The SustainML Project is an EU Funded project.

About

Sustain ML: Application Aware, Life-Cycle Oriented Model-Hardware Co-Design Framework for Sustainable, Energy Efficient ML Systems

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.

O1: Model the requirements of specific ML applications.
O2: Resource-aware optimization methods based on models from previous objectives.
O3: Footprint and AI-waste transparent interactive design assistant that guides the developers through the entire process.
O4: Collection of efficient methods and cores as catalogs and libraries of energy-optimized parameterized ML models.
O5: Dedicated toolchain implementation.

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.

IMPACT 1: On a global level reducing AI waste through the entire life cycle to a wide spectrum of application domains targeting all AI developers from novice to expert. Ultimately lowering the global consumption of data centers and the energy/carbon footprint of AI, with the potential to accelerate AI research and adoption.
IMPACT 2: While AI will not solve all problems by itself, the ability to greatly expand AI into all industry sectors and use its means of optimizing resource consumption more widely, without AI itself being a major CO2 emission and financial cost factor, will be hugely impactful.
IMPACT 3: Transition from the “democratisation of AI” to the “democratisation of Green AI” that will allow especially SMEs, private enthusiasts, NGOs, and individual innovators to develop and use AI in a sustainable way.
IMPACT 4: Cultivating trust in AI for the general public, and insight into AI for non-expert practitioners through systematic, transparent, and explainable taxonomy and knowledge recycling approaches.
IMPACT 5: Being a nexus to spread new technologies/hardware such as PIM that can offer a sustainable alternative to current HW.

SustainML is financed by EU

This project has received funding from the European Union‘s Horizon 2020 research and innovation programme under grant agreement No 101070408. The project started in October 2021 and will end in October 2025. The opinions expressed on this website reflect only the author‘s view and reflects in no way the European Commission‘s opinions. The European Commission is not responsible for any use that may be made of the information it contains.

Participants

The SustainML consortium consists of a diverse mix of partners from small to multinational companies as well as from academia to an EU-wide acting foundation. This diversity will ensure a broad spectrum of requirements and use cases for the outcomes of this project.

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