By Topic

Parametric fault identification and dynamic compensation techniques for cellular neural network hardware

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Grimaila, M.R. ; Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA ; de Gyvez, J.P.

Testing strategies to quantify parametric faults in a fully programmable, two-dimensional cellular neural network (CNN) are presented. The approach is intended to quantify system offsets, time constant mismatches, nonlinearities in the multipliers and state nodes, and the magnitude of the dynamic range of operation which can lead to misconvergence in the CNN array. For some cases, the authors present dynamic solutions by compensating the templates, the input data, and/or the initial condition values to minimise or cancel the undesired effects. The proposed dynamic compensation techniques can be applied to any CNN independent of the array size or topology. To demonstrate the feasibility of the proposed techniques, the authors examine their application to an actual complex VLSI CNN implementation

Published in:

Circuits, Devices and Systems, IEE Proceedings -  (Volume:143 ,  Issue: 5 )