Analyses of a 1-layer neuromorphic network using memristive devices with non-continuous resistance levels

A Siemon,D J Wouters,S Ferch,R Waser,A Heittmann,S Menzel

doi:10.1063/1.5108658

Abstract

The emerging nonvolatile memory technology of redox-based resistive switching (RS) devices is not only a promising candidate for future high density memories but also for computational and neuromorphic applications. In neuromorphic as well as in memory applications, RS devices are configured in nanocrossbar arrays, which are controlled by CMOS circuits. With those hybrid systems, brain-inspired artificial neural networks can be built up and trained by using a learning algorithm. First works on hardware implementation using relatively large and high current level RS devices are already published. In this work, the influence of small and low current level devices showing noncontinuous resistance levels on neuromorphic networks is studied. To this end, a well-established physical-based Verilog A model is modified to offer continuous and discrete conduction. With this model, a simple one-layer neuromorphic network is simulated to get a first insight and understanding of this problem using a backpropagation algorithm based on the steepest descent method.

Highlights

Current computing systems are designed for computation purposes
A Verilog A model of an electrochemical metallization (ECM) resistive switching RAM (ReRAM) was derived by combining two well-established ECM models, which incorporates the discrete conduction steps that are observed in the experiment at low current levels
It is used to investigate the influence of discrete conduction steps on the training accuracy of a simple one-layer ECM-based ANN using a backpropagation learning algorithm based on the steepest descent method

Summary

Introduction

Current computing systems are designed for computation purposes. there is an increasing need for more cognitive tasks as pattern recognition. Humans, are very good in solving such tasks, people developed brain-inspired artificial neural networks (ANN’s) to handle these tasks. This culminated in the complex, multilayer structured Deep Learning (DL) systems of today that even achieve better-than-human performance.[1]. Current DL ANN systems, are mainly software constructions that still run on classical von Neumann computers. While their computational performance has tremendously increased over the last decades, thanks to the advancement and scaling of CMOS technology, for implementing more cognitive tasks, they are much less efficient in terms of both system size and energy dissipation than the human brain. Especially for edge computation applications, there is a need for more efficient hardware to realize these ANN systems

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