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Sequential Bayesian inference and Monte-Carlo sampling using memristor stochasticity
File(s)
Author(s)
Malik, Adil
Papavassiliou, Christos
Type
Journal Article
Abstract
In this paper, we study the stochastic state trajectory and conductance distributions of memristors under periodic pulse excitation. Our results, backed by experimental evidence, reveal that practical memristors exhibit a 1/f2 Brownian noise power spectrum. Based on this, we develop a Memristive Distribution Generator (MDG) circuit that produces tunable analog distributions by exploiting the physical stochasticity of memristors. By encoding the prior distributions of Bayesian problems in the physical output samples of these circuits, we demonstrate that Monte-Carlo sampling can be devised without knowledge of the analytical output distribution of the memristor. Using examples of 1-D Bayesian linear regression and a dynamic 2-D nonlinear localisation problem, we show how MDG circuits can act as a tunable source of randomness, efficiently representing distributions of interest. Our results, obtained using Pt/TiO2/Pt memristors, validate the use of memristor-based MDGs for implementing probabilistic algorithms.
Date Issued
2024-12-01
Date Acceptance
2024-09-24
Citation
IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2024, 71 (12), pp.5506-5518
URI
URL
DOI
ISSN
1549-8328
Publisher
Institute of Electrical and Electronics Engineers
Start Page
5506
End Page
5518
Journal / Book Title
IEEE Transactions on Circuits and Systems Part 1: Regular Papers
Volume
71
Issue
12
Copyright Statement
Copyright © 2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Subjects
AI hardware
Bayes methods
Bayesian inference
Circuits
Engineering
Engineering, Electrical & Electronic
Generators
Memristor
Memristors
model
Monte Carlo methods
Monte Carlo sampling
Noise
particle filter
probabilistic computing
Pulse measurements
Random variables
Science & Technology
Stochastic processes
Technology
Trajectory
Publication Status
Published
Date Publish Online
2024-10-03