Neuromorphic Computing for IoT: Ultra-Low Power AI for Real-Time Intelligence

Author(s)	Subhasis Kundu
Country	United States
Abstract	Neuromorphic computing presents a promising approach for enabling artificial intelligence capabilities in Internet of Things (IoT) devices with stringent power constraints. This paper explores the potential of neuromorphic architectures to deliver ultra-low power AI for real-time intelligence in IoT applications. We examine the fundamental principles of neuromorphic computing and its advantages for event-driven processing in IoT environments. The paper discusses key neuromorphic hardware platforms and algorithms optimized for IoT deployment. We analyze the energy efficiency and performance benefits of neuromorphic systems compared to conventional computing approaches. Several case studies demonstrate neuromorphic IoT applications in areas like smart sensors, autonomous vehicles, and intelligent infrastructure. Finally, we outline research challenges and future directions for advancing neuromorphic computing to revolutionize IoT intelligence.
Keywords	Neuromorphic Computing, Internet of Things (IoT), Artificial Intelligence (AI), Event-driven Processing, Ultra-low Power, Spiking Neural Networks (SNNs), Real-time Intelligence, Edge Computing, Smart Sensors, Autonomous Vehicles
Field	Engineering
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-11-08
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.40671
Short DOI	https://doi.org/g9bthf

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International Journal For Multidisciplinary Research