Neuromorphic Computing Overview

The paper, "Neuromorphic Computing Overview," explores how brain-inspired computing models can overcome the limitations of traditional Von Neumann architectures, such as inefficiency and high energy consumption. Neuromorphic computing emulates biological neural networks using components like artificial neurons, synapses, spiking neural networks (SNNs), and memristors, enabling low-power, real-time, and scalable computing. It highlights applications in robotics, autonomous systems, and real-time data analysis through specialized hardware like IBM’s TrueNorth and Intel’s Loihi chips. Despite its advantages, challenges like scalability, training complexities, and interdisciplinary demands remain. The paper emphasizes the transformative potential of neuromorphic computing for future AI and computing technologies.