Early-Stage Fluctuation in Low-Power Analog Resistive Memory: Impacts on Neural Network and Mitigation Approach

Abstract

In this letter, a new reliability phenomenon, named early-stage resistance fluctuation (ERF), in analog resistive random-access memory (RRAM) devices and its impact on the neuromorphic computing are investigated. ERF is found to be a non-negligible random fluctuation behavior of RRAM resistance within ~10,000s after verification due to stochastic migration of vacancies. As a result, ERF can induce weight noise in RRAM-based neural network even with write-verify operation and cause a significant (~42%) drop of recognition accuracy on the MNIST dataset recognition tasks. An approach incorporating automatic early-stop and dropout during training is proposed to reduce the impacts of ERF. Results show that the recognition accuracy with ERF can be improved from 58% to 96% after adopting the proposed optimization method.