Programmable current mode Hebbian learning neural network using programmable metallization cell

Abstract

The design and performance of a Hebbian learning based neural network is presented in this work. In situ analog learning was employed, thus computing the synaptic weight changes continuously during the normal operation of the artificial neural network (ANN). The complexity of a synapse is minimized by using a novel device called the Programmable Metallization Cell (PMC). Simulations with circuits with three PMCs per synapse showed that appropriate learning behaviour was obtained at the synaptic level.