Memristor crossbar based implementation of a multilayer perceptron

This paper describes a memristor-based neuromorphic system that can be used for ex-situ training of various multi-layer neural network algorithms. This system is based on an analog neuron circuit that is capable of performing an accurate dot product calculation. The presented ex-situ programming technique can be used to map many key neural algorithms directly onto the grid of resistances in a memristor crossbar. Using this weight-to-crossbar mapping approach along with the dot product calculation circuit, complex neural algorithms can be easily implemented using this system. To show the effectiveness and versatility of this circuit, a Multilayer Perceptron (MLP) is trained to perform Sobel edge detection. Following these simulations, an analysis was presented that shows how both memristor accuracy and neuron circuit gain relates to output error. Additionally, this paper discusses how circuit noise and neural network layout contribute to testing accuracy.