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Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

Janakiraman, V and Bharadwaj, Amrutur and Visvanathan, V (2010) Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks. In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 29 (7). pp. 1056-1069.

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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.

Item Type: Journal Article
Additional Information: Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Activation; leakage; log-normal;neural network;sigmoid; statistical.
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Depositing User: Id for Latest eprints
Date Deposited: 18 Oct 2010 10:10
Last Modified: 18 Oct 2010 10:10
URI: http://eprints.iisc.ac.in/id/eprint/33274

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