Descrizione del progetto
Tecnologia avanzata per un’elaborazione efficiente ispirata al cervello
L’informatica neuromorfica è una branca dell’informatica e dell’ingegneria che si ispira all’architettura e al funzionamento del cervello umano per progettare e costruire sistemi informatici più efficienti e simili al nostro organo cerebrale. Grazie alla capacità di emulare le sinapsi nei sistemi biologici, i memristori hanno suscitato interesse per potenziali applicazioni nell’informatica neuromorfica. Il progetto RobustNanoNet, finanziato dal Consiglio europeo della ricerca, si propone di risolvere i problemi di prestazioni dei memristori migliorando la tecnologia di commutazione resistiva. Il gruppo di ricerca svilupperà dispositivi con nuovi materiali, ne collauderà le prestazioni e ne indagherà l’uso in sistemi informatici avanzati per l’apprendimento automatico. L’obiettivo è creare una tecnologia affidabile per un’elaborazione efficiente e affidabile ispirata al cervello.
Obiettivo
Resistive switching refers to the controlled change in resistance of an electronic material, e.g. metal oxide, via the creation and modulation of nanoscale filaments. Although its physics is not yet fully understood, resistive switching devices (called memristors) are promising as efficient artificial synapses in neuro-inspired computing systems. However practical challenges exist. Current devices excel in only a few of the performance metrics necessary for circuit and system integration. Moreover, they exhibit non-idealities causing neuromorphic systems using these devices to have low performance. The project will address this key issue by pursuing device-system co-optimization across four objectives, aiming to engineer a single “hero” resistive switching technology with all the desired metrics. Aim 1 will develop resistive switching devices based on a new class of materials with broad compositional space, called high entropy oxides. Promising compositions will be fabricated in a high throughput fashion. In Aim 2, a proposed characterization method via a state-of-the-art mid-infrared laser will help understand in-operando the filamentary switching at nanoscale and uncover the physical mechanisms behind its non-idealities. The fabrication and characterization will iteratively target a broad range of performance metrics. Some metrics can only be quantified across a population of devices, so Aim 3 will integrate the optimized devices on transistor circuitry for benchmarking at scale. Aim 4 targets the applicability of these devices to next generation neuromorphic systems for machine learning training. Preliminary work on a multi-layer neural network validated this concept and indicated the need for co-optimization, as proposed. RobustNanoNet will address the interdisciplinary challenges towards a reliable resistive switching technology to support robust neuromorphic systems for energy efficient computing.
Campo scientifico
- natural scienceschemical sciencesinorganic chemistryinorganic compounds
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencesphysical sciencesopticslaser physics
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
077190 Voluntari
Romania