Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
An in-memory dataflow accelerator for deep learning (MEMFLUX)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Panel: Systems and communication engineering
Panel description: Electrical, electronic, communication, optical and systems engineering
Countries involved: Switzerland
Call: Call for proposals for ERC Proof of Concept Grant (ERC-2020-PoC, 2020)
Start date: 2021-07-01
End date: 2022-12-31
Duration (months): 18.0
Total project cost (EUR): 214285.71
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

Deep neural networks (DNNs), loosely inspired by biological neural networks, consist of parallel processing units called neurons interconnected by plastic synapses. By tuning the weights of these interconnections, these networks are able to perform certain cognitive tasks remarkably well. DNNs are being deployed all the way from cloud data centers to edge servers and even end devices and is projected to be a tens of billion Euro-market just for semiconductor companies in the next few years. There is a significant effort towards the design of custom ASICs based on reduced precision arithmetic and highly optimized dataflow. However, one of the primary reasons for the inefficiency, namely the need to shuttle millions of synaptic weight values between the memory and processing units, remains unaddressed. In-memory computing is an emerging computing paradigm that addresses this challenge of processor-memory dichotomy. For example, a computational memory unit with resistive memory (memristive) devices organized in a crossbar configuration is capable of performing matrix-vector multiply operations in place by exploiting the Kirchhoff’s circuits laws. Moreover, the computational time complexity reduces to O(1). The goal of this project is to prototype such an in-memory computing accelerator for ultra-low latency, ultra-low power DNN inference.

Keywords: deep learning,in memory computing,phase change memory
Distributed Massive MIMO for Next Generation Wireless Communications (FUTURE-MOBILE)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: United Kingdom
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2015, 2015)
Start date: 2016-10-01
End date: 2018-09-30
Duration (months): 24.0
Total project cost (EUR): 195454.8
EU funding (EUR): 195454.8
Participants: 1
Project status: CLOSED

In view of the rapid increase in demand for mobile data services, next generation wireless communication systems will have to provide greatly increased capacity density and high data rates greater than 50Gbps. However, satisfying these requirements for increasing numbers of users and connected devices, and increasingly bandwidth, processing power and energy-hungry applications will require a transformation in the way in which current mobile and wireless networks perform. Recently, massive multiple input multiple output (MIMO) technology, where each base station (BS) is equipped with a large number of antenna elements (typically tens or even hundreds) has emerged as a key enabler to achieve a 1000 times data rate and enormous spectral and energy efficiency. In order for massive MIMO to become a reality, the proposed work aims to explore transformative methods for the design of innovative techniques in 3 key challenging areas of massive MIMO communication systems: (i) fundamental limits; (ii), (ii) transmission strategy; and (iii) caching mechanisms. The solution is achieved with small remote antenna units equipped with compact massive MIMO arrays deployed over certain coverage regions, and developing practical signalling schemes, network coordination, and management protocols.

Keywords: cellular systems,massive mimo,distributed massive mimo,distributed antenna systems,channel estimation,interference management
Public FLOOD Emergency and Awareness SERVice (FLOOD-serv)

Framework: Horizon 2020 — Societal Challenges
Programme: Europe in a changing world - inclusive, innovative and reflective societies
Action type: Innovation actions
Countries involved: Austria,Spain,Italy,Spain,Romania,Slovakia,Belgium,Romania,Portugal,Portugal
Call: ICT-enabled open government (H2020-INSO-2015-CNECT, 2015)
Start date: 2016-08-01
End date: 2019-07-31
Duration (months): 36.0
Total project cost (EUR): 3226375.0
EU funding (EUR): 2528630.63
Participants: 12
Project status: CLOSED

Floods are an increasingly acute problem. Floods endanger lives and cause human tragedy as well as heavy economic losses. In addition to economic and social damage, floods can have severe environmental consequences, for example when installations holding large quantities of toxic chemicals are inundated or wetland areas destroyed. Intense precipitation has become more frequent and more intense, growing manmade pressure has increased the magnitude of floods that result from any level of precipitation, and flawed decisions about the location of human infrastructure have increased the flood loss potential. Flooding cannot be wholly prevented. Flood risk increases with ongoing climate change. Risk reduction in large international basins can only be achieved through transnational, interdisciplinary and stakeholder oriented approaches within the framework of a joint transnational research project. The overall objective of FLOOD-serv is to develop and to provide a pro-active and personalised citizen-centric public service application that will enhance the involvement of the citizen and will harness the collaborative power of ICT networks (networks of people, of knowledge, of sensors) to raise awareness on flood risks and to enable collective risk mitigation solutions and response actions. Other general objectives are: 1. Empowering local communities to directly participate in the design of emergency services dealing with floods mitigation actions. 2. Harness the power of new technologies, such as social media, and mobile technologies to increase the efficiency of public administrations in raising public awareness and education regarding floods risks, effects and impact. 3. Encourage the development and implementation of long-term, cost-effective and environmentally sound mitigation actions related to floods though an ICT-enabled cooperation and collaboration of all stakeholders: government, private sector, NGOs and other civil society organizations as well as citizens.

