Source-linked AI summary
Stochastic Memristive Devices for Computing and Neuromorphic Applications
Siddharth Gaba, Patrick Sheridan, Jiantao Zhou, Shinhyun Choi, Wei Lu
TL;DR
The paper addresses significant randomness and temporal variation in resistive switches. It characterizes and controls switching behavior, then demonstrates stochastic and neuromorphic computing approaches that exploit this behavior.
Problem
Significant randomness or temporal variations in resistive switches remain a problem to overcome before potential commercialization.
Method
The paper analyzes temporal variations in switching behavior and characterizes whether switching behavior can be well characterized and controlled.
Results
The paper demonstrates stochastic and neuromorphic computing approaches using the devices' stochastic switching behavior.
Takeaways & Limitations
Random individual switching locations can support noise-tolerant representations in which a bit flip causes an error of 1/n.
Takeaways & Limitations
The locations of individual 1s remain random, and the paper treats this randomness as essential to its approach.
Abstract
from arXiv · showhide
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.