Existing studies have demonstrated the use of memristor crossbars for learning linearly separable functions. The memristors are used as analog synaptic weights, thus allowing the memristor crossbar to evaluate a large number of multiplication and addition operations concurrently in the analog domain. Non-linearly separable functions can be implemented by cascading two or more crossbars, with each crossbar implementing a linearly separable function. The training circuits for these cascaded crossbars implementing non-linearly separable functions requires more complex logic than for linearly separable functions. In this paper we have implemented non-linear classifiers utilizing multiple linear separators and thus can utilize a simpler training circuit. We have examined the implementation of Boolean functions and motion detection applications as case studies.


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    Title :

    Memristor crossbar based low cost classifiers and their applications


    Contributors:


    Publication date :

    2014-06-01


    Size :

    737094 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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