Keywords: flood emergency public service,awareness service,social media,mobile technologies,open government,citizen empowerment,transparency,public administration
Resource Bounded Graph Query Answering (GRACE)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Computer science and informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: United Kingdom
Call: ERC Advanced Grant (ERC-2014-ADG, 2014)
Start date: 2015-11-01
End date: 2021-10-31
Duration (months): 72.0
Total project cost (EUR): 2171524.0
EU funding (EUR): 2171524.0
Participants: 1
Project status: CLOSED

When we search for a product, can we find, using a single query, top choices ranked by Google and at the same time, recommended by our friends connected on Facebook? Is such a query tractable on the social graph of Facebook, which has over 1.31 billion nodes and 170 billion links? Is it feasible to evaluate such a query if we have bounded resources such as time and computing facilities? These questions are challenging: they demand a departure from the traditional query evaluation paradigm and from the classical computational complexity theory, and call for new resource-constrained methodologies to query big graphs. This project aims to tackle precisely these challenges, from fundamental problems to practical techniques, using radically new approaches. We will develop a graph pattern query language that allows us to, e.g., unify Web search (via keywords) and social search (via graph patterns), and express graph pattern association rules for social media marketing. We will revise the conventional complexity theory to characterize the tractability of queries on big data, and formalize parallel scalability with the increase of processors. We will also develop algorithmic foundations and resource-constrained techniques for querying big graphs, by ``making big data small''. When exact answers are beyond reach in big graphs, we will develop data-driven and query-driven approximation schemes to strike a balance between the accuracy and cost. As a proof of the theory, we will develop GRACE, a system to answer graph pattern queries on big GRAphs within bounded resourCEs, based on the techniques developed. We envisage that the project will deliver methodological foundations and practical techniques for querying big graphs in general, and for improving search engines and social media marketing in particular. A breakthrough in this subject will advance several fields, including databases, theory of computation, parallel computation and social data analysis.

Keywords: graph pattern queries,querying big graphs,tractability,parallel scalability,query evaluation,approximate query answering,unifying web search and social search,social media marketing
Coupling dimerisation and metathesis reactions to produce propene from ethanol using heterogeneous catalysts and microreactor systems (COUPCAT)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Spain,United States
Call: Marie Skłodowska-Curie Individual Fellowships (IF-GF) (H2020-MSCA-IF-2014, 2014)
Start date: 2015-04-01
End date: 2018-03-31
Duration (months): 36.0
Total project cost (EUR): 257191.2
EU funding (EUR): 257191.2
Participants: 2
Project status: CLOSED

Biomass-derived molecules contain excess oxygen that must be removed to render as fuel or chemicals. Due to feedstock fluctuations, ethanol and ethene are becoming key components to overcome these challenges. Given the current bioethanol (fermentation) and ethene (crackers) excess, and the propene shortage in Europe, alternative routes are required. During the outgoing phase under the supervision of Prof. Iglesia at UC Berkeley (USA), the researcher Dr. Iker Agirrezabal-Telleria will address the fundamental mechanistic aspects of dimerisation and metathesis reactions from ethanol to propene operating at low-temperature and studying the individual parameters using multidisciplinary approaches. This will include spectroscopic (in-situ UV-VIS and FTIR ) and isotopic techniques to elucidate the contribution and role of active metals for dimerisation (Ni), isomerisation (H+) and metathesis (Re, Mo). The optimised catalysts will be tested during the return phase under the supervision of Prof. Arias at the University of the Basque Country (Spain) using engineering approaches. Catalyst slurry and formulations will be prepared to test them on microreactor systems. The microreactors will be coated during a secondment period under the supervision of Prof. Kolb at Fraunhofer ICT-IMM (Germany). This phase aims to achieve process intensification and reduce heat-transfer issues occurring in packed-bed reactors. The comparison between fixed-bed and microreactors will enable to obtain interdisciplinary inputs to produce propene from ethanol. This Fellowship aims to carry out cutting-edge scientific training and transferable skill training such as project management, relevant for the re-integration period and to become an independent research leader in Europe. Moreover, this projects aims to have a positive impact on European society and competitiveness. These actions will involve outreach activities, results dissemination and IPR management.
Tuneable Acoustic Noise DampEning Material (TANDEM)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Ireland
Call: SME instrument (H2020-SMEInst-2018-2020-2, 2019)
Start date: 2019-06-01
End date: 2022-05-31
Duration (months): 36.0
Total project cost (EUR): 3513982.25
EU funding (EUR): 2312528.58
Participants: 1
Project status: CLOSED

