Battery-aware power management based on Markovian decision processes
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Proceedings of the 40th annual Design Automation Conference
Disk drive energy optimization for audio-video applications
Proceedings of the 2004 international conference on Compilers, architecture, and synthesis for embedded systems
Energy optimal speed control of devices with discrete speed sets
Proceedings of the 42nd annual Design Automation Conference
Hierarchical power management with application to scheduling
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
CODES+ISSS '05 Proceedings of the 3rd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Energy optimization for a two-device data flow chain
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Applying stochastic modeling to bus arbitration for systems-on-chip
Integration, the VLSI Journal
Stochastic modeling and optimization for robust power management in a partially observable system
Proceedings of the conference on Design, automation and test in Europe
Dynamic power management under uncertain information
Proceedings of the conference on Design, automation and test in Europe
Energy optimal speed control of a producer--consumer device pair
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Autonomic power and performance management for computing systems
Cluster Computing
Stochastic DVS-based dynamic power management for soft real-time systems
Microprocessors & Microsystems
Adaptive Power Management Based on Reinforcement Learning for Embedded System
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Model checking expected time and expected reward formulae with random time bounds
Computers & Mathematics with Applications
SFM'07 Proceedings of the 7th international conference on Formal methods for performance evaluation
Uncertainty-aware dynamic power management in partially observable domains
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Evaluating the effectiveness of model-based power characterization
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
SOC dynamic power management using artificial neural network
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
An adaptive hybrid dynamic power management algorithm for mobile devices
Computer Networks: The International Journal of Computer and Telecommunications Networking
Robust and adaptive dynamic power management for time varying system
ICESS'04 Proceedings of the First international conference on Embedded Software and Systems
Verification of linear duration properties over continuous-time markov chains
Proceedings of the 15th ACM international conference on Hybrid Systems: Computation and Control
Verification of linear duration properties over continuous-time markov chains
ACM Transactions on Computational Logic (TOCL)
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The goal of a dynamic power management policy is to reduce the power consumption of an electronic system by putting system components into different states, each representing a certain performance and power consumption level. The policy determines the type and timing of these transitions based on the system history, workload, and performance constraints. In this paper we propose a new abstract model of a power-managed electronic system. We formulate the problem of system-level power management as a controlled optimization problem based on the theories of continuous-time Markov derision processes and stochastic networks. This problem is solved exactly using linear programming or heuristically using “policy iteration.” Our method is compared with existing heuristic methods for different workload statistics. Experimental results show that the power management method based on a Markov decision process outperforms heuristic methods by as much as 44% in terms of power dissipation savings for a given level of system performance