Multilayer feedforward networks are universal approximators
Neural Networks
Full-chip sub-threshold leakage power prediction model for sub-0.18 μm CMOS
Proceedings of the 2002 international symposium on Low power electronics and design
Design Challenges of Technology Scaling
IEEE Micro
Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
Full chip leakage estimation considering power supply and temperature variations
Proceedings of the 2003 international symposium on Low power electronics and design
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
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Prediction of leakage power under process uncertainties
ACM Transactions on Design Automation of Electronic Systems (TODAES)
VLSID '07 Proceedings of the 20th International Conference on VLSI Design held jointly with 6th International Conference: Embedded Systems
Statistical analysis of full-chip leakage power considering junction tunneling leakage
Proceedings of the 44th annual Design Automation Conference
VLSID '08 Proceedings of the 21st International Conference on VLSI Design
Analytical yield prediction considering leakage/performance correlation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Statistical Thermal Profile Considering Process Variations: Analysis and Applications
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Genetic algorithm based neural network for license plate recognition
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1 % and 2 % respectively across a range of voltage and temperature values.