Abstract
Functional decomposition has important applications in many fields of modern engineering and science, such as combinational and sequential logic synthesis for VLSI systems, pattern analysis, knowledge discovery, machine learning, decision systems, data bases, data mining etc. Its practical usefulness for very complex systems is however limited by lack of an effective and efficient method for selection of the appropriate input supports for sub-systems. A classical method based on a systematic search of the whole solution space is inefficient.In this paper, an effective and efficient heuristic method for input support selection is proposed and discussed. The method is based on application of information relationship measures, which allows us to reduce the search space to a manageable size while keeping the high-quality solutions in the reduced space. The experimental results demonstrate that the proposed heuristic method is able to construct optimal or near optimal supports very efficiently even for large systems. It is much faster than the systematic method while delivering results of comparable quality.