Name
Abstract | ||
|---|---|---|
Rapidly evolving embedded applications continuously demand more functionality and better performance under tight energy and thermal budgets, and maintaining high energy efficiency has become a significant design challenge for mobile devices. Although learning-based runtime power management can adapt to dynamic conditions, it is a challenging issue to quickly find an efficient management policy under ever-increasing hardware and software complexity. In this work, we propose a multi-device collaborative power management approach to address this issue. The collaborative power management periodically shares knowledge among multiple devices to accelerate the learning process and improve the quality of learned policies. We integrate the proposed method with dynamic voltage and frequency scaling (DVFS) on the multicore processors in mobile devices. Experimental results on realistic applications show that the collaborative power management can achieve on average 8x speedup and 10% energy saving compared with state-of-the-art learning-based approaches.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ASPDAC.2018.8297277 | ASP-DAC |
Keywords | Field | DocType |
ever-increasing hardware,software complexity,multidevice collaborative power management approach,multiple devices,learning process,learned policies,dynamic voltage,frequency scaling,mobile devices,multidevice collaborative management,knowledge sharing,embedded applications,thermal budgets,high energy efficiency,runtime power management,dynamic conditions,management policy,DVFS | Power management,Multi device,Knowledge sharing,Computer science,Real-time computing,Mobile device,Frequency scaling,Programming complexity,Multi-core processor,Speedup,Distributed computing | Conference |
ISSN | ISBN | Citations |
2153-6961 | 978-1-4503-6007-4 | 2 |
PageRank | References | Authors |
0.38 | 11 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongyuan Tian | 1 | 7 | 3.56 |
| Zhehui Wang | 2 | 262 | 24.56 |
| Haoran Li | 3 | 20 | 6.33 |
| Peng Yang | 4 | 64 | 10.97 |
| Rafael Kioji Vivas Maeda | 5 | 24 | 7.09 |
| Jiang Xu | 6 | 704 | 61.98 |