z-logo
Premium
Dense CMOS implementation of a binary‐programmable cellular neural network
Author(s) -
Flak Jacek,
Laiho Mika,
Paasio Ari,
Halonen Kari
Publication year - 2006
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/cta.365
Subject(s) - computer science , template , artificial neural network , cellular neural network , binary number , digital electronics , cmos , chip , computer hardware , computation , computer architecture , transient (computer programming) , process (computing) , parallel computing , computer engineering , electronic circuit , algorithm , electronic engineering , arithmetic , artificial intelligence , mathematics , engineering , programming language , telecommunications , electrical engineering
An implementation of a cellular neural/non‐linear network (CNN) for processing black‐and‐white (B/W) images is presented in which the template terms are 1‐bit programmable. Such approach leads to a very compact implementation of the coefficient circuits and fast (digital) programming. In this programming scheme, the more complex templates are split into subtasks that are run successively. The structure allows a direct or algorithmic evaluation of the majority of templates proposed for B/W images. The transient mask is utilized in performing the local logic operations as well as in template operations. The proposed architecture is suitable for high‐density implementations. A test structure of a 4 × 4 network has been implemented with a standard digital 0.18‐µm CMOS process. One cell occupies only 155 µm 2 , making possible the implementations of very large networks on a single chip. The algorithms used for the logic function computations and selected template evaluations are described, and the corresponding measurement results are shown. Copyright © 2006 John Wiley & Sons, Ltd.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom