a low-cost ann-based approach to implement logic circuits on memristor crossbar array

The memristor crossbar array structure provides a low-cost and highly efficient platform for the artificial neural network (ann) implementation. on the other hand, the implementation of combinational logic circuits using memristor-based platforms has attracted great attention recently. however, the basic operations of a memristor are multiplication (ohm's law) and addition (kirchhoff's circuit laws), which make the implementation of logical operations very complex. to overcome this problem, we propose an ann-based synthesizer that first translates the combinational logic circuit behavior to a neural network, which would be implemented using a memristor crossbar array. the proposed synthesizer includes a feature extractor and a multilayer perceptron (mlp) to classify the input vectors into 0 or 1 groups. the results show that the delay of an ann-crossbar circuit is considerably lower than that of the circuit implemented by memristor-based logic gates. although the accuracy of an ann-crossbar circuit is not 100% because of the natural behavior of ann-based applications, an ann-crossbar circuit could be useful regarding error-resilient systems such as image processing applications. furthermore, these circuits are appropriate for advanced neuromorphic computers that rely on non-deterministic operations.