Join us for a cutting-edge workshop on sustainable AI! As AI models continue to grow in complexity, their energy consumption and carbon footprint become more of a  concern. SCEFA will focus on developing frugal AI models that can handle data efficiently, while minimizing energy consumption and environmental impact.
We’ll explore the challenges of training with limited data and computational resources, and we’ll examine approaches like pruning, quantization, and knowledge distillation to deploy energy-efficient models. Our goal is to develop algorithms that reduce energy consumption while maintaining robust performance in the face of noise.

We welcome submissions on both hardware and software sides, applicative and theoretical, sharing the spirit of improving data usage, training and/or inference efficiency.

The full list of the articles accepted is publicly available.