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 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.