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Sökning: WFRF:(Bessani A.)

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1.
  • Kaiser, M., et al. (författare)
  • VEDLIoT: Very Efficient Deep Learning in IoT
  • 2022
  • Ingår i: Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022. - : IEEE. - 9783981926361
  • Konferensbidrag (refereegranskat)abstract
    • The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.
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2.
  • Bessani, A., et al. (författare)
  • BiobankCloud : A platform for the secure storage, sharing, and processing of large biomedical data sets
  • 2016
  • Ingår i: 1st International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2015 and Workshop on Big-Graphs Online Querying, Big-O(Q) 2015 held in conjunction with 41st International Conference on Very Large Data Bases, VLDB 2015. - Cham : Springer. - 9783319415758 - 9783319415765 ; , s. 89-105
  • Konferensbidrag (refereegranskat)abstract
    • Biobanks store and catalog human biological material that is increasingly being digitized using next-generation sequencing (NGS). There is, however, a computational bottleneck, as existing software systems are not scalable and secure enough to store and process the incoming wave of genomic data from NGS machines. In the BiobankCloud project, we are building a Hadoop-based platform for the secure storage, sharing, and parallel processing of genomic data. We extended Hadoop to include support for multi-tenant studies, reduced storage requirements with erasure coding, and added support for extensible and consistent metadata. On top of Hadoop, we built a scalable scientific workflow engine featuring a proper workflow definition language focusing on simple integration and chaining of existing tools, adaptive scheduling on Apache Yarn, and support for iterative dataflows. Our platform also supports the secure sharing of data across different, distributed Hadoop clusters. The software is easily installed and comes with a user-friendly web interface for running, managing, and accessing data sets behind a secure 2-factor authentication. Initial tests have shown that the engine scales well to dozens of nodes. The entire system is open-source and includes pre-defined workflows for popular tasks in biomedical data analysis, such as variant identification, differential transcriptome analysis using RNA-Seq, and analysis of miRNA-Seq and ChIP-Seq data.
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