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Dense CMOS Implementation of a Binary-Programmable Cellular Neural Network

Jacek Flak, Mika Laiho, Ari Paasio, Kari Halonen, Dense CMOS Implementation of a Binary-Programmable Cellular Neural Network. International Journal of Circuit Theory and Applications 34(4), 429–443, 2006.

Abstract:

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-m CMOS process. One cell occupies only 155 m2, 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.

BibTeX entry:

@ARTICLE{jFlLaPaHa06a,
  title = {Dense CMOS Implementation of a Binary-Programmable Cellular Neural Network},
  author = {Flak, Jacek and Laiho, Mika and Paasio, Ari and Halonen, Kari},
  journal = {International Journal of Circuit Theory and Applications},
  volume = {34},
  number = {4},
  editor = {Tamas Roska, Paulo Arena Csaba Rekeczky},
  publisher = {John Wiley & Sons, Ltd.},
  pages = {429–443},
  year = {2006},
}

Belongs to TUCS Research Unit(s): Microelectronics

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