Document Type
Conference Proceeding
Publication Date
2005
Publication Title
Proceedings of the 2005 IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications
Publisher
Institute of Electrical and Electronics Engineers
First page number:
208
Last page number:
213
Abstract
Dynamic power management (DPM) refers to the strategies employed at system level to reduce energy expenditure (i.e. to prolong battery life) in embedded systems. The trade-off involved in DPM techniques is between the reductions of energy consumption and latency suffered by the tasks. Such trade-offs need to be decided at runtime, making DPM an on-line problem. We formulate DPM as a hybrid automaton control problem and integrate stochastic control. The control strategy is learnt dynamically using stochastic learning hybrid automata (SLHA) with feedback learning algorithms. Simulation-based experiments show the expediency of the feedback systems in stationary environments. Further experiments reveal that SLHA attains better trade-offs than several former predictive algorithms under certain trace data.
Keywords
Batteries; Embedded computer systems – Energy consumption; Energy conservation; Learning models (Stochastic processes)
Repository Citation
Erbes, T.,
Shukla, S. K.,
Kachroo, P.
(2005).
Stochastic Learning Feedback Hybrid Automata for Dynamic Power Management in Embedded Systems.
Proceedings of the 2005 IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications
208-213.
Institute of Electrical and Electronics Engineers.
https://digitalscholarship.unlv.edu/ece_fac_articles/136
Comments
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