A macromodel fault generator for cellular neural networks

M.R. Grimaila, J. Pineda de Gyvez

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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    A CAD tool based on SPICE macromodels to simulate simplified faulty, circuit realizations of a fully programmable, two dimensional cellular neural network (CNN) is presented. The models can be easily adapted to match the electrical parameters of real circuit implementations. Generic macromodels for both current mode and voltage mode CNNs are provided. The macromodels not only simulate the conceptual CNN cell, but also provide the capability to model actual CNN architectures and their nonidealities. Moreover, macromodeling provides the capability to determine the effect of parameter variation on the operation of the CNN efficiently without the need for computationally expensive, exhaustive circuit simulations. We have used the CNN macromodels to develop robust testing strategies for detecting faults in VLSI implementations of CNN arrays. Three fault cases are introduced into a CNN array to provide insight to the usefulness of macromodeling
    Original languageEnglish
    Title of host publicationProceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA-94, 18-21 December 1994, Rome, Italy
    Place of PublicationNew York
    PublisherInstitute of Electrical and Electronics Engineers
    ISBN (Print)0-7803-2070-0
    Publication statusPublished - 1994


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