Explore the words cloud of the ULPEC project. It provides you a very rough idea of what is the project "ULPEC" about.
The following table provides information about the project.
UNIVERSITE DE BORDEAUX
|Coordinator Country||France [FR]|
|Total cost||4˙862˙256 €|
|EC max contribution||3˙876˙396 € (80%)|
1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
|Duration (year-month-day)||from 2017-01-01 to 2020-12-31|
Take a look of project's partnership.
|1||UNIVERSITE DE BORDEAUX||FR (BORDEAUX)||coordinator||588˙348.00|
|2||ROBERT BOSCH GMBH||DE (GERLINGEN-SCHILLERHOEHE)||participant||934˙606.00|
|4||CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS||FR (PARIS)||participant||423˙318.00|
|5||SORBONNE UNIVERSITE||FR (PARIS)||participant||414˙081.00|
|6||UNIVERSITEIT TWENTE||NL (ENSCHEDE)||participant||372˙500.00|
|7||Twente solid state technology B.V.||NL (Enschede)||participant||345˙000.00|
|8||EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH||CH (ZUERICH)||participant||0.00|
|9||IBM RESEARCH GMBH||CH (RUESCHLIKON)||participant||0.00|
|10||UNIVERSITE PIERRE ET MARIE CURIE - PARIS 6||FR (PARIS)||participant||0.00|
The long term goal of ULPEC is to develop advanced vision applications with ultra-low power requirements and ultra-low latency. The output of the ULPEC project is a demonstrator connecting a neuromorphic event-based camera to a high speed ultra-low power consumption asynchronous visual data processing system (Spiking Neural Network with memristive synapses). Although ULPEC device aims to reach TRL 4, it is a highly application-oriented project: prospective use cases will be studied and an application roadmap will be developed, by considering interoperability for an integration in “systems of systems” as well as the definition of upper power consumption limits depending on future application. The project consortium therefore includes an industrial end-user (Bosch), which will more particularly investigate autonomous and computer assisted driving. Autonomous and computer assisted driving are indeed a major disruption in the transport and car manufacturing sector. Vision and recognition of traffic event must be computed with very low latency (to improve security) and low power (to accommodate the power limited environment in a car, such as power budget and heat dissipation). Substantial impact on innovation capacity and creation of market opportunities is expected under the ULPEC project: four enterprises (two SMEs) participate to the project. The ULPEC project is an opportunity for European companies such as TSST to increase the competitiveness and increase the global market share in manufacturing tools for complex oxide thin film synthesis. Besides, a compact, low-power vision system based on the technology intended to be developed in this project would generate a distinct competitive advantage over conventional solutions and would clearly boost Chronocam’s market potential. ULPEC is also an opportunity for SMEs to develop stronger collaboration with the industrial leaders involved in the project, such as IBM and Bosch.
|Reporting report nÂ°1||Documents, reports||2020-03-20 18:22:18|
|List of interesting applications||Documents, reports||2020-03-20 18:22:18|
|First ULPEC dissemination and public engagement strategy paper||Documents, reports||2020-03-20 18:22:17|
|Project online presence||Websites, patent fillings, videos etc.||2020-03-20 18:22:17|
|Project management guidelines||Documents, reports||2020-03-20 18:22:17|
Take a look to the deliverables list in detail: detailed list of ULPEC deliverables.
|year||authors and title||journal||last update|
Michael Pfeiffer, Thomas Pfeil
Deep Learning With Spiking Neurons: Opportunities and Challenges
published pages: , ISSN: 1662-453X, DOI: 10.3389/fnins.2018.00774
|Frontiers in Neuroscience 12||2020-03-20|
Luce Chiodelli, Sylvain Saighi
ULPEC,Ultra-Low Power Event-Based Camera, H2020
published pages: 79-81, ISSN: 2398-7073, DOI: 10.21820/23987073.2018.9.79
SÃ¶ren Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, StÃ©phane Fusil, StÃ©phanie Girod, CÃ©cile CarrÃ©tÃ©ro, Karin Garcia, StÃ©phane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibes, AgnÃ¨s BarthÃ©lÃ©my, Sylvain SaÃ¯ghi, Vincent Garcia
Learning through ferroelectric domain dynamics in solid-state synapses
published pages: 14736, ISSN: 2041-1723, DOI: 10.1038/ncomms14736
|Nature Communications 8||2020-03-20|
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The information about "ULPEC" are provided by the European Opendata Portal: CORDIS opendata.
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