Improved extraction method on logic function optimization of mass data processing
一种支持大规模数据逻辑函数优化的改进选拔算法

Extraction method is one of the classical methods that achieve the minimum coverage in two-level logic synthesis. But as the output variables and the prime implicant grow up, both the long processing time and the resource requirement become the major problems to be resolved with the extraction method. To overcome these drawbacks, a new ameliorated algorithm for the coverage minimization was presented in this thesis on the basis of the extraction method theory, which was adapted to the processing of mass data. Based on the intersection iterative and the local search algorithm theory, two major phases in this algorithm were improved, including the extremal selecting and the branches processing. As a result, by using the existing computer resources, testing shows a promising result and the improved algorithm is superior to the others multi-output logic function optimizations.