Probabilistic computing with NbOx metal-insulator transition-based self-oscillatory pbit

Hakseung Rhee, Gwangmin Kim, Hanchan Song, Woojoon Park, Do Hoon Kim, Jae Hyun In, Younghyun Lee and Kyung Min Kim ()
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Hakseung Rhee: Korea Advanced Institute of Science and Technology (KAIST)
Gwangmin Kim: Korea Advanced Institute of Science and Technology (KAIST)
Hanchan Song: Korea Advanced Institute of Science and Technology (KAIST)
Woojoon Park: Korea Advanced Institute of Science and Technology (KAIST)
Do Hoon Kim: Korea Advanced Institute of Science and Technology (KAIST)
Jae Hyun In: Korea Advanced Institute of Science and Technology (KAIST)
Younghyun Lee: Korea Advanced Institute of Science and Technology (KAIST)
Kyung Min Kim: Korea Advanced Institute of Science and Technology (KAIST)

Nature Communications, 2023, vol. 14, issue 1, 1-8

Abstract: Abstract Energy-based computing is a promising approach for addressing the rising demand for solving NP-hard problems across diverse domains, including logistics, artificial intelligence, cryptography, and optimization. Probabilistic computing utilizing pbits, which can be manufactured using the semiconductor process and seamlessly integrated with conventional processing units, stands out as an efficient candidate to meet these demands. Here, we propose a novel pbit unit using an NbOx volatile memristor-based oscillator capable of generating probabilistic bits in a self-clocking manner. The noise-induced metal-insulator transition causes the probabilistic behavior, which can be effectively modeled using a multi-noise-induced stochastic process around the metal-insulator transition temperature. We demonstrate a memristive Boltzmann machine based on our proposed pbit and validate its feasibility by solving NP-hard problems. Furthermore, we propose a streamlined operation methodology that considers the autocorrelation of individual bits, enabling energy-efficient and high-performance probabilistic computing.

Date: 2023
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DOI: 10.1038/s41467-023-43085-6

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