Imperial College London
Alternate

DrEdwardStott

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Teaching Fellow
 
 
 
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Contact

 

+44 (0)20 7594 6314ed.stott

 
 
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Location

 

612Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Yang:2015:10.1109/PATMOS.2015.7347594,
author = {Yang, S and Shafik, R and Merrett, G and Stott, E and Levine, J and Davis, JJ and Al-Hashimi, B},
doi = {10.1109/PATMOS.2015.7347594},
pages = {103--110},
publisher = {IEEE},
title = {Adaptive Energy Minimization of Embedded Heterogeneous Systems using Regression-based Learning},
url = {http://dx.doi.org/10.1109/PATMOS.2015.7347594},
year = {2015}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Modern embedded systems consist of heterogeneous computing resources with diverse energy and performance trade-offs. This is because these resources exercise the application tasks differently, generating varying workloads and energy consumption. As a result, minimizing energy consumption in these systems is challenging as continuous adaptation between application task mapping (i.e. allocating tasks among the computing resources) and dynamic voltage/frequency scaling (DVFS) is required. Existing approaches have limitations due to lack of such adaptation with practical validation (Table I). This paper addresses such limitation and proposes a novel adaptive energy minimization approach for embedded heterogeneous systems. Fundamental to this approach is a runtime model, generated through regression-based learning of energy/performance trade-offs between different computing resources in the system. Using this model, an application task is suitably mapped on a computing resource during runtime, ensuring minimum energy consumption for a given application performance requirement. Such mapping is also coupled with a DVFS control to adapt to performance and workload variations. The proposed approach is designed, engineered and validated on a Zynq-ZC702 platform, consisting of CPU, DSP and FPGA cores. Using several image processing applications as case studies, it was demonstrated that our proposed approach can achieve significant energy savings (>70%), when compared to the existing approaches.
AU  - Yang,S
AU  - Shafik,R
AU  - Merrett,G
AU  - Stott,E
AU  - Levine,J
AU  - Davis,JJ
AU  - Al-Hashimi,B
DO  - 10.1109/PATMOS.2015.7347594
EP  - 110
PB  - IEEE
PY  - 2015///
SP  - 103
TI  - Adaptive Energy Minimization of Embedded Heterogeneous Systems using Regression-based Learning
UR  - http://dx.doi.org/10.1109/PATMOS.2015.7347594
UR  - http://ieeexplore.ieee.org/document/7347594/
UR  - http://hdl.handle.net/10044/1/33177
ER  -
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