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

Project description

Making artificial intelligence more energy efficient

Artificial intelligence (AI) has experienced a rapid increase in use across numerous industries and for countless applications. Solutions for climate change are among the most important. Despite this increase in use, most new AI technologies focus on improved output rather than energy efficiency and sustainability. This leads to increased emissions and energy consumption. In this context, the EU-funded SustainML project offers a solution in the form of an interactive design framework and models that allow developers to work in energy efficiency throughout the life-cycle of ML applications for Artificial intelligence, allowing for reduced emissions and energy usage.

Objective

AI is increasingly becoming a significant factor in the CO2 footprint of the European economy. To avoid a conflict between sustainability and economic competitiveness and to allow the European economy to leverage AI for its leadership in a climate friendly way, new technologies to reduce the energy requirements of all parts of AI system are needed. A key problem is the fact that tools (e.g. PyTorch) and methods that currently drive the rapid spread and democratization of AI prioritize performance and functionality while paying little attention to the CO2 footprint. As a consequence, we see rapid growth in AI applications, but not much so in AI applications that are optimized for low power and sustainability. To change that we aim to develop an interactive design framework and associated models, methods and tools that will foster energy efficiency throughout the whole life-cycle of ML applications: from the design and exploration phase that includes exploratory iterations of training, testing and optimizing different system versions through the final training of the production systems (which often involves huge amounts of data, computation and epochs) and (where appropriate) continuous online re-training during deployment for the inference process. The framework will optimize the ML solutions based on the application tasks, across levels from hardware to model architecture. AI developers from all experience levels will be able to make use of the framework through its emphasis on human-centric interactive transparent design and functional knowledge cores, instead of the common blackbox and fully automated optimization approaches in AutoML. The framework will be made available on the AI4EU platform and disseminated through close collaboration with initiatives such as the ICT 48 networks. It will also be directly exploited by the industrial partners representing various parts of the relevant value chain: from software framework, through hardware to AI services.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-CL4-2021-HUMAN-01

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Coordinator

PROYECTOS Y SISTEMAS DE MANTENIMIENTO SL
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 785 625,00
Address
PLAZA ENCINA DE LA NUM 10 ESC 4 PLANTA 2
28760 TRES CANTOS MADRID
Spain

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 785 625,00

Participants (5)

Partners (1)

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