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Cellular neural networks as a general massively parallel computational paradigm
Author(s) -
Destri Giulio,
Marenzoni Paolo
Publication year - 1996
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/(sici)1097-007x(199605/06)24:3<397::aid-cta924>3.0.co;2-n
Subject(s) - massively parallel , computer science , locality , workstation , artificial neural network , parallel computing , cellular neural network , general purpose , theoretical computer science , transmission (telecommunications) , range (aeronautics) , computational science , computer architecture , artificial intelligence , telecommunications , philosophy , linguistics , materials science , composite material , operating system
In this paper is presented the use of the discrete‐time cellular neural network (DTCNN) paradigm to develop algorithms devised for general‐purpose massively parallel processing (MPP) systems. This paradigm is defined in discrete N ‐dimensional spaces (lattices) and is characterized by the locality of the direct information transmission between the space points (cells) and by continuous values of data and parameters; the DTCNN paradigm is thus able to express most of the typical MPP applications. A general version of a DTCNN has been implemented and optimized for three MPP architectures, namely the Connection Machines CM‐2 and CM‐5 and the Cray T3D. The comparison between the three machine performances with those achieved by a standard SPARC‐20 workstation shows that, particularly with large lattices, the speed‐up allowed in the computational times is significant and the range of solvable problem sizes is widely extended.