Voltage-controlled magnetoelectric devices for neuromorphic diffusion process
Abstract Neuromorphic diffusion models have become one of the major breakthroughs in the field of generative artificial intelligence. Unlike discriminative models that have been well developed to tackle classification or regression tasks, diffusion models aim at creating content based upon contexts...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58932-x |
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| author | Yang Cheng Qingyuan Shu Albert Lee Haoran He Ivy Zhu Minzhang Chen Renhe Chen Zirui Wang Hantao Zhang Chih-Yao Wang Shan-Yi Yang Yu-Chen Hsin Cheng-Yi Shih Hsin-Han Lee Ran Cheng Kang L. Wang |
| author_facet | Yang Cheng Qingyuan Shu Albert Lee Haoran He Ivy Zhu Minzhang Chen Renhe Chen Zirui Wang Hantao Zhang Chih-Yao Wang Shan-Yi Yang Yu-Chen Hsin Cheng-Yi Shih Hsin-Han Lee Ran Cheng Kang L. Wang |
| author_sort | Yang Cheng |
| collection | DOAJ |
| description | Abstract Neuromorphic diffusion models have become one of the major breakthroughs in the field of generative artificial intelligence. Unlike discriminative models that have been well developed to tackle classification or regression tasks, diffusion models aim at creating content based upon contexts learned. However, the more complex algorithms of these models result in high computational costs using today’s technologies. Here, we develop a spintronic voltage-controlled magnetoelectric memory hardware for the neuromorphic diffusion process. The in-memory computing capability of our spintronic devices goes beyond current Von Neumann architecture, where memory and computing units are separated. Together with the non-volatility of magnetic memory, we can achieve high-speed and low-cost computing, which is desirable for the increasing scale of generative models in the current era. We experimentally demonstrate that the hardware-based true random diffusion process can be implemented for image generation and achieve comparable image quality to software-based training as measured by the Fréchet inception distance (FID) score, achieving ~103 better energy-per-bit-per-area over traditional hardware. |
| format | Article |
| id | doaj-art-e6ab33248a1241d380ce036db7337345 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e6ab33248a1241d380ce036db73373452025-08-20T02:00:03ZengNature PortfolioNature Communications2041-17232025-05-011611810.1038/s41467-025-58932-xVoltage-controlled magnetoelectric devices for neuromorphic diffusion processYang Cheng0Qingyuan Shu1Albert Lee2Haoran He3Ivy Zhu4Minzhang Chen5Renhe Chen6Zirui Wang7Hantao Zhang8Chih-Yao Wang9Shan-Yi Yang10Yu-Chen Hsin11Cheng-Yi Shih12Hsin-Han Lee13Ran Cheng14Kang L. Wang15Department of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Physics, The Ohio State UniversityDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Physics and Astronomy, University of CaliforniaIndustrial Technology Research InstituteIndustrial Technology Research InstituteIndustrial Technology Research InstituteIndustrial Technology Research InstituteIndustrial Technology Research InstituteDepartment of Electrical and Computer Engineering, University of CaliforniaDepartment of Electrical and Computer Engineering, University of CaliforniaAbstract Neuromorphic diffusion models have become one of the major breakthroughs in the field of generative artificial intelligence. Unlike discriminative models that have been well developed to tackle classification or regression tasks, diffusion models aim at creating content based upon contexts learned. However, the more complex algorithms of these models result in high computational costs using today’s technologies. Here, we develop a spintronic voltage-controlled magnetoelectric memory hardware for the neuromorphic diffusion process. The in-memory computing capability of our spintronic devices goes beyond current Von Neumann architecture, where memory and computing units are separated. Together with the non-volatility of magnetic memory, we can achieve high-speed and low-cost computing, which is desirable for the increasing scale of generative models in the current era. We experimentally demonstrate that the hardware-based true random diffusion process can be implemented for image generation and achieve comparable image quality to software-based training as measured by the Fréchet inception distance (FID) score, achieving ~103 better energy-per-bit-per-area over traditional hardware.https://doi.org/10.1038/s41467-025-58932-x |
| spellingShingle | Yang Cheng Qingyuan Shu Albert Lee Haoran He Ivy Zhu Minzhang Chen Renhe Chen Zirui Wang Hantao Zhang Chih-Yao Wang Shan-Yi Yang Yu-Chen Hsin Cheng-Yi Shih Hsin-Han Lee Ran Cheng Kang L. Wang Voltage-controlled magnetoelectric devices for neuromorphic diffusion process Nature Communications |
| title | Voltage-controlled magnetoelectric devices for neuromorphic diffusion process |
| title_full | Voltage-controlled magnetoelectric devices for neuromorphic diffusion process |
| title_fullStr | Voltage-controlled magnetoelectric devices for neuromorphic diffusion process |
| title_full_unstemmed | Voltage-controlled magnetoelectric devices for neuromorphic diffusion process |
| title_short | Voltage-controlled magnetoelectric devices for neuromorphic diffusion process |
| title_sort | voltage controlled magnetoelectric devices for neuromorphic diffusion process |
| url | https://doi.org/10.1038/s41467-025-58932-x |
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