A neural network approach to PLA folding problems

Kazuhiro Tsuchiya, Yoshiyasu Takefuji

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A near-optimum parallel algorithm for solving PLA folding problems is presented in this paper where the problem is NP-complete and one of the most fundamental problems in VLSI design. The proposed system is composed of n X n neurons based on an artificial two-dimensional maximum neural network where n is the number of inputs and outputs or the number of product lines of PLA. The two-dimensional maximum neurons generate the permutation of inputs and outputs or product lines. Our algorithm can solve not only a simple folding problem but also multiple, bipartite, and constrained folding problems. We have discovered improved solutions in four benchmark problems over the best existing algorithms using the proposed algorithm.

Original languageEnglish
Pages (from-to)1299-1305
Number of pages7
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume15
Issue number10
DOIs
Publication statusPublished - 1996

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Neural networks
Neurons
Parallel algorithms
Computational complexity

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

A neural network approach to PLA folding problems. / Tsuchiya, Kazuhiro; Takefuji, Yoshiyasu.

In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 15, No. 10, 1996, p. 1299-1305.

Research output: Contribution to journalArticle

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