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

M.R. Grimaila, J. Pineda de Gyvez

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    Abstract

    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
    Original languageEnglish
    Pages (from-to)295-301
    Number of pages7
    JournalIEE Proceedings - Circuits, Devices and Systems
    Volume143
    Issue number5
    DOIs
    Publication statusPublished - 1996

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