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Tecniche Di  Machine Learning Con Dispositivi FPGA  per Gli Esperimenti Di Fisica Delle Particelle

Nov 2 – 4, 2022
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Contribution List

13 out of 13 displayed
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  1. 1. Efficient Machine Learning in High-Energy Physics
    Jennifer Ngadiuba (CMS)
    11/2/22, 2:15 PM
  2. 2. Introduction to FPGA
    Riccardo Travaglini (Istituto Nazionale di Fisica Nucleare)
    11/2/22, 3:00 PM
  3. 3. Deep Learning inference with FPGA
    Riccardo Travaglini (Istituto Nazionale di Fisica Nucleare)
    11/2/22, 3:35 PM
  4. 4. Fast inference with HLS4ML: Machine Learning with FPGA at LHC
    Thea Aarrestad (ETH Zurich)
    11/2/22, 4:30 PM
  5. 5. Xilinx ML/AI: technologies (edge, on-premise, on cloud) and solutions (VitisAI, Pynq, …)
    Mario Ruiz-Noguera (AMD)
    11/2/22, 5:05 PM
  6. 6. HLS4ML tutorial part 1
    Sioni Summers, Thea Aarrestad (ETH Zurich)
    11/3/22, 9:30 AM

    Functionalities, quantization and compression

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  7. 7. HLS4ML tutorial part 2
    Marco Lorusso (Istituto Nazionale di Fisica Nucleare), Sioni Summers, Thea Aarrestad (ETH Zurich)
    11/3/22, 2:30 PM

    Demo on real HW or deploying on AWS F1

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  8. 8. Deep Learning inference with the Bond Machine project: introduction and demo
    Mirko Mariotti (Istituto Nazionale di Fisica Nucleare)
    11/4/22, 9:00 AM
  9. 9. Open discussion and demo
    Riccardo Travaglini (Istituto Nazionale di Fisica Nucleare)
    11/4/22, 10:00 AM
  10. 10. ML/AI with Intel FPGA
    Vladimir Loncar
    11/4/22, 11:30 AM
  11. 11. Alternative solutions at the Edge: Lattice, Microchip
    Riccardo Travaglini (Istituto Nazionale di Fisica Nucleare)
    11/4/22, 12:30 PM
  12. 12. Introduction to Vitis AI
    Riccardo Travaglini (Istituto Nazionale di Fisica Nucleare)
    11/4/22, 2:00 PM
  13. 13. Vitis AI tutorial/hands-on
    Marco Lorusso (Istituto Nazionale di Fisica Nucleare)
    11/4/22, 2:20 PM