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

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 circui...

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Bibliographic Details
Published in:Nanoscale advances 2024-05, Vol.6 (11), p.2892-2902
Main Authors: Baek, In Kyung, Lee, Soo Hyung, Jang, Yoon Ho, Park, Hyungjun, Kim, Jaehyun, Cheong, Sunwoo, Shim, Sung Keun, Han, Janguk, Han, Joon-Kyu, Jeon, Gwang Sik, Shin, Dong Hoon, Woo, Kyung Seok, Hwang, Cheol Seong
Format: Article
Language:English
Subjects:
Chemistry
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Summary: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 (
ISSN:2516-0230
2516-0230
DOI:10.1039/d3na01166f