AI is revolutionizing our world, but it's also contributing to the climate crisis. In this talk, we'll explore the hidden energy costs of AI and discuss how to optimize algorithms, hardware, and infrastructure for a greener future


The exponential growth of artificial intelligence, particularly deep learning, has brought with it a surge in energy consumption and carbon emissions. This talk delves into the technical underpinnings of AI's environmental impact, examining the energy demands of model training, inference, and data management. We'll explore:

The energy cost of scale: Analyzing the relationship between model size, computational complexity, and energy consumption.
Hardware efficiency: Evaluating the role of specialized hardware, such as GPUs and TPUs, in optimizing energy usage.
Algorithmic optimization: Investigating techniques for developing energy-aware algorithms and reducing computational overhead.
Sustainable data centers: Discussing the role of renewable energy sources and efficient cooling systems in minimizing the environmental footprint of AI infrastructure.
Join us for a deep dive into the technical challenges and solutions for creating a more sustainable AI ecosystem. We'll discuss cutting-edge research, best practices, and open problems in the quest for energy-efficient and environmentally responsible artificial intelligence.