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EVALUATION OF CNN TEMPLATE ROBUSTNESS TOWARDS VLSI IMPLEMENTATION
Author(s) -
KINGET PETER,
STEYAERT MICHIEL
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(199601/02)24:1<111::aid-cta907>3.0.co;2-x
Subject(s) - template , robustness (evolution) , very large scale integration , computer science , parametric statistics , cellular neural network , computer engineering , artificial neural network , computer architecture , artificial intelligence , embedded system , mathematics , programming language , biochemistry , chemistry , statistics , gene
In this paper a method for the evaluation of static robustness towards random variations in cellular neural network (CNN) templates is proposed. From this evaluation, circuit accuracy specifications for a VLSI implementation are derived which allow the designer to optimize the performance. Moreover, from this evaluation method, guidelines for robust template designs are derived and parametric testing templates are developed.