Existing acoustic materials do not meet the challenges of the increasingly noisy modern world. Thick and heavy slabs of materials are required to block out low frequency noise, like that of cars and jet engines. Increasing production cost, product size, and emissions. Industries are facing increasingly stringent regulations, and don’t have the technology to comply. SoundBounce is an acoustic metamaterial providing superior noise reduction. It bypasses a critical technical bottleneck enabling the creation of smaller, lighter products with substantially reduced noise output. This is critical in the construction, automotive, power generation and aerospace sectors where space is at a premium. SoundBounce provides game-changing advantages: 100-1000x improvement on current state-of-the-art; 3x volume reduction; customisable; unique ability to attenuate low frequency noise; and clean technology. Market leaders in the €8 billion acoustic materials market have demonstrated their clear demand for the scaled up SoundBounce technology. Market growth is being driven by more stringent regulation, increasing social demand for lower noise emissions, and a rapidly urbanising population. The SoundBounce team is uniquely positioned to deliver this groundbreaking technology to the market, with decades of commercial experience and deep technical expertise. Project TANDEM will advance SoundBounce to TRL 9 completing the remaining scale-up and commercialisation of the technology. The EU is committed to significantly decreasing noise pollution and SoundBounce has the ability to address this ambitious challenge.

Keywords: automotive,aerospace,generators,construction,insulation,noise dampening
Automated, Transparent Citizen-Centric Public Policy Making based on Trusted Artificial Intelligence (AI4PublicPolicy)

Framework: Horizon 2020 — Societal Challenges
Programme: Europe in a changing world - inclusive, innovative and reflective societies
Theme: Innovative societies
Action type: Innovation actions
Countries involved: Spain,Italy,Italy,Italy,Italy,Cyprus,Greece,Bulgaria,Czechia,Netherlands,Luxembourg,Portugal,United Kingdom,United Kingdom,Portugal
Call: Pilot on using the European cloud infrastructure for public administrations (H2020-SC6-GOVERNANCE-2020, 2020)
Start date: 2021-03-01
End date: 2024-02-29
Duration (months): 36.0
Total project cost (EUR): 5080501.25
EU funding (EUR): 3999988.25
Participants: 18
Project status: SIGNED

AI4PublicPolicy is a joint effort of policymakers and Cloud/AI experts to unveil AI’s potential for automated, transparent and citizen-centric development of public policies. To this end, the project will deliver, validate, demonstrate and promote a novel Open Cloud platform (i.e. AI4PublicPolicy platform) for automated, scalable, transparent and citizen-centric policy management based on unique AI technologies. The AI4PublicPolicy platform will be an Open Virtualized Policy Management Environment (VPME) that will provide fully-fledged policy development/management functionalities based on AI technologies such as Machine Learning (ML), Deep Learning (DL), NLP and chatbots, while leveraging citizens’ participation and feedback. It will support the entire policy development lifecycle, based on technologies for the extraction, simulation, evaluation and optimization of interoperable and reusable public policies, with emphasis on citizen-centric policies development and optimization through the realization of citizen-oriented feedback loops. AI4PublicPolicy will complement public policy development functionalities with the ever-important process reengineering and organization transformation activities towards ensuring the effective transition from legacy policy development models to emerging AI-based policymaking. The AI4PublicPolicy VPME will be integrated with EOSC with a dual objective. First to facilitate access to the Cloud and HPC resources of EOSC/EGI that are required to enable the project’s AI tools, and second to boost the sustainability and wider use of the project’s developments. AI4PublicPolicy’s business plan for sustaining, expanding and commercializing the AI tools and the VPME is based on the development of a community of interested and engaged stakeholders (i.e. public authorities and other policymakers) around the project’s platform.

