As Artificial Intelligence (AI) continues to transform industries from healthcare to autonomous driving, the demand for more powerful and energy-efficient computing hardware is increasing. This project explores how to optimize the efficiency of modern AI hardware—such as GPUs, TPUs, and AI accelerators—by analyzing and leveraging the trade-offs between accuracy, energy consumption, and computational throughput.

The core objective is to develop a comprehensive understanding of the design space of AI accelerators, focusing on how different design choices impact overall system efficiency. The project aims to:

• Identify and model the trade-offs between performance, energy usage, and result accuracy.

• Develop tools and frameworks that help researchers and engineers navigate these trade-offs effectively.

• Propose guidelines and innovations for more balanced and sustainable AI hardware solutions.