| Texto completo | |
| Autor(es): |
Simas, Arthur J.
;
Rothenberg, Christian Esteve
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2025 IEEE 11TH INTERNATIONAL CONFERENCE ON NETWORK SOFTWARIZATION, NETSOFT; v. N/A, p. 4-pg., 2025-01-01. |
| Resumo | |
Nature has perfected efficient ways to process information-like how our brains quickly recognize faces or learn new skills using minimal energy. Neuromorphic computing is a biologically inspired computing paradigm that mimics the brain's neural architecture to achieve high efficiency and adaptive learning. Current research focuses on scaling neuromorphic systems for energy-efficient AI acceleration, robotics, video recognition, alongside advances in Spiking Neural Networks (SNNs), in-memory computing architectures (e.g., memristors), and hardware-software co-design for applications like autonomous systems and healthcare diagnostics. While neuromorphic computing has shown promise in other domains, its role in softwarized networks remains underexplored. This paper presents the early stage of the doctoral research, outlining the required investigations to leverage neuromorphic computing in softwarized networks. We discuss the methodology, involved challenges, limitations, and potential impacts of this novel intersection. (AU) | |
| Processo FAPESP: | 21/00199-8 - Redes e serviços inteligentes rumo 2030 (SMARTNESS) |
| Beneficiário: | Christian Rodolfo Esteve Rothenberg |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada |