Stochastic finite elements: a spectral approach
Stochastic finite elements: a spectral approach
Estimation of average switching activity in combinational and sequential circuits
DAC '92 Proceedings of the 29th ACM/IEEE Design Automation Conference
Efficient estimation of dynamic power consumption under a real delay model
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Electrothermal analysis of VLSI systems
Electrothermal analysis of VLSI systems
Improving the efficiency of Monte Carlo power estimation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on the 11th international symposium on system-level synthesis and design (ISSS'98)
Statistical analysis of subthreshold leakage current for VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Full-chip analysis of leakage power under process variations, including spatial correlations
Proceedings of the 42nd annual Design Automation Conference
Enhanced Dual-Transition Probabilistic Power Estimation with Selective Supergate Analysis
ICCD '05 Proceedings of the 2005 International Conference on Computer Design
Efficient statistical capacitance variability modeling with orthogonal principle factor analysis
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Projection-based statistical analysis of full-chip leakage power with non-log-normal distributions
Proceedings of the 43rd annual Design Automation Conference
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
ISVLSI '09 Proceedings of the 2009 IEEE Computer Society Annual Symposium on VLSI
Variational capacitance extraction of on-chip interconnects based on continuous surface model
Proceedings of the 46th Annual Design Automation Conference
Proceedings of the 2009 International Conference on Computer-Aided Design
Proceedings of the 47th Design Automation Conference
Dynamic power estimation for deep submicron circuits with process variation
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Maximum power estimation for CMOS circuits using deterministic and statistical approaches
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A Monte Carlo approach for power estimation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Stratified random sampling for power estimation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Gate-level power estimation using tagged probabilistic simulation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Probabilistic simulation for reliability analysis of CMOS VLSI circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Robust Extraction of Spatial Correlation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Transition density: a new measure of activity in digital circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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In this paper, we propose an efficient statistical full-chip total power estimation method considering process variations with spatial correlation. Traditionally, dynamic power and leakage power were computed separately as leakage power is more susceptible to process variations. But in the end, it is total power that designers will be concerned with. We propose a new method to compute the statistical total power via circuit level simulation under realistic input testing vectors. To consider the process variations with spatial correlation, we first apply principle factor analysis method (PFA) or its weighted version (wPFA) to transform the correlated variables into uncorrelated ones and meanwhile reduce the number of resulting random variables. Afterwards, Hermite orthogonal polynomials and sparse grid techniques are used to estimate total power distribution in a sampling way. The proposed method has no restrictions on models of statistical distributions for total powers. The method works well when strong spatial correlation exists among random variables in the chip. Experimental results show that the proposed method has 100X times speedup than the Monte Carlo method under fixed input vector and 20X times speedup than the Monte Carlo method considering both random input vectors and process variations with spatial correlation.