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1.
  • Abata, E., et al. (författare)
  • Study of energy response and resolution of the ATLAS barrel calorimeter to hadrons of energies from 20 to 350 GeV
  • 2010
  • Ingår i: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier. - 0168-9002 .- 1872-9576 .- 0167-5087. ; 621:1-3, s. 134-150
  • Tidskriftsartikel (refereegranskat)abstract
    • A fully instrumented slice of the ATLAS detector was exposed to test beams from the SPS (Super Proton Synchrotron) at CERN in 2004. In this paper, the results of the measurements of the response of the barrel calorimeter to hadrons with energies in the range 20-350 GeV and beam impact points and angles corresponding to pseudo-rapidity values in the range 0.2-0.65 are reported. The results are compared to the predictions of a simulation program using the Geant 4 toolkit. (C) 2010 Published by Elsevier B.V.
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2.
  • Salami, B., et al. (författare)
  • LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing
  • 2020
  • Ingår i: PROCEEDINGS OF THE 2020 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2020). - 1530-1591. - 9783981926347 ; , s. 169-174
  • Konferensbidrag (refereegranskat)abstract
    • The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017.
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3.
  • 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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