A statistical framework for designing on-chip thermal sensing infrastructure in nano-scale systems
Proceedings of the 19th international symposium on Physical design
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
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
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Recent thermal management techniques for microprocessors
ACM Computing Surveys (CSUR)
Temperature tracking: an innovative run-time approach for hardware Trojan detection
Proceedings of the International Conference on Computer-Aided Design
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In this work we present a method for accurate estimation of temperature at various locations on a chip considering the inaccuracies in thermal sensor readings due to limitations mainly due to thermal sensor placement and sensor noise. This technique enables accurate estimation of temperature at different locations on the chip with only a limited number of sensors in an efficient way. We utilize Kalman filter (KF) for temperature estimation and for elimination of sensing inaccuracies as well. The computational complexity is reduced by using steady state Kalman filter during normal operation of the chip and reducing the order of the thermal model by a projection based model order reduction method. Our experimental results show that this technique typically reduces the standard deviation and maximum value of temperature estimation errors by about an order of magnitude.