An Ultra-Low Power and Fast Ising Machine using Voltage-Controlled Magnetoresistive Random Access Memory
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911513368854528 |
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| author | Li, Sai Zhang, Yihao Lee, Albert Zhu, Zheng Zeng, Lang Wang, Peng Gao, Lei Wu, Di Zhao, Weisheng |
| author_facet | Li, Sai Zhang, Yihao Lee, Albert Zhu, Zheng Zeng, Lang Wang, Peng Gao, Lei Wu, Di Zhao, Weisheng |
| contents | Physics-inspired computing paradigms, such as Ising machines, are emerging as promising hardware alternatives to traditional von Neumann architectures for tackling computationally intensive combinatorial optimization problems (COPs). While quantum, optical, and electronic devices have garnered significant attention for their potential in realizing Ising machines, their translation into practical systems for industry-relevant applications remains challenging, with each approach facing specific limitations in power consumption and speed. To address this challenge, we report the first chip-level spintronic Ising machine using voltage-controlled magnetoresistive random access memory. The core of our design leverages magnetic tunnel junctions (MTJs) driven by the voltage-controlled magnetic anisotropy effect to realize the probabilistic update of Ising spins through a new mechanism. It enables a latency below 1 ns and an energy consumption under 40 fJ per spin update, achieving a 1000-times improvement over previous current-driven MTJ-based implementations. We map two real-world COPs in electronic design automation-global routing and layer assignment-onto the Ising model and demonstrate high-quality results with an energy efficiency of 25000 solutions per second per watt. This outperforms state-of-the-art quantum and graphics processing units by six and seven orders of magnitude, respectively. These results establish voltage-controlled spintronics as a compelling route towards next-generation physics-inspired machine intelligence, offering a paradigm for ultra-low-power, high-speed, and scalable computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19106 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Ultra-Low Power and Fast Ising Machine using Voltage-Controlled Magnetoresistive Random Access Memory Li, Sai Zhang, Yihao Lee, Albert Zhu, Zheng Zeng, Lang Wang, Peng Gao, Lei Wu, Di Zhao, Weisheng Applied Physics Disordered Systems and Neural Networks Physics-inspired computing paradigms, such as Ising machines, are emerging as promising hardware alternatives to traditional von Neumann architectures for tackling computationally intensive combinatorial optimization problems (COPs). While quantum, optical, and electronic devices have garnered significant attention for their potential in realizing Ising machines, their translation into practical systems for industry-relevant applications remains challenging, with each approach facing specific limitations in power consumption and speed. To address this challenge, we report the first chip-level spintronic Ising machine using voltage-controlled magnetoresistive random access memory. The core of our design leverages magnetic tunnel junctions (MTJs) driven by the voltage-controlled magnetic anisotropy effect to realize the probabilistic update of Ising spins through a new mechanism. It enables a latency below 1 ns and an energy consumption under 40 fJ per spin update, achieving a 1000-times improvement over previous current-driven MTJ-based implementations. We map two real-world COPs in electronic design automation-global routing and layer assignment-onto the Ising model and demonstrate high-quality results with an energy efficiency of 25000 solutions per second per watt. This outperforms state-of-the-art quantum and graphics processing units by six and seven orders of magnitude, respectively. These results establish voltage-controlled spintronics as a compelling route towards next-generation physics-inspired machine intelligence, offering a paradigm for ultra-low-power, high-speed, and scalable computation. |
| title | An Ultra-Low Power and Fast Ising Machine using Voltage-Controlled Magnetoresistive Random Access Memory |
| topic | Applied Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2505.19106 |