Neuromorphic computing for energy-efficient machine intelligence

Abstract Neuromorphic computing has gained a significant amount of attention from industry as well as the research community as a means of overcoming the growing energy demands of machine intelligence. Neuromorphic systems offer promising computation approaches by mimicking the functioning of the human brain’s energy-efficient neural processing. This perspective explores the significance of energy efficiency in artificial intelligence (AI) systems, examines the potential issues associated with traditional AI architectures, and highlights the importance of neuromorphic computing as a sustainable solution. It also provides an overview of the working principle of neuromorphic systems, a spiking neural network (SNN) implementation, and the comparative advantages over conventional computing architectures. This perspective further investigates several real-world applications to assess the potential of neuromorphic computing to perform real-time and energy-efficient operations. Although they offer significant advantages, neuromorphic systems face multiple challenges regarding materials development, fabrication techniques, large-scale deployments, and a lack of standard software tools. Finally, this perspective outlines potential research directions to explore the role of neuromorphic computing in next-generation AI systems.