About SustainML

Our Objectives

This project is based on the insight that in order to significantly reduce the CO2 footprint of ML applications power-aware applications must be as easy to develop as standard ML systems are today. Users with little or no understanding of the tradeoffs between different architecture choices and energy footprint should be able to easily reduce the power consumption of their applications. We envision a sustainable, interactive ML framework development for Green AI that will comprehensively prioritize and advocate energy efficiency across the entire life cycle of an application and avoid AI-waste.

Model the requirements of specific ML applications.

Resource-aware optimization methods based on models from previous objectives.

Footprint and AI-waste transparent interactive design assistant that guides the developers through the entire process.

Dedicated toolchain implementation.

Collection of efficient methods and cores as catalogs and libraries of energy-optimized parameterized ML models.

Expected Impact

The SustainML framework will address the carbon and resource footprints of ML models and offer multiple pathways to avoid AI-waste from the very early stages of AI life-cycles. This will not be a limiting factor for the rapid growth of both AI research and AI adoption, but rather an enabling tool focused on sustainable growth.

About SustainML

SustainML is dedicated to creating a sustainable ML framework for Green AI. By prioritizing energy efficiency, SustainML aims to pave the way for environmentally conscious AI solutions that are both efficient and effective.

Our Objectives

Expected Impact

Less sensitive to statistical features of training data

Parallel and distributed training capabilities

Combine unstructured data with formal specifications of human knowledge

Human recognizable training

High Mathematical transparency.

Meet the Consortium

Coordinator Office Address

General information