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Faster and More Energy Efficient Machine Learning for Embedded Systems

Project description

Expediating and improving artificial intelligence system development

Artificial intelligence (AI) and machine learning technologies have the potential to provide significant benefits across various sectors and processes. Many organisations, corporations, and entities are actively engaged in research and development to create innovative tools and software based on these technologies. However, AI development, particularly in deep learning (DL), can be challenging and time-consuming, resulting in many projects remaining incomplete. The EU-funded EmbeDL project aims to address this issue by developing a revolutionary software development kit for on-premises installation. This kit not only optimises complex DL models on hardware systems but also provides manufacturers with essential toolchains and services. These services are designed to enhance the quality and efficiency of development, ultimately reducing the time required for completion.

Objective

EmbeDL is a Software Development Kit installed on premises to optimise complex DL models on embedded hardware systems. It empowers product manufacturers by providing them with the entire toolchain and services necessary to accelerate their AI-driven system development and launch into production. What is currently taking months, years or abandoned R&D, can now be done in weeks. Using the EmbeDL platform, the product development team can:
-Automatically optimise a DL model for specific hardware while meeting energy consumption, hardware cost, and other requirements, by pruning/compressing a state of the art model in a hardware and requirements-aware fashion or searching automatically for the optimal model.
-Automatically evaluate hardware for its model (currently existing as an internal tool to be developed into a full fledged product), by a combination of predictive methods and hardware-in-the-loop.
-Accelerate the development cycle with easy to use and highly automated tools.

Fields of science (EuroSciVoc)

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-EIC-ACC-BF - HORIZON EIC Accelerator Blended Finance

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-EIC-2022-ACCELERATOR-01

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Coordinator

EMBEDL AB
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.

€ 2 499 999,00
Address
GAMLA ALMEDALSVAGEN 39 PLAN 3
412 63 Goteborg
Sweden

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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
Södra Sverige Västsverige Västra Götalands län
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.

€ 3 640 830,00
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