Reliability Issues in Analog ReRAM Based Neural-Network Processor.

We present reliability issues to be considered in neural network (NN) processors based on analog nonvolatile memories. We developed a reliability model of resistive random access memory (ReRAM). In this model, the properties of conductive filament control the reliability characteristics. A ReRAM based neural-network processor is also demonstrated. Using well-controlled analog currents, we realized MNIST handwritten digits dataset recognition with low power. Furthermore, the difference in handling reliability for digital memory and analog NN processor is discussed. We propose an NN simulation based on ReRAM reliability model in order to consider the reliability of analog NN processor.