• Pranav Murthy
• Aditya Mehra

Neuromorphic computing is a paradigm shift in processing architecture that is rooted in the neural structure of the human brain to develop computer systems that can imitate biological cognitive processes. This abstract relates neuromorphic computing to being a game-changing concept for application on edge database architectures in an effort to hit ultra-low latency in transaction processing. The neuromorphic system is explored in high-speed transactions with minimum delays to realize efficient and responsive edge computing environments. This research intends to use intrinsic parallelism and online learning capabilities of neuromorphic circuits in transcending the fundamental bounds of traditional von Neumann architectures, limited by sequential instruction and data processing. The application of neuromorphic computing in edge database architectures is certain to drive a tectonic shift in periphery-based data processing, hence real-time analytics and decision making in applications, from IoT devices to autonomous vehicles. This would then make it possible for developing more intelligent, efficient, autonomous edge computing systems, consequently making huge strides in distributed computing.

Neuromorphic, Neuromorphic Computing, Ultra-Low Latency Transaction Processing, Edge Database Architectures, Internet of Things (IoT), Artificial Intelligence, Robotics

"Exploring Neuromorphic Computing for Ultra-Low Latency Transaction Processing in Edge Database Architectures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 1, page no.25-33, January-2021, Available :http://www.jetir.org/papers/JETIR2101347.pdf

"Exploring Neuromorphic Computing for Ultra-Low Latency Transaction Processing in Edge Database Architectures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 1, page no. pp25-33, January-2021, Available at : http://www.jetir.org/papers/JETIR2101347.pdf