Thermal-Aware Resource Management for Embedded Real-Time Systems
- Authors
- Lee, Youngmoon; Chwa, Hoon Sung; Shin, Kang Geun; Wang, Shige
- Issue Date
- Nov-2018
- Publisher
- Institute of Electrical and Electronics Engineers
- Keywords
- Dynamic ambient temperature; embedded real-time systems; task-level power dissipation; thermal-aware resource management
- Citation
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, v.37, no.11, pp.2857 - 2868
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- Volume
- 37
- Number
- 11
- Start Page
- 2857
- End Page
- 2868
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/5194
- DOI
- 10.1109/TCAD.2018.2857279
- ISSN
- 0278-0070
- Abstract
- With an increasing demand for complex and powerful system-on-chips, modern real-time automotive systems face significant challenges in managing on-chip-temperature. We demonstrate, via real experiments, the importance of accounting for dynamic ambient temperature and task-level power dissipation in resource management so as to meet both thermal and timing constraints. To address this problem, we propose RT-TRM, a real-time thermal-aware resource management framework. We first introduce a task-level dynamic power model that can capture different power dissipations with a simple task-level parameter called the activity factor. We then develop two new mechanisms, adaptive parameter assignment and online idle-time scheduling. The former adjusts voltage/frequency levels and task periods according to the varying ambient temperature while preserving feasibility. The latter generates a schedule by allocating idle times efficiently without missing any task/job deadline. By tightly integrating the solutions of these two mechanisms, we can guarantee both thermal and timing constraints in the presence of dynamic ambient temperature variations. We have implemented RT-TRM on an automotive microcontroller to demonstrate its effectiveness, achieving better resource utilization by 18.2% over other runtime approaches while meeting both thermal and timing constraints.
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