Title: Toward CNN chip-specific robustness
Authors: Xavier-de-Souza, S ×
Yalcin, ME
Suykens, Johan
Vandewalle, Joos #
Issue Date: May-2004
Publisher: Ieee-inst electrical electronics engineers inc
Series Title: IEEE Transactions on circuits and systems i-regular papers vol:51 issue:5 pages:892-902
Abstract: The promising potential of cellular neural networks (CNN) has resulted in the development of several template design methods. The CNN universal machine (CNN-UM), a programmable CNN, has made it possible to create image-processing algorithms that run on this platform. However, very large-scale integration implementations of CNN-UMs presented parameter deviations that do not occur on ideal CNN structures. Consequently, new design methods were developed aiming at more robust templates. Although these new templates were indeed more robust, erroneous behavior can still be observed. An alternative for chip-independent robustness is chip-specific optimization, where the template is targeted to an individual chip. This paper describes a solution proposal in this sense to automatically tune templates in order to make the chip react as an ideal CNN structure. The approach uses measurements of actual CNN-UM chips as part of the cost function for a global optimization method to find an optimal template given an initial approximation. Further improvements are achieved by generating chip-specific robust templates by doing a search for the best template among the optimal ones. The tuned templates are therefore customized versions that are expected to be much less sensitive to imperfections on the operation of CNN-UM chips. Results are presented for the binary and grayscale cases, including the case of grayscale output. It is expected that as this technique matures, it will give CNN-UM chips enough reliability to compete with digital systems in terms of robustness in addition to advantages of speed.
ISSN: 1057-7122
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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