Bio–Inspired Neuromorphic Computing for Energy–Efficient Edge AI: A Scalable Framework Integrating Spiking Neural Networks and Memristor Based Architectures

Neuromorphic computing, inspired by the biological brain’s efficiency in processing information, has emerged as a revolutionary paradigm for next-generation edge artificial intelligence (AI). This research presents a comprehensive bio-inspired neuromorphic framework that leverages spiking neural networks (SNNs), memristor-based synaptic architectures, and event-driven processing to achieve unprecedented energy efficiency and real-time adaptability in edge computing environments. The proposed system introduces a dynamic spike encoding mechanism that optimizes neuronal activation based on input relevance, coupled with adaptive synaptic pruning to minimize redundant computations. Additionally, the integration of spike-timing-dependent plasticity (STDP) enables continuous self-learning, making the system highly effective for dynamic edge applications such as autonomous navigation, real-time healthcare monitoring, and industrial IoT anomaly detection. Experimental validation demonstrates that the proposed neuromorphic framework achieves a 60% reduction in power consumption, a 3× improvement in processing speed, and a 45% enhancement in model adaptability compared to conventional deep learning models deployed on edge platforms. Hardware efficiency is further amplified through memristor-based in-memory computing, eliminating the energy overhead associated with von Neumann architectures. Real-world evaluations across multiple edge scenarios—including neuromorphic vision processing with event-based cameras—confirm significant improvements in latency, robustness, and scalability. The findings underscore the transformative potential of neuromorphic computing in enabling sustainable, low-power AI for next-generation edge devices. Future research directions include hybrid neuromorphic-deep learning integration and quantum-inspired architectures to further enhance performance and scalability in ultra-low-power edge AI applications.