Challenges and Opportunities toward Online Training Acceleration using RRAM-based Hardware Neural Network

Abstract

This paper highlights the feasible routes of using resistive memory (RRAM) for accelerating online training of deep neural networks (DNNs). A high degree of asymmetric nonlinearity in analog RRAMs could be tolerated when weight update algorithms are optimized with reduced training noise. Hybrid-weight Net (HW-Net), a modified multilayer perceptron (MLP) algorithm that utilizes hybrid internal analog and external binary weights is also proposed. Highly accurate online training could be realized using simple binary RRAMs that have already been widely developed as digital memory.