Neuromorphic Computing: The Next Frontier in AI and Cognitive Computing

Abstract

Neuromorphic computing mimics the structure and functionality of the human brain to create highly efficient, low-power AI systems. This paper explores the principles behind neuromorphic chips, their advantages over traditional computing architectures, and real-world applications in robotics, healthcare, and edge AI. We analyze the challenges of large-scale adoption, including hardware development, algorithm design, and integration with existing AI models. Our research concludes that neuromorphic computing represents a significant leap toward creating intelligent, energy-efficient systems capable of real-time learning and adaptation.