Neuromorphic Computing: Cutting-Edge Advances and Future Directions

Neuromorphic computing draws motivation from the human brain and presents a distinctive substitute for the traditional von Neumann architecture. Neuromorphic systems provide simultaneous data analysis, energy efficiency, and error resistance by simulating neural networks. They promote innovations in eHealth, science, education, transportation, smart city planning, and the metaverse, spurred on by deep learning and artificial intelligence. However, performance-focused thinking frequently ignores sustainability, emphasizing the need for harmony. Three primary domains comprise neuromorphic research: neuromorphic computing, which investigates biologically inspired data processing and alternative algorithms; neuromorphic devices, which utilize electronic and photonic advancements to fabricate novel nano-devices; and neuromorphic engineering, which replicates brain mechanisms using CMOS and post-CMOS technological advances. This chapter will discuss the current state of computing, the neuromorphic computing approach, established and upcoming technologies, material challenges, breakthrough computing concepts, and the advanced stage of emerging technologies. Along with software and algorithmic advancements in spike neural networks (SNNs) and neuromorphic learning algorithms, it will cover hardware improvements, such as memristors, synaptic devices, and neuromorphic processors. We will investigate applications in robotics, autonomous systems, edge computing, the Internet of Things (IoT), and sensory systems. In conclusion, the chapter will discuss future challenges and possibilities, emphasizing major findings and new research directions.