Azam Beg and P. W. Chandana Prasad and S.M.N.A Senenayake
Estimating Shortest Circuit Path Length Complexity
3146 - 3150
2008
2
9
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/11750
https://publications.waset.org/vol/21
World Academy of Science, Engineering and Technology
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 largescale integrated circuitries
and related computeraided design tools that use binary decision
diagrams.
Open Science Index 21, 2008