Memristive floating-point Fourier neural operator network for efficient scientific modeling.

Journal: Science advances
Published Date: Jun 20, 2025

Abstract

Emerging artificial intelligence for science (AI-for-Science) algorithms, such as the Fourier neural operator (FNO), enabled fast and efficient scientific simulation. However, extensive data transfers and intensive high-precision computing are necessary for network training, which challenges conventional digital computing platforms. Here, we demonstrated the potential of a heterogeneous computing-in-memristor (CIM) system to accelerate the FNO for scientific modeling tasks. Our system contains eight four-kilobit memristor chips with embedded floating-point computing workflows and a heterogeneous training scheme, representing a heterogeneous CIM platform that leverages precision-limited analog devices to accelerate floating-point neural network training. We demonstrate the capabilities of this system by solving the one-dimensional Burgers' equation and modeling the three-dimensional thermal conduction phenomenon. An expected nearly 116 times to 21 times increase in computational energy efficiency was achieved, with solution precision comparable to those of digital processors. Our results extend in-memristor computing applicability beyond edge neural networks and facilitate construction of future AI-for-Science computing platforms.

Authors

  • Jiancong Li
    School of Integrated Circuits, Hubei Key Laboratory for Advanced Memories, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Jing Tian
    School of Biological Engineering, Dalian Polytechnic University No. 1st Qinggongyuan, Ganjingzi Dalian 116034 P. R. China liqian19820903@163.com +86-411-86323725 +86-411-86323725.
  • Yudeng Lin
    School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.
  • Zhiwei Zhou
    Interdisciplinary Research Center on Biology and Chemistry, and Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China.
  • Yi Li
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Bin Gao
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: gaob1@tsinghua.edu.cn.
  • Jianshi Tang
    Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China. jtang@tsinghua.edu.cn.
  • Jia Chen
    Department of Oncology Internal Medicine, Nantong Tumor Hospital, Affiliated Tumor Hospital of Nantong University, Nantong, China.
  • Yuhui He
  • He Qian
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: qianh@tsinghua.edu.cn.
  • Huaqiang Wu
    Institue of Microelectronics, Tsinghua University, Beijing, 100084, China.
  • Xiangshui Miao

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