Stochastic Memristive Devices for Computing and Neuromorphic Applications

TL;DR

This paper demonstrates that stochastic memristive devices, with predictable switching probabilities, can be effectively used for error-tolerant computing and neuromorphic applications, leveraging their inherent randomness rather than suppressing it.

Contribution

It introduces the use of inherently stochastic memristors as reliable building blocks for error-tolerant computing and neuromorphic systems, exploiting their probabilistic switching behavior.

Findings

Switching in memristors is fully stochastic but predictable.

Memristor-based stochastic bitstreams can be generated in time and space.

Arrays of binary memristors can function as multi-level analog devices.

Abstract

Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially large variations in space and in time in these nanoscale devices. Here we show that in metal-filament based memristive devices the switching can be fully stochastic. While individual switching events are random, the distribution and probability of switching can be well predicted and controlled. Rather than trying to force high switching probabilities using excessive voltage or time, the inherent stochastic nature of resistive switching allows these binary devices to be used as building blocks for novel error-tolerant computing schemes such as stochastic computing and provide a needed "analog" feature in neuromorphic applications. To verify such potential, we demonstrated memristor-based stochastic bitstreams in both time and space domains, and show that an array of binary memristors can act as a multi-level "analog" device for neuromorphic applications.