Project description
Running deep neural networks on low-power IoT devices
The internet of things (IoT) and the rapid rise of artificial intelligence and machine learning has created a whole new set of challenges. One of these is the difficulty of running deep learning algorithms between diverse hardware platforms. This is an issue that has been largely addressed with workflows based on CPUs and GPUs. However, this is not the case with low-power devices like smartphones, cars, or watches on which deep learning inference has been gaining traction. The EU-funded hls4ml project will develop an open-software library that will automatically adapt deep neural networks to electronic circuits by utilising high-level synthesis tools and reducing resource utilisation.
Objective
With Deep Learning becoming ubiquitous in our life, running Deep Learning algorithms in real time on an heterogeneous set of hardware platforms is a pressing need in many aspects of our society. While traditional workflows based on standard CPUs and GPUs are established, Deep Learning inference on low-power devices (e.g. cars, smart phones, watches, etc) is gaining more attention. Typically, this would require strong background in electronic engineering to convert a neural network into a Digital Signal Processor. We propose to develop a complete open-software library to automatically convert Deep Neural Networks to electronic circuits, using High Level Synthesis tools. With a large basis of potential applications (e.g. autonomous cars, medical devices, portable monitoring devices, custom electronics as in the real-time data processing system of large-scale scientific experiments, etc.), the hls4ml library would assists users by automatising the logic circuit design as well as by reducing resource utilisation while preserving accuracy.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology mechanical engineering vehicle engineering automotive engineering autonomous vehicles
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences data science data processing
- natural sciences computer and information sciences artificial intelligence computational intelligence
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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)
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.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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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.
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.
ERC-POC - Proof of Concept Grant
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2020-PoC
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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.
1211 GENEVE 23
Switzerland
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.