Leveraging Neuromorphic Computing for Efficient and Scalable Data Analytics

Neuromorphic computing is a new data analytical paradigm that mimics the behavior of biological neural systems to offer better computational power. State-of-the-art performance in conventional deep learning models (CNNs and transformers) comes at the expense of exorbitant energy and computation time. The real-time processing, low latency, and better energy efficiency make neuromorphic architectures to be a more appealing solution to kiosks where inferences on a large scale of data are being performed. In this paper, we discuss neuromorphic computing, how it can minimize data processing, its superiority compared to traditional AI models, and its future selection for diverse applications. Neuromorphic systems demonstrate a scalable way to the next-generation artificial intelligence by taking advantage of event-driven processing and dedicated hardware