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 artificial intelligence (AI) and unconventional computing.

Spintronic-based multifunctional devices are a substantial opportunity to improve the energy efficiency of next-generation computing hardware. Moreover, this approach allows taking advantage of brain-inspired computing models to deploy cutting-edge neuromorphic algorithms, crossing the gap between current hardware AI implementations and exceptional brain computing ability.

Research topics

Bio-inspired computing

As the brain performs very sophisticated operations and consumes only a few Watts, brain-inspired/neuromorphic computing is a promising path for which spintronic devices can efficiently emulate both neurons and synapses in hardware. Their nanometric size, sensitivity to input stimuli, and interactions make those devices ideal for implementing large arrays of neuro-synaptic elements: spintronic nano-oscillators, spintronic and ferroelectric memristors, magnetic memories, superparamagnetic tunnel junctions, skyrmions, etc.

Probabilistic computing

Noise is a crucial ingredient in emulating the stochastic nature of the neural activity and executing energy-efficient computing algorithms such as energy-based or temporal-based machine learning models. In this context, probabilistic computing is a very suitable approach that relaxes usual precision computing constraints. The truly random nature of spintronic devices (such as superparamagnetic tunnel junctions) makes them attractive for hardware implementations of probabilistic computing approaches.

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




Recent news

  • Post-doctoral position – Spintronic Spiking Neurons (December 07th, 2020)Post-doctoral position – Spintronic Spiking Neurons
    In the frame of the French national ANR project SpinSpike, Spintec laboratory is opening a postdoctoral researcher position. The candidate will work in collaboration with UMPhy CNRS-Thales and Thales TRT. The objective is the realization ...
  • Masters thesis projects for Spring 2021 (September 15th, 2020)Masters thesis projects for Spring 2021
    You find here the list of proposals for Master-2 internships to take place at Spintec during Spring 2021. In most cases, these internships are intended to be suitable for a longer-term PhD work. Interested Master-1 ...
  • Recruiting a Research Scientist on spintronic neuromorphic computing (June 11th, 2020)Recruiting a Research Scientist on  spintronic neuromorphic computing
    Spintec Positioned at the crossroad of science and technology, SPINTEC (SPINtronique et TEchnologie des Composants, is one of the leading spintronics research laboratories worldwide. SPINTEC was created in 2002 and rapidly expanded to currently reach ...
  • Lorena ANGHEL joins SPINTEC (June 05th, 2020)Lorena ANGHEL joins SPINTEC
    We are pleased to announce the arrival on June 1st, 2020 at SPINTEC, within the Spintronics IC design team of Lorena ANGHEL, professor at Grenoble INP / PHELMA, currently deputy director in charge of Research ...
  • Seminar – Reservoir Computing with Random Magnetic Textures (November 13th, 2019)Seminar - Reservoir Computing with Random Magnetic Textures
    On November 20th at 11:00, we have the pleasure to welcome Daniele Pinna from Johannes Gutenberg University. He will give us a seminar at CEA/SPINTEC (*) Bat 1005, room 445, entitled : Reservoir Computing with Random ...


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