Micromagnetic realization of energy-based models using stochastic magnetic tunnel junctions
Source abstract
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.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Magnetic properties of thin films
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (56)
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