Name
Abstract | ||
|---|---|---|
Minimizing energy consumption while satisfying the user-specified performance requirement is a primary design objective for mobile devices. To achieve this, off-the-shelf mobile devices are usually equipped with dynamic voltage and frequency scaling (DVFS)-enabled processors to tradeoff performance for energy reduction through online adjustment of operating points. Recently, reinforcement learning algorithms have been widely studied and show great potential for runtime DVFS control because of their adaptability to the changing environment. However, the rapid evolution of hardware and the ever-growing diversity of mobile applications increase the system complexity dramatically and make it hard for the learning agent to quickly obtain an efficient power management policy. To address this challenge, we propose a decentralized collaborative Q-learning (DCQL)-based approach in this article to solve the DVFS control problem of multicore mobile processors. By exchanging learning experiences and knowledge among multiple devices, DCQL increases the convergence rate of the learning algorithm and improves the quality of the derived power management policy. On each device, the 4-phase action selection strategy is applied for efficient exploration of the action space defined by various power settings. Experimental results on realistic applications show that DCQL can achieve 3.4%–8.0% energy reduction over various existing approaches while providing an average of
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speedup over the state-of-the-art individual learning algorithm. We also show that the proposed approach can scale well with the number of cores through clustered DVFS control. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TVLSI.2020.2970762 | IEEE Transactions on Very Large Scale Integration (VLSI) Systems |
Keywords | DocType | Volume |
Multicore processing,Program processors,Power system management,Energy consumption,Performance evaluation,Mobile handsets,Convergence | Journal | 28 |
Issue | ISSN | Citations |
5 | 1063-8210 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongyuan Tian | 1 | 7 | 3.56 |
| Jiang Xu | 2 | 704 | 61.98 |
| Haoran Li | 3 | 20 | 6.33 |
| Rafael Kioji Vivas Maeda | 4 | 24 | 7.09 |