Advancements in Neuro-Inspired and Neuromorphic Computing Architectures for Future Intelligent Systems: Design Principles, Challenges, and Emerging Applications

Abstract

Authors: Dr. Priyanka R. Deshmukh, Mr. Karthik S. Nair

ABSTRACT: The continuous expansion of artificial intelligence (AI) and machine learning (ML) has driven the demand for highly efficient and biologically inspired computational systems. Traditional von Neumann architectures face limitations in power efficiency and scalability when simulating complex neural processes. Neuro-inspired and neuromorphic computing architectures emulate the structure and function of the human brain to enable energy-efficient, adaptive, and parallel information processing. This paper provides a comprehensive overview of the principles, design paradigms, and applications of neuromorphic systems. It explores the integration of spiking neural networks (SNNs), memristor-based synaptic devices, and brain-inspired learning models. Furthermore, it discusses current challenges, opportunities, and future research directions in developing scalable, low-power, and robust neuromorphic architectures capable of supporting next-generation intelligent systems.

KEYWORDS: Neuro-inspired computing, Neuromorphic architectures, Spiking neural networks, Memristors, Brain-inspired AI, Edge intelligence, Cognitive computing.