In today's rapidly advancing world of Artificial Intelligence (AI), energy efficiency has emerged as a crucial factor to facilitate the ubiquitous development of intelligent systems. The efficient deployment of AI holds the key to overcoming limitations posed by power-constrained devices and contributes to sustainable technological progress. Neuromorphic computing offers a brain-inspired paradigm of AI, called Spiking Neural Networks (SNNs), that represents a promising step forward in sustainable AI development. Inspired by the brain's neural architecture, SNNs process information in sparse, asynchronous, and event-driven patterns, resulting in reduced power consumption. This project aims to integrate SNNs with modern integrated circuits propelling energy efficiency across various AI domains, such as object detection, autonomous driving and image classification. The project team aims to devise novel algorithms and hardware design with prototype chips to accelerate the performance of SNNs in low-power and memory-efficient systems. These spiking neural chips will enable the practical and immediate application of neuromorphic systems in areas like drones, autonomous robots, portable medical devices, and wearable smart assistants. Furthermore, the project embraces an algorithm-to-system approach, providing opportunities for high school, undergraduate, and graduate students to explore research in the field of neuromorphic computing. An essential focus of this project also lies i