Spintronic Devices for Thermodynamically Guided Analog Computing
Inspired by statistical physics, Ising machines promise to efficiently address complex optimization problems by leveraging topologies that enable local information processing. At the algorithmic level, they emulate Boltzmann probability laws to explore the state space and tend toward optimal configurations. Guided by thermodynamic principles, they minimize collective energy while preserving the entropy-driven ability to move to neighboring states at a given temperature. Among various approaches to implementing Ising machines, analog accelerators built from asynchronous networks of nanodevices or small circuits most closely resemble the microscopic model of directly interacting spins, operating without explicit update instructions or temporary storage of global cost functions or gradients. Magnetic tunnel junctions (MTJs) offer adjustable, tunable stability and rapid switching between binary states, making them promising building blocks for energy-efficient cognitive computing architectures [1]. In this work, we investigate stochastic MTJs (sMTJs) for level-encoded approaches and Spin-Torque Oscillators (STOs) for phase-encoded computing (Fig. 1).
Authors
- Kamel-Eddine Harabi (ORCID: https://orcid.org/0000-0002-8321-0286)
- Lucian Prejbeanu (ORCID: https://orcid.org/0000-0001-6577-032X)
- Kévin Garello (ORCID: https://orcid.org/0000-0003-0236-322X)
- Franck Badets (ORCID: https://orcid.org/0000-0002-8331-4042)
- Ricardo C. Sousa (ORCID: https://orcid.org/0000-0001-8903-3359)
- Nhat-Tan Phan (ORCID: https://orcid.org/0000-0002-2806-1660)
- F. Disdier
- Lorena Anghel (ORCID: https://orcid.org/0000-0001-9569-0072)
- Damien Querlioz (ORCID: https://orcid.org/0000-0002-0295-1008)
- Louis Hutin (ORCID: https://orcid.org/0000-0001-6429-3867)
- John A. Peters (ORCID: https://orcid.org/0000-0002-4277-4245)
- F. Andrieu (ORCID: https://orcid.org/0000-0002-1951-1033)
- Abderrazak Hakam (ORCID: https://orcid.org/0009-0006-6555-8840)
- A. Bazzi
- B. Viala (ORCID: https://orcid.org/0000-0001-6087-7670)
- T. Dalgaty
- Guillaume Prenat (ORCID: https://orcid.org/0000-0003-4899-2101)
- B. Delaët
- Kamal Danouchi (ORCID: https://orcid.org/0000-0002-8501-9928)
- C. Bouchard
- P. Talatchian
- L. Soumah
- U. Ebels
- M. A. Iftakher
- E. Yildiz
- Hugo Levices
Institutions
- Centre National de la Recherche Scientifique (FR)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris-Saclay (FR)
- CEA Grenoble (FR)
- Laboratoire d'Intégration des Systèmes et des Technologies (FR)
- Institut Laue-Langevin (FR)
- Centre Inria de Saclay (FR)
- Spintronique et Technologie des Composants (FR)
- Laboratoire d'Électronique des Technologies de l'Information (FR)
- Université Grenoble Alpes (FR)
Publication Details
- Published
- 2025-06-22
- DOI
- https://doi.org/10.1109/drc66027.2025.11105749
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- HORIZON EUROPE Framework Programme