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Fernando Sevilla Martínez
e-Health Center, Universitat Oberta de Catalunya UOC, Barcelona, Spain
Volkswagen AG, Wolfsburg, Germany
Jordi Casas-Roma
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain
Laia Subirats
e-Health Center, Universitat Oberta de Catalunya UOC, Barcelona, Spain
Raúl Parada
Centre Tecnològic de Telecomunicacions de Catalunya CTTC/CERCA, Barcelona, Spain
Eco-Efficient Deployment of Spiking Neural Networks on Low-Cost Edge Hardware
Fernando Sevilla Martínez, Jordi Casas-Roma, Laia Subirats, Raúl Parada
IEEE Networking Letters, 2025
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.
View at ieeexplore.ieee.org
Fernando Sevilla Martínez
e-Health Center, Universitat Oberta de Catalunya UOC, Barcelona, Spain
Volkswagen AG, Wolfsburg, Germany
Jordi Casas-Roma
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain
Laia Subirats
e-Health Center, Universitat Oberta de Catalunya UOC, Barcelona, Spain
Raúl Parada
Centre Tecnològic de Telecomunicacions de Catalunya CTTC/CERCA, Barcelona, Spain
Eco-Efficient Deployment of Spiking Neural Networks on Low-Cost Edge Hardware
Fernando Sevilla Martínez, Jordi Casas-Roma, Laia Subirats, Raúl Parada
IEEE Networking Letters, 2025
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.
View at ieeexplore.ieee.org