Stochastic memristive devices for computing and neuromorphic applications

TLDR

Memristive devices have been explored for memory, logic, and neuromorphic systems, but their nanoscale operation suffers from significant spatial and temporal variability. The study seeks to exploit the inherent stochastic switching of metal‑filament memristors to create error‑tolerant computing schemes and provide analog functionality for neuromorphic applications. Instead of forcing high switching probabilities with excessive voltage or time, the authors use the stochastic nature to generate binary memristor bitstreams in both time and space, demonstrating that an array of such devices can act as a multi‑level analog element. They show that switching is fully stochastic, yet the distribution and probability can be predicted and controlled, and that the resulting stochastic bitstreams enable multi‑level analog behavior suitable for neuromorphic use.

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.