Spiking Neural Networks for Energy-Efficient Edge Intelligence

Abstract: Spiking Neural Networks (SNNs) represent a promising frontier in neuromorphic computing, enabling low-power, high-efficiency computations for edge intelligence. This paper investigates the application of SNNs in real-time edge scenarios such as sensor fusion, event-based vision, and speech recognition. By comparing SNNs to conventional deep neural networks (DNNs), we demonstrate significant reductions in energy consumption and latency while maintaining competitive accuracy. We employ simulation frameworks like PyTorch and TensorFlow, model interpretability tools like SHAP and LIME, and perform regression and predictive analyses to assess performance. Results indicate that SNN-based models achieve up to 65% lower energy consumption compared to DNNs on edge devices while delivering acceptable performance trade-offs. Keywords: Spiking Neural Networks, SNNs, Edge Intelligence, Neuromorphic Computing, Energy Efficiency, TensorFlow, PyTorch, SHAP, LIME