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LeanXcale SL

LEANXCALE SL
Country: Spain
12 Projects, page 1 of 3
  • Funder: EC Project Code: 611068
  • Funder: EC Project Code: 619606
  • Funder: EC Project Code: 687628
    Overall Budget: 6,283,900 EURFunder Contribution: 6,283,900 EUR

    VINEYARD will develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by using typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). VINEYARD will develop two types of energy-efficient servers integrating two novel hardware accelerator types: coarse-grain programmable dataflow engines and fine-grain all-programmable FPGAs that accommodate multiple ARM cores. The former will be suitable for data centre applications that can be represented in dataflow graphs while the latter will be used for accelerating applications that need tight communication between the processor and the hardware accelerators. Both types of programmable accelerators will be customized based on application requirements, resulting in higher performance and significantly reduced energy budgets. VINEYARD will additionally develop a new programming framework and the required system software to hide the programming complexity of the resulting heterogeneous system based on the hardware accelerators. This programming framework will also allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer efficient energy use. VINEYARD will foster the expansion of the soft-IP cores industry, currently limited in the embedded systems, to in data centre market. The VINEYARD consortium has strong industrial foundations, and covers the whole value chain in the data-centre ecosystem; from the data-centre vendors up to the data-centre application programmers. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases a) a bioinformatics application for high-accuracy brain modelling, b) two critical financial applications and c) a big-data analysis application.

  • Funder: EC Project Code: 732051
    Overall Budget: 4,832,130 EURFunder Contribution: 4,832,130 EUR

    The project aims at producing a European Cloud Database Appliance for providing a Database as a Service able to match the predictable performance, robustness and trustworthiness of on premise architectures such as those based on mainframes. The project will evolve cloud architectures to enable the increase of the uptake of cloud technology by providing the robustness, trustworthiness, and performance required for applications currently considered too critical to be deployed on existing clouds. CloudDBAppliance will deliver a cloud database appliance featuring: 1. A scalable operational database able to process high update workloads such as the ones processed by banks or telcos, combined with a fast analytical engine able to answer analytical queries in an online manner. 2. A Hadoop data lake integrated with the operational database to cover the needs from companies on big data. 3. A cloud hardware appliance leveraging the next generation of hardware to be produced by Bull, the main European hardware provider. This hardware is a scale-up hardware similar to the one of mainframes but with a more modern architecture. Both the operational database and the in-memory analytics engine will be optimized to fully exploit this hardware and deliver predictable performance. Additionally, CloudDBAppliance will deal with the need to tolerate catastrophic cloud data centres failures (e.g. a fire or natural disaster) providing data redundancy across cloud data centres.

  • Funder: EC Project Code: 101017441
    Overall Budget: 5,997,990 EURFunder Contribution: 5,997,990 EUR

    The specific focus of iHELP is on early identification and mitigation of the risks associated with Pancreatic Cancer based on the application of advance AI-based learning and decision support techniques on the historic (primary) data of Cancer patients gathered from established data banks and cohorts. This analysis helps to (i) determine key risks associated with Pancreatic Cancer, (ii) develop predictive models for identified risks, and (iii) develop adaptive models for targeted prevention and intervention measures. Based on these developments, the project selects high-risk individuals that are invited to take part in the pilot activities or digital trials. The digital trials are carried out through user-centric mobile and wearable applications that apply proven usability principles to offer more awareness, more engaging experience for health monitoring, risk assessment and personalised decision support. Close collaboration between clinical and AI experts focus on drawing decision support against identified/predicted risks and providing personalised recommendations (e.g. lifestyle changes, behavioural nudges, screening test etc) to the participants in the digital trials. The iHELP (mobile and wearable) technology solutions help in validating iHELP solutions and raising health related awareness at individual level. The (secondary) data gathered through the mobile and wearable applications (concerning life style, behavioural, social interactions and response to targeted prevention and intervention measures) is integrated with primary data in the standardised HHR format – within a big data platform. Frugal AI-based learning techniques are developed to provide near real-time risk assessment based on the integrated and standardised HHR data. iHELP solutions are targeted at multiple stakeholders, including policymakers that will get decision support on the design of new screening programs and new guidelines for bringing improvements in clinical and lifestyle aspects.

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