PhD in Business

This project aims to pioneer advancements in the energy-efficient Generative AI models (GenAI), focusing on achieving faster inference times and reduced model sizes without compromising performance and increasing carbon emissions./nAs GenAI becomes increasingly central to a wide range of applications, from generating images to generating videos and music, their computational demand and the time required for training and inference have escalated. This research seeks to address these challenges by developing innovative techniques for efficiency, including architectural innovations, compression strategies, algorithmic improvements, and system level optimizations./nThis project will contribute to making GenAI more democratic, efficient, and scalable, paving the way for their application in real-time and resource-constrained scenarios. The main research objectives are to develop cutting-edge techniques for model compression, design and experiment with new GenAI architectures, create new algorithms and system wide optimizations, and develop benchmarks and metrics to evaluate the energy-aware efficiency and performance of GenAI./nYou will conduct comprehensive experimental validation and testing. This approach seeks to push the boundaries of what's possible with GenAI, setting new standards for efficiency and accessibility in AI technologies.