Full metadata
Title
Predictive dynamic thermal and power management for heterogeneous mobile platforms
Description
Heterogeneous multiprocessor systems-on-chip (MPSoCs) powering mobile platforms integrate multiple asymmetric CPU cores, a GPU, and many specialized processors. When the MPSoC operates close to its peak performance, power dissipation easily increases the temperature, hence adversely impacts reliability. Since using a fan is not a viable solution for hand-held devices, there is a strong need for dynamic thermal and power management (DTPM) algorithms that can regulate temperature with minimal performance impact. This abstract presents a DTPM algorithm based on a practical temperature prediction methodology using system identification. The DTPM algorithm dynamically computes a power budget using the predicted temperature, and controls the types and number of active processors as well as their frequencies. Experiments on an octa-core big.LITTLE processor and common Android apps demonstrate that the proposed technique predicts temperature within 3% accuracy, while the DTPM algorithm provides around 6x reduction in temperature variance, and as large as 16% reduction in total platform power compared to using a fan.
Date Created
2015
Contributors
- Singla, Gaurav (Author)
- Ogras, Umit Y. (Thesis advisor)
- Bakkaloglu, Bertan (Committee member)
- Unver, Ali (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
viii, 44 p. : ill. (some col.)
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.29665
Statement of Responsibility
by Gaurav Singla
Description Source
Viewed on June 11, 2015
Level of coding
full
Note
Partial requirement for: M.S., Arizona State University, 2015
Note type
thesis
Includes bibliographical references (p. 41-44)
Note type
bibliography
Field of study: Electrical engineering
System Created
- 2015-06-01 08:04:38
System Modified
- 2021-08-30 01:30:12
- 2 years 8 months ago
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