%0 Journal Article
	%A Azam Beg and  P. W. Chandana Prasad and  S.M.N.A Senenayake
	%D 2008
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 21, 2008
	%T Estimating Shortest Circuit Path Length Complexity
	%U https://publications.waset.org/pdf/11750
	%V 21
	%X When binary decision diagrams are formed from
uniformly distributed Monte Carlo data for a large number of
variables, the complexity of the decision diagrams exhibits a
predictable relationship to the number of variables and minterms. In
the present work, a neural network model has been used to analyze the
pattern of shortest path length for larger number of Monte Carlo data
points. The neural model shows a strong descriptive power for the
ISCAS benchmark data with an RMS error of 0.102 for the shortest
path length complexity. Therefore, the model can be considered as a
method of predicting path length complexities; this is expected to lead
to minimum time complexity of very large-scale integrated circuitries
and related computer-aided design tools that use binary decision
diagrams.
	%P 3146 - 3150