PhD in Artificial Intelligence And Machine Learning

This project aims to develop a novel brain-inspired hardware-algorithm co-design framework that enhances the performance of computational models by closely mimicking the operational principles of the human brain. Brain-inspired computing seeks to emulate the structure and function of the brain's neural networks, offering significant improvements in processing efficiency, power consumption, and speed over traditional computing paradigms. In this project, we not only focus on designing new brain-inspired algorithms to boost computational performance but also develop an integrated co-design framework that optimally maps these algorithms onto brain-inspired hardware. The goal is to achieve optimal power efficiency, minimal area consumption, and reduced latency, making the systems ideal for real-time applications.