Mott memristor based stochastic neurons for probabilistic computing

Many studies suggest that probabilistic spiking in biological neural systems is beneficial as it aids learning and provides Bayesian inference-like dynamics. If appropriately utilised, noise and stochasticity in nanoscale devices can benefit neuromorphic systems. In this paper, we build a stochastic...

Full description

Saved in:
Bibliographic Details
Published in:Nanotechnology 2024-07, Vol.35 (29), p.295201
Main Authors: Fida, Aabid Amin, Mittal, Sparsh, Khanday, Farooq Ahmad
Format: Article
Language:English
Subjects:
insulator to metal transition
nanoscale
spiking neural networks
stochastic leaky integrate and fire
threshold memristor
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many studies suggest that probabilistic spiking in biological neural systems is beneficial as it aids learning and provides Bayesian inference-like dynamics. If appropriately utilised, noise and stochasticity in nanoscale devices can benefit neuromorphic systems. In this paper, we build a stochastic leaky integrate and fire (LIF) neuron, utilising a Mott memristor's inherent stochastic switching dynamics. We demonstrate that the developed LIF neuron is capable of biological neural dynamics. We leverage these characteristics of the proposed LIF neuron by integrating it into a population-coded spiking neural network and a spiking restricted Boltzmann machine (sRBM), thereby showcasing its ability to implement probabilistic learning and inference. The sRBM achieves a software-comparable accuracy of 87.13%. Unlike CMOS-based probabilistic neurons, our design does not require any external noise sources. The designed neurons are highly energy efficient and ultra-compact, requiring only three components: a resistor, a capacitor and a memristor device.
ISSN:0957-4484
1361-6528
DOI:10.1088/1361-6528/ad3c4b