Voltage-controlled magnetoelectric devices for neuromorphic diffusion process

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 and Kang L. Wang ()
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Yang Cheng: University of California
Qingyuan Shu: University of California
Albert Lee: University of California
Haoran He: University of California
Ivy Zhu: The Ohio State University
Minzhang Chen: University of California
Renhe Chen: University of California
Zirui Wang: University of California
Hantao Zhang: University of California
Chih-Yao Wang: Industrial Technology Research Institute
Shan-Yi Yang: Industrial Technology Research Institute
Yu-Chen Hsin: Industrial Technology Research Institute
Cheng-Yi Shih: Industrial Technology Research Institute
Hsin-Han Lee: Industrial Technology Research Institute
Ran Cheng: University of California
Kang L. Wang: University of California

Nature Communications, 2025, vol. 16, issue 1, 1-8

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58932-x

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