Neuromorphic Computing: Architectures and Applications for Intelligent Systems

Neuromorphic computing offers a transformative paradigm for intelligent system design by emulating the structure and dynamics of biological neural networks. This lecture examines the architectural foundations of neuromorphic systems, including spiking neural networks, event-driven computation, and ultra-low-power hardware mechanisms. Through examples from robotics, sensory processing, and edge AI, we explore how neuromorphic platforms enable adaptive, low-latency intelligence in real-world environments. The talk highlights recent advances in neuromorphic chip architectures, integration strategies for embedded and multicore systems, and emerging applications in autonomous systems and intelligent infrastructure. Attendees will gain a clear understanding of neuromorphic design principles, performance considerations, deployment challenges, and the broader potential of neuromorphic architectures to reshape future computing models.