Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Systematic temperature sensor allocation and placement for microprocessors
Proceedings of the 43rd annual Design Automation Conference
Many-core design from a thermal perspective
Proceedings of the 45th annual Design Automation Conference
Thermal monitoring mechanisms for chip multiprocessors
ACM Transactions on Architecture and Code Optimization (TACO)
Static and dynamic temperature-aware scheduling for multiprocessor SoCs
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Accurate temperature estimation using noisy thermal sensors
Proceedings of the 46th Annual Design Automation Conference
Spectral techniques for high-resolution thermal characterization with limited sensor data
Proceedings of the 46th Annual Design Automation Conference
Utilizing predictors for efficient thermal management in multiprocessor SoCs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Thermal monitoring of real processors: techniques for sensor allocation and full characterization
Proceedings of the 47th Design Automation Conference
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
Improved Thermal Tracking for Processors Using Hard and Soft Sensor Allocation Techniques
IEEE Transactions on Computers
3D-ICE: fast compact transient thermal modeling for 3D ICs with inter-tier liquid cooling
Proceedings of the International Conference on Computer-Aided Design
Eagle-eye: a near-optimal statistical framework for noise sensor placement
Proceedings of the International Conference on Computer-Aided Design
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Chip designers place on-chip sensors to measure local temperatures, thus preventing thermal runaway situations in multicore processing architectures. However, thermal characterization is directly dependent on the number of placed sensors, which should be minimized, while guaranteeing full detection of all hot-spots and worst case temperature gradient. In this paper, we present EigenMaps: a new set of algorithms to recover precisely the overall thermal map from a minimal number of sensors and a near-optimal sensor allocation algorithm. The proposed methods are stable with respect to possible temperature sensor calibration inaccuracies, and achieve significant improvements compared to the state-of-the-art. In particular, we estimate an entire thermal map for an industrial 8-core industrial design within 1°C of accuracy with just four sensors. Moreover, when the measurements are corrupted by noise (SNR of 15 dB), we can achieve the same precision only with 16 sensors.