Exploring Neuromorphic Paradigms in Softwarized Networks: A Preliminary Study
Fuente:
Zenodo
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866901985857372160 |
|---|---|
| author | J Simas, Arthur Esteve Rothenberg, Christian |
| author_facet | J Simas, Arthur Esteve Rothenberg, Christian |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15576663 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Exploring Neuromorphic Paradigms in Softwarized Networks: A Preliminary Study J Simas, Arthur Esteve Rothenberg, Christian WP.NW4 Neuromorphic computing Spiking neural networks Softwarized networks Cognitive network management <p>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.</p> |
| title | Exploring Neuromorphic Paradigms in Softwarized Networks: A Preliminary Study |
| topic | WP.NW4 Neuromorphic computing Spiking neural networks Softwarized networks Cognitive network management |
| url | https://doi.org/10.5281/zenodo.15576663 |