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 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
Projects
Partners
- CEA LIST
- UMPHY
- CEA LETI
Recent news
- [POSITION FILLED] Post-doctoral positions – Coupled nano-oscillator arrays for brain inspired computing (August 04th, 2021)
![[POSITION FILLED] Post-doctoral positions - Coupled nano-oscillator arrays for brain inspired computing](https://www.spintec.fr/wp-content/uploads/2021/08/2021-08-ai-post-docs-100x70.png)
SPINTEC laboratory (Grenoble, France), in collaboration with CEA-LETI, has currently two postdoc position openings to work on theory (12 month) and on experiment (18 month) of coupled oscillator arrays for implementing neuromorphic and unconventional computing ... - NeuSPIN – An ANR project (June 24th, 2021)
NeuSPIN stands for Design of a reliable edge neuromorphic system based on spintronics for Green AI. Current computing architectures with separate processing and memory blocks are not ideal for energy-efficient learning and inference processing for ... - SpinSpike – An ANR project (January 19th, 2021)
SpinSpike stands for Spintronic Spiking Neurons. It is a 42-month-long ANR project (2021/2024). Spintronics has recently shown its promise for neuromorphic computing, but is lacking an essential ingredient of biological neural networks: spiking neurons. In this ... - Philippe Talatchian joins SPINTEC (December 17th, 2020)
Philippe Talatchian arrives at SPINTEC as CEA research engineer on December 10, 2020 to strengthen the artificial intelligence team in the design and experimental realization of neuromorphic spintronic devices. Philippe is bringing to Spintec an ... - Post-doctoral position – Spintronic Spiking Neurons (December 07th, 2020)
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 ...
