| Talk | |||||||||||
| Title | Fast Deep Learning on FPGAs for the Phase-II L0 Muon Barrel Trigger of the ATLAS Experiment | ||||||||||
| Video |
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| Author(s) | Sabetta, Luigi (speaker) (Sapienza Universita e INFN, Roma I (IT)) | ||||||||||
| Corporate author(s) | CERN. Geneva | ||||||||||
| Imprint | 2019-04-17. - 0:18:21. | ||||||||||
| Series | (LPCC Workshops) (3rd IML Machine Learning Workshop) | ||||||||||
| Lecture note | on 2019-04-17T17:10:00 | ||||||||||
| Subject category | LPCC Workshops | ||||||||||
| Abstract | The Level-0 Muon Trigger system of the ATLAS experiment will undergo a full upgrade for HL-LHC to stand the challenging performances requested with the increasing instantaneous luminosity. The upgraded trigger system foresees to send RPC raw hit data to the off-detector trigger processors, where the trigger algorithms run on new generation of Field-Programmable Gate Arrays (FPGAs). The FPGA represents an optimal solution in this context, because of its flexibility, wide availability of logical resources and high processing speed. Studies and simulations of different trigger algorithms have been performed, and novel low precision deep neural network architectures (based on ternary dense and convnet networks) optimized to run on FPGAs and to cope with sparse data are presented. Both physics performances in terms of efficiency and fake rates, and FPGA logic resource occupancy and timing obtained with the developed algorithms are presented. | ||||||||||
| Copyright/License | © 2019-2024 CERN | ||||||||||
| Submitted by | paul.seyfert@cern.ch | ||||||||||
Record created 2019-04-26, last modified 2022-11-02
Talk details