A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

Abstract

In this work, a synaptic weight transfer method for a neuromorphic system based on resistive-switching random-access memory (RRAM) is proposed and validated. To implement the on-chip trainable neuromorphic system which utilizes large-scale hardware synapse units, a fast and reliable write scheme needs to be established. Based on the experimental results, it is confirmed that the gradual set and full reset operation is the most suitable operation scheme for fast programming due to the fundamental reliability characteristics of the resistive-switching memory cell. Also, the superiority of this programming method using the proposed RRAM compact model is demonstrated. In addition, a one weight/one synaptic device structure is newly adopted for realizing high-density synapse arrays by using a nonnegative weight constraint in supervised learning. Finally, the pattern recognition accuracies obtained at the software and hardware levels are compared.