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Hybrid, functional and polymeric materials for various applications (biomedical, cultural heritage, sustainability)
SMaterial-related problems of neuromorphic technologies
Advancements in materials science have been instrumental in the progression of neuromorphic technologies. However, existing neuromorphic hardware often relies on rigid and energy-intensive fabrication processes. There is an urgent need for innovative materials and device architectures that can bridge this gap while ensuring scalability and eco-friendliness.
Scope:
The inevitability of a revolution in electronic technologies is dictated by the emergence of new paradigms of electronics which cannot be implemented on existing hardware. Traditional computing architectures struggle with energy efficiency, scalability, and adaptability, particularly as AI and data-intensive applications demand greater computational power. In the past decades, electronics and telecommunications have become a new global energy consumer. Machine learning tends, in the limit, to consume all the power produced in the world, and this development model is costly, inefficient and unsustainable. Neuromorphic computing, inspired by biological neural networks, presents a promising alternative by enabling real-time, low-power processing. Crucial for this development are materials being able to implement bio-inspired functions. However, existing neuromorphic hardware often relies on rigid and energy-intensive fabrication processes, limiting material flexibility, integration potential, and environmental sustainability. There is an urgent need for innovative materials and device architectures that can bridge this gap while ensuring scalability and eco-friendliness.
Thus, the symposium is dedicated to the material problems of neuromorphic technologies and aims to bring together a wide range of theorists and experimentalists, software and hardware developers to exchange ideas and advances in this field.
Hot topics to be covered by the symposium:
- Theory and modelling
- Novel neuromorphic materials, cells and arrays
- Neuromorphic devices based on 0D, 1D and 2D materials
- Memristors for neuromorphic computing
- Resistive switching mechanisms
- Experimental techniques
- Manufacture technologies
- Electrical conduction
- Ion transport
- Neural networks and bio-interfaces
- Quantum neuromorphic systems
- Memcomputing
- Physical reservoirs for reservoir computing
- Applications of AI to neuromorphic materials research
- Optoelectronic memristive hardware
- Reliability of neuromorphic devices
- Multifunctional devices
- Defects in neuromorphic materials and devices
- Brain-inspired metamaterials
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4555 Overlook Ave. SW, Washington DC, 20375, USA
hans.s.cho.civ@us.navy.milAv. Dos Estados, 5001, 09280-560, Santo André, São Paulo, Brazil
m.sparvoli@ufabc.edu.brDepartamento de Física and I3N, Campus de Santiago, 3810-193 Aveiro, Portugal
sobolev@ua.ptDresden 01069, Germany
ronald.tetzlaff@tu-dresden.de