NEUROMORPHIC COMPUTING FOR NEXT-GENERATION ARTIFICIAL INTELLIGENCE: ARCHITECTURES, APPLICATIONS, AND FUTURE RESEARCH DIRECTIONS

The growing computational demands of modern Artificial Intelligence (AI) have exposed the limitations of conventional von Neumann computing architectures in terms of energy efficiency, latency, and scalability. Neuromorphic computing has emerged as a revolutionary computing paradigm inspired by the structure and functioning of the human brain. By integrating specialized hardware with brain-inspired neural processing models, neuromorphic systems aim to perform intelligent computation with significantly lower power consumption and faster response times. This paper provides a comprehensive review of neuromorphic computing, including its architecture, fundamental principles, enabling technologies, real-world applications, implementation challenges, and future research opportunities. The study also discusses the integration of neuromorphic processors with edge computing, robotics, the Internet of Things (IoT), and autonomous systems. The findings indicate that neuromorphic computing has the potential to transform intelligent computing by enabling energy-efficient, adaptive, and real-time AI systems.