Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Systematic temperature sensor allocation and placement for microprocessors
Proceedings of the 43rd annual Design Automation Conference
Throughput of multi-core processors under thermal constraints
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
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)
Temperature-constrained power control for chip multiprocessors with online model estimation
Proceedings of the 36th annual international symposium on Computer architecture
Spectral techniques for high-resolution thermal characterization with limited sensor data
Proceedings of the 46th Annual Design Automation Conference
Optimizing Thermal Sensor Allocation for Microprocessors
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Thermal and power characterization of field-programmable gate arrays
Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays
Proceedings of the 49th Annual Design Automation Conference
Proceedings of the 49th Annual Design Automation Conference
Power Modeling and Characterization of Computing Devices: A Survey
Foundations and Trends in Electronic Design Automation
A survey and taxonomy of on-chip monitoring of multicore systems-on-chip
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A power-driven thermal sensor placement algorithm for dynamic thermal management
Proceedings of the Conference on Design, Automation and Test in Europe
Mitigating dark-silicon problems using superlattice-based thermoelectric coolers
Proceedings of the Conference on Design, Automation and Test in Europe
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The increased power densities of multi-core processors and the variations within and across workloads lead to runtime thermal hot spots locations of which change across time and space. Thermal hot spots increase leakage, deteriorate timing, and reduce the mean time to failure. To manage runtime thermal variations, circuit designers embed within-die thermal sensors that acquire temperatures at few selected locations. The acquired temperatures are then used to guide runtime thermal management techniques. The capabilities of these techniques are essentially bounded by the spatial thermal resolution of the sensor measurements. In this paper we characterize temperature signals of real processors and demonstrate that on-chip thermal gradients lead to sparse signals in the frequency domain. We exploit this observation to (1) devise thermal sensor allocation techniques, and (2) devise signal reconstruction techniques that fully characterize the thermal status of the processor using the limited number of measurements from the thermal sensors. To verify the accuracy of our methods, we compare our temperature characterization results against thermal measurements acquired from a state-of-the-art infrared camera that captures the mid-band infrared emissions from the back of the die of a 45 nm dual-core processor. Our results show that our techniques are capable of accurately characterizing the temperatures of real processors.