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Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors
Kyung Seok Woo1, Jaehyun Kim1, Janguk Han1
1Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul, 08826, Republic of Korea.
Nature Communications
|September 30, 2022
Summary
This study introduces probabilistic computing (p-computing) using novel memristor devices. These devices enable efficient complex calculations, paving the way for advanced big data processing.
Area of Science:
- Materials Science
- Computer Science
- Electrical Engineering
Background:
- The proliferation of big data necessitates advanced computing schemes.
- Probabilistic computing (p-computing) offers an efficient approach using probabilistic bits (p-bits).
Purpose of the Study:
- To propose and demonstrate a p-computing scheme utilizing the threshold switching (TS) behavior of a novel diffusive memristor.
- To explore the potential of memristor-based p-bits for complex computational tasks.
Main Methods:
- Fabrication and characterization of a Cu0.1Te0.9/HfO2/Pt (CTHP) diffusive memristor.
- Implementation of p-bits based on the stochastic TS behavior of CTHP memristors.
- Development of a p-computing network architecture inspired by Hopfield networks.
Main Results:
- Demonstrated realization of memristor-based p-bits with probability controlled by input voltage.
- Enabled all 16 Boolean logic operations (forward and inverted) using the memristor network.
- Showcased potential for complex operations like full adders and factorization.
Conclusions:
- CTHP diffusive memristors can effectively implement p-bits for probabilistic computing.
- Memristor-based p-computing provides a scalable solution for big data challenges.
- This approach offers a pathway to more efficient and versatile computational systems.
