Orientation classification by a winner-take-all network with oxide RRAM based synaptic devices

An emerging application for the oxide based resistive random access memory (RRAM) technology is to serve as the synaptic device for the hardware implementation of neuromorphic computing. The gradual resistance modulation capability in RRAM is proposed for emulating analog synapses, and the stochastic switching behavior in RRAM is proposed for emulating binary synapses. In order to evaluate the effectiveness of analog synapses and binary synapses in realizing the competitive learning algorithm, a simulation of winner-take-all network is performed based on the parameters extracted from the experiments. The simulation suggests that the orientation classification can be effectively realized using both analog synapses and binary synapses.