Integration of Neuromorphic Computing in Embedded Systems: Opportunities and Challenges

Abstract

The integration of neuromorphic computing into embedded systems presents a promising avenue for advancing artificial intelligence (AI) at the edge. Neuromorphic computing, inspired by the human brain's architecture, offers opportunities for energy-efficient, real-time processing of sensory data and cognitive tasks. This paper explores the principles and advantages of neuromorphic computing and discusses the challenges and opportunities associated with its integration into embedded systems. We highlight the potential applications of neuromorphic computing in areas such as robotics, Internet of Things (IoT), and autonomous vehicles, while also addressing the technical, architectural, and algorithmic challenges that must be overcome. By examining current trends and future directions, this paper provides insights into the transformative potential of neuromorphic computing in embedded systems.