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.
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.
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.
- IRS SPINBRAIN (2020-2022)
- ANR Spinspike (2020-2024)
- HANS, UGA (2019-2022)
- CEA LIST
- CEA LETI
- Masters thesis projects for Spring 2022 (September 17th, 2021)
You find here the list of proposals for Master-2 internships to take place at Spintec during Spring 2022. In most cases, these internships are intended to be suitable for a longer-term PhD work. Interested Master-1 ...
- Post-doctoral positions – Coupled nano-oscillator arrays for brain inspired computing (August 04th, 2021)
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 ...
- 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 ...