Abstract
Energy-based models (EBMs) can bridge physics, machine learning, and statistics. EBMs provide a unified and powerful platform to describe, learn, and optimize complex systems. In this paper, we propose a neuromorphic implementation of EBMs using a network of stochastic magnetic tunnel junctions (MTJs) that can perform energy minimization and solve optimization problems. Our implementation builds on the Object Oriented MicroMagnetic Framework (OOMMF). We derive the different energy terms and map them to the micromagnetic Landau-Lifshitz-Gilbert (LLG) equation. We then develop a C + + module for EBMs which integrates seamlessly with OOMMF. We demonstrate our implementation on a full set of logic gates using stochastic MTJs networks. Our method offers several advantages, including fast modeling of EBMs with spintronic devices and design insights for stochastic MTJ-based neuromorphic circuits.
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Acknowledgements
This work is supported by Agency for Science, Technology and Research (A*STAR) under Career Development Fund (Project No. C210812054).
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BC supervised the study, analyzed the data, performed part of simulations, and wrote the manuscript. YH wrote C + + codes and incorporated them into OOMMF software packages to realize the energy-based models. CKG analyzed the data, reviewed, and edited the manuscript. MZ supervised the study, performed part of simulations, and reviewed the manuscript.
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Chen, B., Hou, Y., Gan, C.K. et al. Micromagnetic realization of energy-based models using stochastic magnetic tunnel junctions. Appl. Phys. A 129, 655 (2023). https://doi.org/10.1007/s00339-023-06931-4
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DOI: https://doi.org/10.1007/s00339-023-06931-4