Eco-efficient deployment of spiking neural networks on low-cost edge hardware
This letter presents a practical and energy-aware framework for deploying Spiking Neural Networks on low-cost hardware for edge computing on existing software and hardware components. We detail a reproducible pipeline that integrates neuromorphic processing with secure remote access and distributed...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/153992 |
| Acceso en línea: | https://hdl.handle.net/10609/153992 https://doi.org/10.1109/LNET.2025.3611426 |
| Access Level: | acceso abierto |
| Palabra clave: | edge processing autonomous driving SSH V2X MQTT federated learning neuromorphic computing spiking neural networks |
| Sumario: | This letter presents a practical and energy-aware framework for deploying Spiking Neural Networks on low-cost hardware for edge computing on existing software and hardware components. We detail a reproducible pipeline that integrates neuromorphic processing with secure remote access and distributed intelligence. Using Raspberry Pi and the BrainChip Akida PCIe accelerator, we demonstrate a lightweight deployment process including model training, quantization, and conversion. Our experiments validate the eco-efficiency and networking potential of neuromorphic AI systems, providing key insights for sustainable distributed intelligence. This letter offers a blueprint for scalable and secure neuromorphic deployments across edge networks, highlighting the novelty of providing a reproducible integration pipeline that brings together existing components into a practical, energy-efficient framework for real-world use. |
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