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.
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.
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.
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- Masters thesis projects for Spring 2019 (October 10th, 2018)
You find here the list of proposals for Master-2 internships to take place during Spring 2019. In most cases, these internships are intended to be suitable for a longer-term PhD work. Interested Master-1 students are ...
- Master students to visit SPINTEC and discuss our topics for internships (September 18th, 2016)
On 25th October 2016 our host Institut INAC welcomes students for a presentation of internship topics proposed to host Master-2 students during Spring 2017. Details will be provided later.