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In this paper, we proposed an efficient statistical chip-level total power estimation method considering process variations with spatial correlation. Instead of computing dynamic power and leakage power separately, the new method compute the total power via circuit level simulation under realistic input testing vectors. To consider the process variations with spatial correlation, we first apply principle...
Estimating the dynamic powers is crucial for power and energy efficient chip designs. With increasing variability from manufacture processes, dynamic powers can manifest significant variations due to uncertainties in device geometry and delay variations. In this paper, we propose a new statistical dynamic power estimation method considering the spatial correlation in process variation. We first show...
We present a generic method for analyzing the effect of process variability in nanoscale circuits. The proposed framework uses kernel and a generic tail probability estimator to eliminate the need for a-priori density choice for the nature of circuit variation. This allows capturing the true nature of the circuit variation from a few random samples of its observed responses. The data-driven, non-parametric,...
The dramatic increase in leakage current, coupled with the swell in process variability in nano-scaled CMOS technologies, has become a major issue for future IC design. Moreover, due to the spread of leakage power values, leakage variability cannot be neglected anymore. In this work an accurate analytic estimation and modeling methodology has been developed for logic gates leakage under statistical...
In this paper, we propose a probability-based algorithm to estimate full-chip leakage without knowing layout information, under intra-die and inter-die process variations. Through modeling process variations into a random vector, we show that the standard cell leakage can be modeled as an inverse Gaussian random variable and further demonstrate that full-chip leakage can also be approximated to be...
This paper is concerned with the estimation problem for discrete-time stochastic linear systems with multiple packet dropouts. Based on a recently developed model for multiple-packet dropouts, the original system is transferred to a stochastic parameter system by augmentation of the state and measurement. The optimal full-order linear filter of the form of employing the received outputs at the current...
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