{"title":"Estimating Shortest Circuit Path Length Complexity","authors":"Azam Beg, P. W. Chandana Prasad, S.M.N.A Senenayake","country":null,"institution":"","volume":21,"journal":"International Journal of Computer and Information Engineering","pagesStart":3146,"pagesEnd":3151,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/11750","abstract":"When binary decision diagrams are formed from\nuniformly distributed Monte Carlo data for a large number of\nvariables, the complexity of the decision diagrams exhibits a\npredictable relationship to the number of variables and minterms. In\nthe present work, a neural network model has been used to analyze the\npattern of shortest path length for larger number of Monte Carlo data\npoints. The neural model shows a strong descriptive power for the\nISCAS benchmark data with an RMS error of 0.102 for the shortest\npath length complexity. Therefore, the model can be considered as a\nmethod of predicting path length complexities; this is expected to lead\nto minimum time complexity of very large-scale integrated circuitries\nand related computer-aided design tools that use binary decision\ndiagrams.","references":null,"publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 21, 2008"}