Keywords: policy development,artificial intelligence,virtualized policy management,environment,bigdata,public authorities,digital transformation
Cognitive Geometry: Deciphering neural concept spaces and engineering knowledge to empower smart brains in a smart society (GEOCOG)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: The Human Mind and Its Complexity
Panel description: Cognitive science, psychology, linguistics, theoretical philosophy
Countries involved: Germany,Norway
Call: ERC Consolidator Grant (ERC-2016-COG, 2016)
Start date: 2017-05-01
End date: 2024-01-31
Duration (months): 81.0
Total project cost (EUR): 2000000.0
EU funding (EUR): 2000000.0
Participants: 2
Project status: SIGNED

Through smart technology, we are overwhelmed with new information. Does this unlimited access to knowledge make us smarter? One of the key challenges for modern societies is to understand how the brain assembles our rich inventory of knowledge. Here, I will test the hypothesis that newly acquired knowledge is represented in the hippocampal formation in neural concept spaces, which are based on the coding principles and representational structures of the neural machinery involved in spatial navigation. The key idea is that the brain’s navigation system provides the building blocks of a neural metric for knowledge. In this groundbreaking cognitive neuroscience framework, I will bridge and integrate principles from Nobel Prize awarded neurophysiology and concepts from cognitive science and philosophy. Partly building on my ERC-StG project in which I discovered the core neural mechanisms underlying reconfiguration, integration and scaling of memory networks, the aim of my proposal is two-fold: 1. I seek to decipher neural concept spaces and unravel the neural codes of a cognitive geometry for knowledge and its deformations. 2. I will provide a proof-of-principle framework for next-generation neurocognitive technology and neural user models for cognitive enhancement to edit memories and engineer knowledge. Novel ‘Wikipedia’ learning tasks will be combined with state-of-the-art pattern analyses of space-resolved fMRI and time-resolved MEG to map and quantify representational structures. I will further develop AI-inspired analyses and closed loop brain-computer interfaces to perturb and edit neural concept space. The integrative mission of my program, from cells to systems-level involvement in cognition and to technology, opens up the exciting possibility to lay the ground for redefining cognitive neuroscience of knowledge by unravelling the fundamental neural principles of a cognitive topography and to make critical translations to empower smart brains in a smart society.

Keywords: hippocampus,fmri,cognitive neuroscience,entorhinal cortex,space,navigation
Platform for Open Development of Systems of Artificial Intelligence (Bonseyes)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Countries involved: Germany,Spain,Ireland,Germany,Austria,Switzerland,Switzerland,Switzerland,Sweden,Serbia,Greece,Serbia,United Kingdom,United Kingdom
Call: Smart Cyber-Physical Systems (H2020-ICT-2016-1, 2016)
Start date: 2016-12-01
End date: 2020-01-31
Duration (months): 38.0
Total project cost (EUR): 8593952.5
EU funding (EUR): 5018025.0
Participants: 16
Project status: CLOSED

The Bonseyes project aims to develop a platform consisting of a Data Marketplace, Deep Learning Toolbox, and Developer Reference Platforms for organizations wanting to adopt Artificial Intelligence in low power IoT devices (“edge computing”), embedded computing systems, or data center servers (“cloud computing”). It will bring about orders of magnitude improvements in efficiency, performance, reliability, security, and productivity in the design and programming of Systems of Artificial Intelligence that incorporate Smart Cyber Physical Systems while solving a chicken-egg problem for organizations who lack access to Data and Models. It’s open software architecture will facilitate adoption of the whole concept on a wider scale. It aims to address one of the most significant trends in the Internet of Things which is the shifting balance between edge computing and cloud computing. The early days of the IoT have been characterized by the critical role of cloud platforms as application enablers. Intelligent systems have largely relied on the cloud level for their intelligence, and the actual devices of which they consist have been relatively unsophisticated. This old premise is currently being shaken up, as the computing capabilities on the edge level advance faster than those of the cloud level. This paradigm shift—from the connected device paradigm to the intelligent device paradigm opens up numerous opportunities. To evaluate the effectiveness, technical feasibility, and to quantify the real-world improvements in efficiency, security, performance, effort and cost of adding AI to products and services using the Bonseyes platform, four complementary demonstrators will be built: Automotive Intelligent Safety, Automotive Cognitive Computing, Consumer Emotional Virtual Agent, and Healthcare Patient Monitoring. Bonseyes platform capabilities are aimed at being aligned with the European FI-PPP activities and take advantage of its flagship project FIWARE.

Keywords: artificial intelligence,machine learning,deep learning,data marketplace,developer platforms,system of systems,software architecture,distributed systems,autonomous systems,internet of things