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

• Authors
• Authors and affiliations

ABSTRACT

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.

Significance of the Study:

This study presents a bio-inspired neuromorphic computing framework integrating spiking neural networks (SNNs) and memristor-based architectures to revolutionize energy-efficient edge AI. The system achieves 70% lower power consumption, 3× faster processing, and 45% better adaptability than conventional deep learning models. By leveraging event-driven processing and STDP-based learning, it enables real-time applications like autonomous navigation and IoT anomaly detection. The research highlights neuromorphic computing’s potential to overcome von Neumann bottlenecks, paving the way for ultra-low-power, scalable edge AI solutions in dynamic environments.

Summary of the Study:

The study introduces a neuromorphic framework combining SNNs and memristors for energy-efficient edge AI. It features dynamic spike encoding, synaptic pruning, and STDP learning, reducing power by 70% while improving speed and adaptability. Validated in real-world edge applications, it outperforms CNNs in latency and robustness. Memristor-based in-memory computing eliminates data transfer inefficiencies, enhancing scalability. Future work explores quantum-inspired architectures and hybrid neuromorphic-deep learning. This research advances sustainable, low-power AI for autonomous systems, IoT, and real-time edge intelligence.