Hierarchical Krylov subspace reduced order modeling of large RLC circuits
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Hierarchical Krylov subspace based reduction of large interconnects
Integration, the VLSI Journal
Architecture-level thermal characterization for multicore microprocessors
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
On-chip sensor-driven efficient thermal profile estimation algorithms
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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
Full-chip runtime error-tolerant thermal estimation and prediction for practical thermal management
Proceedings of the International Conference on Computer-Aided Design
Fast and accurate thermal modeling and simulation of manycore processors and workloads
Microelectronics Journal
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As the power density increases exponentially, the runtime regulation of operating temperature by dynamic thermal management (DTM) becomes necessary. This paper proposes two novel approaches to the thermal analysis at the chip architecture level for efficient DTM. The first method, i.e., thermal moment matching with spectrum analysis, is based on observations that the power consumption of architecture-level modules in microprocessors running typical workloads presents a strong nature of periodicity. Such a feature can be exploited by fast spectrum analysis in the frequency domain for computing steady-state response. The second method, i.e., thermal moment matching based on piecewise constant power inputs, is based on the observation that the average power consumption of architecture-level modules in microprocessors running typical workloads determines the trend of temperature variations. As a result, using piecewise constant average power inputs can further speed up the thermal analysis. To obtain transient temperature changes due to the initial condition and constant/average power inputs, numerically stable moment matching methods with enhanced pole searching are carried out to speed up online temperature tracking with high accuracy and low overhead. The resulting thermal analysis algorithm has a linear time complexity in runtime setting when the average power inputs are applied. Experimental results show that the resulting thermal analysis algorithms lead to 10times-100times speedup over the traditional integration-based transient analysis with small accuracy loss