Full metadata
Title
Constrained energy optimization in heterogeneous platforms using generalized scaling models
Description
Mobile platforms are becoming highly heterogeneous by combining a powerful multiprocessor system-on-chip (MpSoC) with numerous resources including display, memory, power management IC (PMIC), battery and wireless modems into a compact package. Furthermore, the MpSoC itself is a heterogeneous resource that integrates many processing elements such as CPU cores, GPU, video, image, and audio processors. As a result, optimization approaches targeting mobile computing needs to consider the platform at various levels of granularity.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
Date Created
2014
Contributors
- Gupta, Ujjwala (Author)
- Ogras, Umit Y. (Thesis advisor)
- Ozev, Sule (Committee member)
- Chakrabarti, Chaitali (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
ix, 81 p. : col. ill
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.25831
Statement of Responsibility
by Ujjwal Gupta
Description Source
Viewed on Nov. 13, 2014
Level of coding
full
Note
Partial requirement for: M.S., Arizona State University, 2014
Note type
thesis
Includes bibliographical references (p. 67-72)
Note type
bibliography
Field of study: Electrical engineering
System Created
- 2014-10-01 04:59:33
System Modified
- 2021-08-30 01:33:25
- 2 years 8 months ago
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