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. (( 25 th INTERNATIONAL WORKSHOP on Thermal Investigations of ICs and Systems )) September 2019, Lecco / IT www.therminic2019.eu ISBN 978-1-7281-2078-2 (( 25 th INTERNATIONAL WORKSHOP Thermal Investigations of ICs and Systems )) 2019 (( 25 th INTERNATIONAL WORKSHOP on Thermal Investigations of ICs and Systems ))
Abstract-We consider wireless caches placed in the plane according to a homogeneous Poisson process. A data file is stored at the caches, which have limited storage capabilities. Clients can contact the caches to retrieve the data. The caches store the data according to one of the two data allocation strategies: partitioning & coding. We consider the Pareto front of the expected deployment cost of the caches and the expected cost of a client retrieving the data from the caches. We show that there is a strong trade-off between the expected retrieval and the expected deployment cost under the partitioning and the coding strategies. We also show that under coding, it is optimal to deploy a high number of caches, each with low storage capacity.
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