Optimized learning scheme for grayscale image recognition in a RRAM based analog neuromorphic system

Abstract

An analog neuromorphic system is developed based on the fabricated resistive switching memory array. A novel training scheme is proposed to optimize the performance of the analog system by utilizing the segmented synaptic behavior. The scheme is demonstrated on a grayscale image recognition. According to the experiment results, the optimized one improves learning accuracy from 77.83% to 91.32%, decreases energy consumption by more than two orders, and substantially boosts learning efficiency compared to the traditional training scheme.