Issue 11, 2024

Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor

Abstract

Bayesian networks and Bayesian inference, which forecast uncertain causal relationships within a stochastic framework, are used in various artificial intelligence applications. However, implementing hardware circuits for the Bayesian inference has shortcomings regarding device performance and circuit complexity. This work proposed a Bayesian network and inference circuit using a Cu0.1Te0.9/HfO2/Pt volatile memristor, a probabilistic bit neuron that can control the probability of being ‘true’ or ‘false.’ Nodal probabilities within the network are feasibly sampled with low errors, even with the device's cycle-to-cycle variations. Furthermore, Bayesian inference of all conditional probabilities within the network is implemented with low power (<186 nW) and energy consumption (441.4 fJ), and a normalized mean squared error of ∼7.5 × 10−4 through division feedback logic with a variational learning rate to suppress the inherent variation of the memristor. The suggested memristor-based Bayesian network shows the potential to replace the conventional complementary metal oxide semiconductor-based Bayesian estimation method with power efficiency using a stochastic computing method.

Graphical abstract: Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor

Supplementary files

Article information

Article type
Paper
Submitted
31 Dec 2023
Accepted
04 Apr 2024
First published
05 Apr 2024
This article is Open Access
Creative Commons BY-NC license

Nanoscale Adv., 2024,6, 2892-2902

Implementation of Bayesian networks and Bayesian inference using a Cu0.1Te0.9/HfO2/Pt threshold switching memristor

I. K. Baek, S. H. Lee, Y. H. Jang, H. Park, J. Kim, S. Cheong, S. K. Shim, J. Han, J. Han, G. S. Jeon, D. H. Shin, K. S. Woo and C. S. Hwang, Nanoscale Adv., 2024, 6, 2892 DOI: 10.1039/D3NA01166F

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