Materials, Physics, and Chemistry of Neuromorphic Computing Systems
[EN] This paper frames the rise of neuromorphic computing as a response to the energy and speed limitations of traditional von Neumann architectures in the context of rapidly growing data demands in artificial intelligence. Inspired by the brain¿s ability to perform parallel and energy-efficient pro...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/232004 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/232004 |
| Access Level: | acceso abierto |
| Palabra clave: | Centrar Nerovous System Circuits Computational Chemistry Materials Neural Networks |
| Sumario: | [EN] This paper frames the rise of neuromorphic computing as a response to the energy and speed limitations of traditional von Neumann architectures in the context of rapidly growing data demands in artificial intelligence. Inspired by the brain¿s ability to perform parallel and energy-efficient processing, neuromorphic systems aim to merge memory and computation, enabling fast learning and low-power operation, with particular emphasis on spiking neural networks and in-sensor or edge computing. The article introduces the special issue Materials, Physics and Chemistry of Neuromorphic Computing Systems, highlighting how the physical chemistry and materials science of devices at the micro- and nanoscale can be engineered to reproduce neural functionalities such as synaptic plasticity, nonlinear dynamics, and network-level learning. The issue covers a broad range of material platforms¿including halide perovskites, organic materials, metal oxides, fluidic systems, and ferroelectrics¿and addresses memristive and transistor-based devices, optoelectronic approaches, and network implementations, illustrating both the challenges and the vast potential of neuromorphic materials and devices for future information-processing technologies. |
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