In recent years the functional decomposition has found an application 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. However, its practical usefulness for very complex systems has been limited by the lack of an efficient method for selecting the appropriate input variable partitioning. This is an NP-hard problem and thus heuristic methods have to be used to efficiently and effectively search for optimal or near-optimal solutions. In this paper, a heuristic method for the input variable partitioning is discussed. The method is based on an application of evolutionary algorithms, what allows exploring the possible solution space of problem while keeping the high-quality solutions in this reduced space. The experimental results show that the proposed heuristic method is able to construct an optimal or near optimal solution very efficiently even for large systems. It is much faster than the systematic method while delivering results of comparable quality.
Przedstawiono narzędzie wspomagające dekompozycję układów logicznych z użyciem oprogramowania uniwersyteckiego Demain i Spark. Narzędzie znacznie skraca czas potrzebny do przeprowadzenia pojedynczego badania przez wygenerowanie gotowego kodu źródłowego w języku opisu sprzętu. Wynikowy kod źródłowy umożliwia bezpośrednią syntezę układu cyfrowego za pomocą systemów komercyjnych.
EN
The paper examines the usefulness of the university tools by the decomposition of example logic functions and analyzes various strategies of the decomposition of truth tables using the notion of r-admissibility. Subsequently, statistically optimal management strategy for the Spark software have been established. Final results have been compared with the Altera Quartus II system.
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