Descripción del proyecto
Computación neuromórfica aplicada al espacio recíproco de un solo elemento magnético
Las redes neuronales artificiales son sistemas informáticos inspirados en las redes neuronales biológicas. Emulan el encéfalo utilizando elementos no lineales que actúan como neuronas interconectadas a través de sinapsis artificiales. Las arquitecturas actuales no están exentas de escollos: la cantidad de sinapsis creadas es muy limitada en comparación con las decenas de miles que existen en el encéfalo humano. Además, cambiar la importancia de cada conexión requiere elementos de memoria adicionales. El proyecto k-NET, financiado con fondos europeos, despejará estos obstáculos. Propone una nueva arquitectura basada en la idea de que la hiperconectividad dinámica se puede implementar no en el espacio real, sino en el recíproco o en el espacio k. Para demostrar este método novedoso, los investigadores seleccionarán nanoestructuras ferromagnéticas en las que las poblaciones de ondas de espín, las excitaciones elementales, desempeñan el papel de las neuronas.
Objetivo
Artificial neural networks represent a key component of neuro-inspired computing for non-Boolean computational tasks. They emulate the brain by using nonlinear elements acting as neurons that are interconnected through artificial synapses. However, such physical implementations face two major challenges. First, interconnectivity is often constrained because of limits in lithography techniques and circuit architecture design; connections are limited to 100s, compared with 10000s in the human brain. Second, changing the weight of these individual interconnects dynamically requires additional memory elements attached to these links.
Here, we propose an innovative architecture to circumvent these issues. It is based on the idea that dynamical hyperconnectivity can be implemented not in real space but in reciprocal or k-space. To demonstrate this novel approach we have selected ferromagnetic nanostructures in which populations of spin waves – the elementary excitations – play the role of neurons. The key feature of magnetization dynamics is its strong nonlinearity, which, when coupled with external stimuli like applied fields and currents, translates into two useful features: (i) nonlinear interactions through exchange and dipole-dipole interactions couple potentially all spin wave modes together, thereby creating high connectivity; (ii) the strength of the coupling depends on the population of each k mode, thereby allowing for synaptic weights to be modified dynamically. The breakthrough concept here is that real-space interconnections are not necessary to achieve hyper-connectivity or reconfigurable synaptic weights.
The final goal is to provide a proof-of-concept of a k-space neural network based on interacting spin waves in low-loss materials such as yttrium iron garnet (YIG). The relevant spin wave eigenmodes are in the GHz range and can be accessed by microwave fields and spin-orbit torques to achieve k-space Neural computation with magnEtic exciTations.
Ámbito científico
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Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-FETOPEN-2018-2019-2020-01
Régimen de financiación
RIA - Research and Innovation actionCoordinador
75794 Paris
Francia