Edge computing is the process of distributing data processing away from centralized core computing nodes to the edge nodes of the Internet, where data is collected and linked to the physical world. The rapid generation of data by these edge devices presents challenges related to scalability and energy consumption for traditional data centers. Achieving linear power scaling using existing CMOS technology becomes challenging, which has led to the exploration of alternative architectures such as neuromorphic computing for edge devices. Neuromorphic computing, inspired by the biological neuron principles underlying human brain processing, holds promise as a potential replacement for the traditional von Neumann computing paradigms. Neuromorphic computing is an emerging field that aims to design and develop computing systems and architectures inspired by the structure and function of the human brain. These systems leverage the principles of neural networks and parallel processing to perform complex cognitive task efficiently. Neuromorphic computing can significantly enhance Artificial Intelligence (AI) applications by enabling faster and more efficient processing of neural networks. The brain-inspired architecture of neuromorphic systems allows for parallel processing, low power consumption, and real time learning, making them well-suited for AI tasks like image and speech recognition, natural language processing, and autonomous decision-making. In the realm of emerging technologies, various platforms like SpiNNaker and BrainScaleS seek to facilitate high end brain-inspired computing. However, these platforms are not commercially accessible and do not align with the requirements of intelligent edge systems. Intel considers the Loihi processor as a potential choice for adoption in low-powered edge devices and even in server infrastructures within data centers.