TY - JFULL
AU - Azam Beg and P. W. Chandana Prasad and S.M.N.A Senenayake
PY - 2008/10/
TI - Estimating Shortest Circuit Path Length Complexity
T2 - International Journal of Computer and Information Engineering
SP - 3145
EP - 3150
VL - 2
SN - 1307-6892
UR - https://publications.waset.org/pdf/11750
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 21, 2008
N2 - 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.
ER -