ARTIFICIAL INTELLIGENCE

Overview

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

Projects

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

Partners

  • CEA LIST
  • UMPHY
  • CEA LETI

Recent news

  • 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 ...
  • Seminar – Dynamics and oscillations in spintronic neural nets (October 03rd, 2019)Seminar - Dynamics and oscillations in spintronic neural nets
    On Thursday October 17 at 11:00 we have the pleasure to welcome Julie Grollier from Unité Mixte de Physique CNRS/Thales. She will give us a seminar at CEA/SPINTEC, Bat 1005, room 434A entitled : Dynamics and ...
  • Masters thesis projects for Spring 2020 (September 30th, 2019)Masters thesis projects for Spring 2020
    You find here the list of proposals for Master-2 internships to take place at Spintec during Spring 2020. In most cases, these internships are intended to be suitable for a longer-term PhD work. Interested Master-1 ...
  • Seminar – Brownian computing using skyrmions (June 13th, 2019)Seminar - Brownian computing using skyrmions
    On Friday July 12 at 11:00 we have the pleasure to welcome Prof. Yoshishige SUZUKI from Osaka University. He will give us a seminar at CEA/IRIG, Bat 1005, room 445 entitled : Brownian computing using skyrmions We ...

Publications

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