Neuromorphic computing based on Analog ReRAM as low power solution for edge application

We have developed neuromorphic computing based on Analog ReRAM, Resistive Analog Neuromorphic Device (RAND), as low power solution for edge application. We have proposed perceptron circuit which has resistive elements to store weights as analog resistance and binarizes output from each layer in order to realize large scale integration and keep high accuracy. We have fabricated 180nm test chip by using mass production process and we have demonstrated MNIST recognition and sensor application in flexible network architecture in which several neural networks can be configured at the same time. We present potential of low power solution by scaling down to 40nm node under development and reliability issues to be considered in neural network processors based on analog nonvolatile memories.