Analysis of Dynamic Linear and Non-linear Memristor Device Models for Emerging Neuromorphic Computing Hardware Design

TL;DR

This paper evaluates linear and non-linear memristor device models to facilitate efficient and accurate neuromorphic hardware design and large-scale system simulations.

Contribution

It provides a comparative analysis of practical memristor models focusing on their suitability for neuromorphic computing architecture simulation.

Findings

Analysis of linear and non-linear memristor models

Assessment of model complexity and simulation accuracy

Guidelines for model selection in neuromorphic design

Abstract

The value memristor devices offer to the neuromorphic computing hardware design community rests on the ability to provide effective device models that can enable large scale integrated computing architecture application simulations. Therefore, it is imperative to develop practical, functional device models of minimum mathematical complexity for fast, reliable, and accurate computing architecture technology design and simulation. To this end, various device models have been proposed in the literature seeking to characterize the physical electronic and time domain behavioral properties of memristor devices. In this work, we analyze some promising and practical non-quasi-static linear and non-linear memristor device models for neuromorphic circuit design and computing architecture simulation.