Project description DEENESFRITPL 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. Show the project objective Hide the project objective 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 engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehiclesengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencescomputer and information sciencesdata sciencedata processingnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Keywords Machine Learning Deep Learning HLS FPGA Edge Computing AI ASIC Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2020-POC - Call for proposals for ERC Proof of Concept Grant Call for proposal ERC-2020-PoC See other projects for this call Funding Scheme ERC-POC - Proof of Concept Grant Host 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 See on map Region Schweiz/Suisse/Svizzera Région lémanique Genève Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost No data Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all 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 See on map Region Schweiz/Suisse/Svizzera Région lémanique Genève Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost No data
