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.
Fields of science
- natural sciencescomputer and information sciencesdata sciencedata processing
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineering
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Programme(s)
Funding Scheme
ERC-POC-LS - ERC Proof of Concept Lump Sum PilotHost institution
ORGANISATION EUROPEENNE POUR LA RECHERCHE NUCLEAIRE
Net EU contribution
€ 150 000,00
Address
Esplanade Des Particules 1 Parcelle 11482 De Meyrin Batiment Cadastral 1046
1211 Geneve 23
Switzerland
1211 Geneve 23
Switzerland
Activity type
Research Organisations
Non-EU contribution
No data
Region
Schweiz/Suisse/Svizzera Région lémanique Genève
Beneficiaries (1)
ORGANISATION EUROPEENNE POUR LA RECHERCHE NUCLEAIRE
Switzerland
Net EU contribution
€ 150 000,00
Address
Esplanade Des Particules 1 Parcelle 11482 De Meyrin Batiment Cadastral 1046
1211 Geneve 23
1211 Geneve 23
Activity type
Research Organisations
Non-EU contribution
No data
Region
Schweiz/Suisse/Svizzera Région lémanique Genève