Energy efficient perceptron pattern recognition using segmented memristor crossbar arrays

This paper presents a segmented memristor crossbar array capable of performing pattern recognition tasks. Partial transistor isolation is used to segment smaller memristor crossbar structures. The synaptic density is less than that of a single large memristor crossbar, although this system is much more energy efficient. This system also reduces the amount of unwanted current paths that are a byproduct of large restive crossbar arrays. The proposed system is validated using SPICE simulations that utilize an accurate memristor model that we previously published. Additionally, wire resistance between memristor devices is accounted for to study how a realistic memristor circuit would perform in terms of energy, area, and ability to classify patterns. In this detailed implementation, the proposed system was shown to classify both 16 and 32 pixel images.