Energy-efficient superparamagnetic Ising machine and its application to traveling salesman problems

Jia Si, Shuhan Yang, Yunuo Cen, Jiaer Chen, Yingna Huang, Zhaoyang Yao, Dong-Jun Kim, Kaiming Cai, Jerald Yoo, Xuanyao Fong and Hyunsoo Yang ()
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Jia Si: National University of Singapore
Shuhan Yang: National University of Singapore
Yunuo Cen: National University of Singapore
Jiaer Chen: National University of Singapore
Yingna Huang: National University of Singapore
Zhaoyang Yao: National University of Singapore
Dong-Jun Kim: National University of Singapore
Kaiming Cai: National University of Singapore
Jerald Yoo: National University of Singapore
Xuanyao Fong: National University of Singapore
Hyunsoo Yang: National University of Singapore

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract The growth of artificial intelligence leads to a computational burden in solving non-deterministic polynomial-time (NP)-hard problems. The Ising computer, which aims to solve NP-hard problems faces challenges such as high power consumption and limited scalability. Here, we experimentally present an Ising annealing computer based on 80 superparamagnetic tunnel junctions (SMTJs) with all-to-all connections, which solves a 70-city traveling salesman problem (TSP, 4761-node Ising problem). By taking advantage of the intrinsic randomness of SMTJs, implementing global annealing scheme, and using efficient algorithm, our SMTJ-based Ising annealer outperforms other Ising schemes in terms of power consumption and energy efficiency. Additionally, our approach provides a promising way to solve complex problems with limited hardware resources. Moreover, we propose a cross-bar array architecture for scalable integration using conventional magnetic random-access memories. Our results demonstrate that the SMTJ-based Ising computer with high energy efficiency, speed, and scalability is a strong candidate for future unconventional computing schemes.

Date: 2024
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DOI: 10.1038/s41467-024-47818-z

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