Energy-Information Trade-Off in Self-Directed Channel Memristors

TL;DR

This paper investigates the energy costs and information capacity trade-offs in Self-Directed Channel memristors, combining experimental modeling and generative AI to characterize storage capabilities over time.

Contribution

It introduces an energy-information trade-off framework for SDC memristors, using experiments and cGAN modeling to analyze storage states and capacity over delays.

Findings

Identified energy requirements for different device states

Modeled stability of states over time

Estimated energy-information curves for various delays

Abstract

Understanding the nature of information storage on memristors is vital to enable their use in novel data storage and neuromorphic applications. One key consideration in information storage is the energy cost of storage and what impact the available energy has on the information capacity of the devices. In this paper, we propose and study an energy-information trade-off for a particular kind of memristive device - Self-Directed Channel (SDC) memristors. We perform experiments to model the energy required to set the devices into various states, as well as assessing the stability of these states over time. Based on these results, we employ a generative modelling approach, using a conditional Generative Adversarial Network (cGAN) to characterise the storage conditional distribution, allowing us to estimate energy-information curves for a range of storage delays, showing the graceful trade-off between energy consumed and the effective capacity of the devices.