The transversal team aims at bringing together all competencies from SPINTEC involving spintronic devices nanofabrication, characterization, circuit integration, architecture and algorithm techniques to implement hardware solutions for AI and unconventional computing applications that are hard to be with current CMOS technologies.

Spintronic based multifunctional devices are also a great opportunity to improve the energy efficiency. Moreover, the high speed and endurance magnetic devices allow more sophisticated learning algorithms implementations such as unsupervised learning, reinforcement learning, etc, crossing the gap between current edge AI implementations and the real brain computing ability.

Research topics

Bio-inspired computing

As the brain performs very sophisticated operations and consumes only few Watts, brain-inspired/neuromorphic computing is very promising path for which spintronic devices can efficiently emulate both neurons and synapses in hardware. Their nanometric size, sensitivity to input stimuli, and interactions makes those devices ideal to implement large arrays of neuro-synaptic elements. In addition, these types of architectures are very well suited to new learning rules and adaptation.  Performant and highly energy efficient, analog, voltage or current-tunable spintronic-based memristors can emulate neurons and massive dynamic connections required to solve complex, non-linear operations: Spintronic nanoscillators, spintronic and ferroelectric memristors, magnetic memories, super paramagnetic and magnetic tunnel junctions, skyrmions, etc.

Probabilistic computing

Biologically inspired operations can take huge benefit from noise, which, in current nanotechnology is considered as an issue.  Indeed, noise can be a crucial ingredient to emulate the stochastic nature of neural activity and used for computing.  In this context, probabilistic computing is particularly suitable approach that relaxes usual precision constraints.  Stochastic circuits and architectures largely benefit from spintronic based device implementations aiming at performing new energy-based or temporal-based machine learning models. The truly random nature of spintronic devices (such as magnetic and super paramagnetic tunnel junctions) makes them attractive for hardware implementations of AI and unconventional computing.

In memory computing

The most promising solutions for non-Von Neumann, in-memory computing architectures are based on the use of emerging technologies, that are able to act as both storage and information processing units thanks to their specific physical properties. High accuracy, Deep neural networks (DNN) can be built with crossbars analog in memory computing concept, involving MRAM families, such as STT, SOT, VCMA, but also with more exotic families of magneto-resistive, and ferroelectric or skyrmion based devices.

The team


  • IRS SPINBRAIN (2020-2022)
  • ANR Spinspike (2020-2024)
  • HANS, UGA (2019-2022)



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