cooling is inapplicable. Thus, it is necessary to consider processor temperature while scheduling tasks. This means estimating the change in temperature due to changed workload is crucial for high performance mobile embedded devices. To address this challenge, we first introduce a model to estimate the temperature and classify the system dependent model parameters. Then, to determine these parameters, we develop a new method, which can be applied on any mobile embedded device. The only requirement for our new method is learning the device characteristics by processing a certain task while recording the temperature with built-in sensors. Our results show that our method can achieve high accuracy within a short testing period.
|Title of host publication||2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC)|
|Editors||Marta Rencz, Lorenzo Codecasa, Andras Poppe|
|Number of pages||6|
|Publication status||Published - 5 Dec 2019|
|Event||25th International Workshop on Thermal Investigations of ICs and Systems, THERMINIC 2019 - University of Milano, Lecco, Italy|
Duration: 24 Sep 2020 → 24 Sep 2020
Conference number: 25
|Workshop||25th International Workshop on Thermal Investigations of ICs and Systems, THERMINIC 2019|
|Period||24/09/20 → 24/09/20|
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2019 Harvey Rosten Award For Excellence
Ozceylan, Baver (Recipient), Haverkort, Boudewijn Remigius Heinrich Maria (Recipient), de Graaf, Maurits (Recipient) & Gerards, Marco E. T. (Recipient), 19 Mar 2020