The stochastic modeling of TiO2 memristor and its usage in neuromorphic system design

Memristor, the fourth basic circuit element, has shown great potential in neuromorphic circuit design for its unique synapse-like feature. However, though the continuous resistance state of memristor has been expected, obtaining and maintaining an arbitrary intermediate state cannot be well controlled in nowadays memristive system. In addition, the stochastic switching behaviors have been widely observed. To facilitate the investigation on memristor-based hardware implementation, we built a stochastic behavior model of TiO
2
memristive devices based on the real experimental results. By leveraging the stochastic behavior of memristors, a macro cell design composed of multiple parallel connecting memristors can be successfully used in implementing the weight storage unit and the stochastic neuron — the two fundamental components in neural network (NN)s, providing a feasible solution in memristor-based hardware implementation.