Electrothermal analysis of VLSI systems
Electrothermal analysis of VLSI systems
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
3D thermal-ADI: an efficient chip-level transient thermal simulator
Proceedings of the 2003 international symposium on Physical design
Predictive dynamic thermal management for multimedia applications
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
Temperature-aware microarchitecture
Proceedings of the 30th annual international symposium on Computer architecture
Dynamic Thermal Management for High-Performance Microprocessors
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
Compact thermal modeling for temperature-aware design
Proceedings of the 41st annual Design Automation Conference
On-chip sensor-driven efficient thermal profile estimation algorithms
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Adaptive and autonomous thermal tracking for high performance computing systems
Proceedings of the 47th Design Automation Conference
Recent thermal management techniques for microprocessors
ACM Computing Surveys (CSUR)
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With the increasing levels of variability in the behavior of manufactured nano-scale devices and dramatic changes in the power density on a chip, timely identification of hot spots on a chip has become a challenging task. This paper addresses the questions of how and when to identify and issue a hot spot alert. There are important questions since temperature reports by thermal sensors may be erroneous, noisy, or arrive too late to enable effective application of thermal management mechanisms to avoid chip failure. This paper thus presents a stochastic technique for identifying and reporting local hot spots under probabilistic conditions induced by uncertainty in the chip junction temperature and the system power state. More specifically, it introduces a stochastic framework for estimating the chip temperature and the power state of the system based on a combination of Kalman Filtering (KF) and Markovian Decision Process (MDP) model. Experimental results demonstrate the effectiveness of the framework and show that the proposed technique alerts about thermal threats accurately and in a timely fashion in spite of noisy or sometimes erroneous readings by the temperature sensor.