NDLI: Efficient method of input variable partitioning in functional decomposition based on evolutionary algorithms

Content Provider	IEEE Xplore Digital Library
Author	Rawski, M. Selvaraj, H. Morawiecki, P.
Copyright Year	2004
Description	Author affiliation: Inst. of Telecommun., Warsaw Univ. of Technol., Poland (Rawski, M.)
Abstract	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, the lack of an effective and efficient method of the input variable partitioning limits its practical usefulness in complex systems. A classical method based on a systematic search of the whole solution space is inefficient due to its nonpolynomial time complexity. In this paper, a heuristic method for the input variable partitioning is proposed and discussed. The method is based on the application of evolutionary algorithms that allows exploring the possible solution space of a 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.
Starting Page	136
Ending Page	143
File Size	304141
Page Count	8
File Format	PDF
ISBN	0769522033
DOI	10.1109/DSD.2004.1333269
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-31
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Input variables Evolutionary computation CMOS logic circuits Logic design Network synthesis Machine learning Productivity Microelectronics Digital circuits CMOS technology
Content Type	Text
Resource Type	Article

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