Projektbeschreibung
Fortschrittliche Technologie für effiziente vom Gehirn inspirierte Informatik
Neuromorphes Rechnen ist ein Zweig der Informatik und des Ingenieurwesens, der sich von der Architektur und der Funktionsweise des menschlichen Gehirns inspirieren lässt, um effizientere und gehirnähnliche Rechensysteme zu entwickeln und zu bauen. Memristor-Bauelemente sind in der Lage, Synapsen in biologischen Systemen zu emulieren und haben daher das Interesse an ihrer Verwendung für neuromorphes Rechnen geweckt. Das vom Europäischen Forschungsrat finanzierte Projekt RobustNanoNet zielt darauf ab, Leistungsprobleme von Memristoren durch die Verbesserung der Widerstandsschaltertechnologie zu lösen. Die Forschenden werden Geräte mit neuen Materialien entwickeln, ihre Leistung testen und ihre Verwendung in fortgeschrittenen Rechensystemen für maschinelles Lernen untersuchen. Ziel ist es, eine verlässliche Technologie für effiziente und zuverlässige vom Gehirn inspirierte Informatik zu schaffen.
Ziel
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.
Wissenschaftliches Gebiet
- natural scienceschemical sciencesinorganic chemistryinorganic compounds
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencesphysical sciencesopticslaser physics
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thema/Themen
Finanzierungsplan
HORIZON-ERC - HORIZON ERC GrantsGastgebende Einrichtung
077190 Voluntari
Rumänien