NEUROMORPHIC COMPUTING: ENGINEERING ARTIFICIAL NEURAL NETWORKS FOR REAL-WORLD APPLICATIONS

Abstract

Neuromorphic computing is an emerging paradigm inspired by the structure and functionality of the human brain, aiming to develop hardware and software systems that mimic biological neural processes. This interdisciplinary field merges neuroscience, computer engineering, and artificial intelligence to create highly efficient, low-power, and adaptive systems. Unlike traditional computing architectures, neuromorphic systems leverage spiking neural networks (SNNs), event-driven processing, and parallelism to perform complex computations more naturally and efficiently. This paper explores the engineering principles behind neuromorphic architectures, the design of artificial neural networks within these systems, and their real-world applications in areas such as robotics, sensory processing, autonomous systems, and edge AI. We also discuss the challenges in hardware development, algorithm design, and scalability, while highlighting recent advancements and future directions. Neuromorphic computing holds significant promise in transforming the future of intelligent computing by offering brain-inspired solutions for energy-efficient and real-time processing in dynamic environments.