A methodology for evaluation time approximation

P. W.C. Prasad; Azam Beg

doi:10.1109/MWSCAS.2007.4488692

A methodology for evaluation time approximation

P. W.C. Prasad
, Azam Beg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

This paper describes a feed-forward neural network model (FFNNM) for complexity prediction of path related objective functions, mainly average path length (APL) of an arbitrary Boolean function (BF). The proposed model is determined by neural training process of evaluation time derived from the Monte Carlo data of randomly generated BFs. Experimental results show a good correlation between the ISCAS benchmark circuits and those predicted by the FFNNM. This model is capable of providing an estimation of the performance of a circuit prior to its final implementation.

Original language	English
Title of host publication	2007 50th Midwest Symposium on Circuits and Systems, MWSCAS - Conference Proceedings
Pages	776-778
Number of pages	3
DOIs	https://doi.org/10.1109/MWSCAS.2007.4488692
Publication status	Published - 2007
Event	2007 50th Midwest Symposium on Circuits and Systems, MWSCAS - Conference - Montreal, QC, Canada Duration: Aug 5 2007 → Aug 8 2007

Publication series

Name	Midwest Symposium on Circuits and Systems
ISSN (Print)	1548-3746

Other

Other	2007 50th Midwest Symposium on Circuits and Systems, MWSCAS - Conference
Country/Territory	Canada
City	Montreal, QC
Period	8/5/07 → 8/8/07

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

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10.1109/MWSCAS.2007.4488692

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Prasad, PWC & Beg, A 2007, A methodology for evaluation time approximation. in 2007 50th Midwest Symposium on Circuits and Systems, MWSCAS - Conference Proceedings., 4488692, Midwest Symposium on Circuits and Systems, pp. 776-778, 2007 50th Midwest Symposium on Circuits and Systems, MWSCAS - Conference, Montreal, QC, Canada, 8/5/07. https://doi.org/10.1109/MWSCAS.2007.4488692

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AB - This paper describes a feed-forward neural network model (FFNNM) for complexity prediction of path related objective functions, mainly average path length (APL) of an arbitrary Boolean function (BF). The proposed model is determined by neural training process of evaluation time derived from the Monte Carlo data of randomly generated BFs. Experimental results show a good correlation between the ISCAS benchmark circuits and those predicted by the FFNNM. This model is capable of providing an estimation of the performance of a circuit prior to its final implementation.

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ER -

A methodology for evaluation time approximation

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