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.
Memristor crossbar based low cost classifiers and their applications
2014-06-01
737094 byte
Conference paper
Electronic Resource
English