This article explores the potential of Neuromorphic VLSI designs in creating energy-efficient AI prediction systems. Although conventional computer systems struggle to maintain energy efficiency, Neuromorphic computing offers a viable alternative. However, power utilization, latency issues, and scalability present significant challenges for real-time applications. The study suggests that novel approaches are necessary to improve these parameters without compromising accuracy or efficiency. The observations illustrate the promise of Neuromorphic VLSI systems by showing that AI inference performance can be greatly enhanced. We also discussed about the applications of large scale Neuromorphic computing. Our study involved a comprehensive review of 60 articles from leading academic databases, including Google Scholar, PubMed, Springer, Science Direct, and Elsevier. Our purpose was to investigate the potential of Neuromorphic VLSI Hardware in enhancing AI inference. Our review revealed that Neuromorphic VLSI Hardware holds promising prospects for advancing AI inference.

VLSI, Neuromorphic Computing, AI-Inference, Spiking, Memristor, Neural Network, Neuro-hybrid system