Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Dynamic Thermal Management for High-Performance Microprocessors
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
Efficient full-chip thermal modeling and analysis
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Analysis and modeling of CD variation for statistical static timing
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Non-linear statistical static timing analysis for non-Gaussian variation sources
Proceedings of the 44th annual Design Automation Conference
Extraction of statistical timing profiles using test data
Proceedings of the 44th annual Design Automation Conference
Thermal-aware task scheduling at the system software level
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
Proactive temperature management in MPSoCs
Proceedings of the 13th international symposium on Low power electronics and design
Microelectronic Circuits Revised Edition
Microelectronic Circuits Revised Edition
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
3-D Thermal-ADI: a linear-time chip level transient thermal simulator
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
High-Efficiency Green Function-Based Thermal Simulation Algorithms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A statistical framework for designing on-chip thermal sensing infrastructure in nano-scale systems
Proceedings of the 19th international symposium on Physical design
Adaptive and autonomous thermal tracking for high performance computing systems
Proceedings of the 47th Design Automation Conference
Accurate direct and indirect on-chip temperature sensing for efficient dynamic thermal management
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems - Special section on the ACM IEEE international conference on formal methods and models for codesign (MEMOCODE) 2009
A system level approach to multi-core thermal sensors calibration
PATMOS'11 Proceedings of the 21st international conference on Integrated circuit and system design: power and timing modeling, optimization, and simulation
Recent thermal management techniques for microprocessors
ACM Computing Surveys (CSUR)
Proceedings of the 49th Annual Design Automation Conference
Proceedings of the 49th Annual Design Automation Conference
Fan-speed-aware scheduling of data intensive jobs
Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design
Temperature tracking: an innovative run-time approach for hardware Trojan detection
Proceedings of the International Conference on Computer-Aided Design
Nano-CMOS thermal sensor design optimization for efficient temperature measurement
Integration, the VLSI Journal
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Multicore SOCs rely on runtime thermal measurements using on-chip sensors for DTM. In this paper we address the problem of estimating the actual temperature of on-chip thermal sensor when the sensor reading has been corrupted by noise. Thermal sensors are prone to noise due to fabrication randomness, VDD fluctuations etc. This causes discrepancy between actual temperature and the one predicted by thermal sensor. Our experiments estimate this variation to be around 30%. In this paper we present a statistical methodology for predicting the actual temperature for a given sensor reading. We present two techniques: single sensor prediction and multi-sensor prediction. The latter tries to estimate the actual temperature for each sensor (of the many on-chip sensors) simultaneously while exploiting the correlations between temperature and noise of different sensors. When the underlying randomness follows a Gaussian characteristic, we present optimal schemes of estimating the expected temperature. We also present heuristic schemes for the case where the Gaussian assumption fails to hold. The experiments showed that using our estimation schemes the RMS error can be reduce as much as 67% as compared to blindly trusting the sensors to be noise free.