Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
On the complexity of partially observed Markov decision processes
Theoretical Computer Science - Special issue on complexity theory and the theory of algorithms as developed in the CIS
Electrothermal analysis of VLSI systems
Electrothermal analysis of VLSI systems
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Dynamic Power Management: Design Techniques and CAD Tools
Dynamic Power Management: Design Techniques and CAD Tools
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive Filtering: Algorithms and Practical Implementation
Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
Dynamic Programming
Full chip leakage estimation considering power supply and temperature variations
Proceedings of the 2003 international symposium on Low power electronics and design
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Proceedings of the 41st annual Design Automation Conference
Leakage power reduction by dual-vth designs under probabilistic analysis of vth variation
Proceedings of the 2004 international symposium on Low power electronics and design
Design challenges at 65nm and beyond
Proceedings of the conference on Design, automation and test in Europe
Variation resilient low-power circuit design methodology using on-chip phase locked loop
Proceedings of the 44th annual Design Automation Conference
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Fault-Tolerant Flow Control in On-chip Networks
NOCS '10 Proceedings of the 2010 Fourth ACM/IEEE International Symposium on Networks-on-Chip
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With the increasing levels of variability and randomness in the characteristics and behavior of manufactured nanoscale structures and devices, achieving performance optimization under process, voltage, and temperature (PVT) variations as well as current, voltage, and thermal (CVT) stress has become a daunting, yet vital, task. In this paper, we present a stochastic dynamic power management (DPM) framework to improve the accuracy of decision making under probabilistic conditions induced by PVT variations and/or stress. More precisely, we propose a resilient power management technique that guarantees to select an optimal policy under sources of uncertainty. A key characteristic of the proposed technique is that the effects of uncertainties due to variability and stress are captured by stochastic processes which control a self-improving power manager. Simulation results with a 65nm processor design show that, compared to the worst-case PVT conditions, the proposed DPM technique ensures energy efficiency, while reducing the uncertain behaviors of the system.