Published Paper Details:

Abstract - The goal of this study is to close the gap between theory and real-world application by focusing on neuromorphic computing architectures for artificial intelligence (Al). This study underlines the critical role of AI in influencing the future of computational architecture for AI systems by evaluating AI integration, optimizing computational systems, and investigating real-world applications. Artificial intelligence (AI) has seen remarkable progress in recent years, with the rapid advancement of neural networks and machine learning algorithms. However, the computational demands of AI tasks often exceed the capabilities of conventional digital computing architectures. Neuromorphic computing, inspired by the human brain's architecture and functioning, has emerged as a promising alternative to bridge this computational gap. This research paper presents a comprehensive review and analysis of neuromorphic computing architectures for AI applications. The paper begins by providing an overview of the fundamental principles of neuromorphic computing, highlighting its biological inspiration, such as spiking neural networks, and its efficient utilization of analog and event-based processing. The advantages and limitations of neuromorphic systems are discussed in comparison to traditional von Neumann computing architectures, emphasizing their potential for low-power, real-time, and brain-like computation. The paper delves into case studies and practical applications of neuromorphic computing in AI, including computer vision, natural language processing, robotics, and edge computing. It explores how neuromorphic hardware accelerators, such as IBM's TrueNorth and Intel's Loihi, have demonstrated significant gains in energy efficiency and performance for AI tasks. Moreover, the research paper examines the challenges and future prospects of neuromorphic computing, including scalability, programming models, and integration with existing AI frameworks. The potential impact of neuromorphic architectures on the development of intelligent systems is highlighted, emphasizing their potential to unlock new frontiers in AI research and applications. In conclusion, this paper underscores the growing significance of neuromorphic computing architectures in the field of artificial intelligence. It highlights the promising results achieved by combining brain-inspired hardware with AI algorithms and emphasizes the need for further research and development to harness the full potential of neuromorphic computing in enabling more efficient, versatile, and brain-like artificial intelligence systems.