Dynamic power management of complex systems using generalized stochastic Petri nets
Proceedings of the 37th Annual Design Automation Conference
A survey of design techniques for system-level dynamic power management
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on low-power electronics and design
Dynamic Power Management for Nonstationary Service Requests
IEEE Transactions on Computers
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
Managing power consumption in networks on chips
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Hierarchical Adaptive Dynamic Power Management
IEEE Transactions on Computers
Hierarchical power management with application to scheduling
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Policy optimization for dynamic power management
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Stochastic modeling of a power-managed system-construction and optimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A framework of stochastic power management using hidden Markov model
Proceedings of the conference on Design, automation and test in Europe
Adaptive power management using reinforcement learning
Proceedings of the 2009 International Conference on Computer-Aided Design
Enhanced Q-learning algorithm for dynamic power management with performance constraint
Proceedings of the Conference on Design, Automation and Test in Europe
Proceedings of the 48th Design Automation Conference
Achieving autonomous power management using reinforcement learning
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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As the hardware and software complexity grows, it is unlikely for the power management hardware/software to have a full observation of the entire system status. In this paper, we propose a new modeling and optimization technique based on partially observable Markov decision process (POMDP) for robust power management, which can achieve near-optimal power savings, even when only partial system information is available. Three scenarios of partial observations that may occur in an embedded system are discussed and their modeling techniques are presented. The experimental results show that, compared with power management policy derived from traditional Markov decision process model that assumes the system is fully observable, the new power management technique gives significantly better performance and energy tradeoff.