CERN Accelerating science

Hlavná stránka > Výsledky hľadania: FPGA
Tipy pre vyhľadávanie
Rozšírené Hľadanie
Add to Search +
Hľadaj kolekcie:
Utriediť podľa: Zobraziť výsledky: Výstupný formát:
CERN Document Server 310 záznamov nájdených  1 - 10ďalšíkoniec  skoč na záznam: Hľadanie trvalo 0.70 sekúnd. 
1.
High Throughput FPGA Deployment of Distilled Deep Sets Networks for Jet Preselection in the High Level Trigger / Antel, Claire (CERN) ; Bezio, Lucas (Universite de Geneve (CH)) ; Berthet, Quentin (HEPIA - Haute école du paysage, d'ingénierie et d'architecture (CH)) ; Franchellucci, Stefano (Universite de Geneve (CH)) ; Sfyrla, Anna (Universite de Geneve (CH)) /ATLAS Collaboration
Deep Sets-based neural networks are well-suited to learning from unordered, variable-length inputs such as particle tracks associated with jets. Their permutation-invariant structure makes them attractive for high-energy physics (HEP) applications where input ordering is ambiguous and throughput is a critical constraint. [...]
ATL-DAQ-SLIDE-2025-467.- Geneva : CERN, 2025 - 1 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
Úplný záznam
2.
Convolutional Neural Networks for pile-up suppression in the ATLAS Global Trigger / Clarke Hall, Noah (University of London (GB)) ; Konstantinidis, Nikolaos (University of London (GB)) ; Kimura, Naoki (University of London (GB)) ; Martynwood, Alex Christopher (University of London (GB)) /ATLAS Collaboration
We describe a pile-up suppression algorithm for the ATLAS Global Trigger, using a convolutional neural network (CNN) architecture. The CNN operates on cell towers and exploits both shower topology and $E_T$ to correct for the contribution of pile-up. [...]
ATL-DAQ-SLIDE-2025-400.- Geneva : CERN, 2025 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : ML4Jets2025, Pasadena, California, Us, 17 - 23 Aug 2025
Úplný záznam
3.
Development and Performance of the Real-Time Muon Reconstruction for the L0 Trigger in HL-LHC ATLAS / Makita, Airu (University of Tokyo (JP)) /ATLAS Collaboration
We will present a detailed performance assessment of the implemented logic and provide an in-depth discussion of the validation strategy and methodology, which has been successfully integrated into our development workflow for the muon reconstruction in the Level-0 (L0) trigger in HL-LHC. The muon trigger will be fully upgraded with a real-time reconstruction system for HL-LHC. [...]
ATL-DAQ-SLIDE-2025-368.- Geneva : CERN, 2025 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 14th International Conference on New Frontiers in Physics 2025 (ICNFP 2025), Kolymbari, Crete, Gr, 17 - 31 Jul 2025
Úplný záznam
4.
Online track reconstruction with graph neural networks on FPGAs for the ATLAS experiment / Burleson, Jared Dynes (Univ. Illinois at Urbana Champaign (US)) ; ATLAS Collaboration /ATLAS Collaboration
The next phase of high energy particle physics research at CERN will involve the High-Luminosity Large Hadron Collider (HL-LHC). In preparation for this phase, the ATLAS Trigger and Data AcQuisition (TDAQ) system will undergo upgrades to the online software tracking capabilities. [...]
ATL-DAQ-SLIDE-2024-675.- Geneva : CERN, 2025 - 1 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Fast Machine Learning for Science Conference 2024, West Lafayette, Us, 15 - 18 Oct 2024
Úplný záznam
5.
Online track reconstruction with graph neural networks on FPGAs for the ATLAS experiment / Burleson, Jared Dynes (Univ. Illinois at Urbana Champaign (US)) ; ATLAS Collaboration /ATLAS Collaboration
The next phase of high energy particle physics research at CERN will involve the High-Luminosity Large Hadron Collider (HL-LHC). In preparation for this phase, the ATLAS Trigger and Data AcQuisition (TDAQ) system will undergo upgrades to the online software tracking capabilities. [...]
ATL-DAQ-SLIDE-2024-674.- Geneva : CERN, 2025 - 16 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Fast Machine Learning for Science Conference 2024, West Lafayette, Us, 15 - 18 Oct 2024
Úplný záznam
6.
Novell trigger strategies for HL-LHC in ATLAS / Geralis, Theodoros (Nat. Cent. for Sci. Res. Demokritos (GR)) /ATLAS Collaboration
The ATLAS experiment at CERN is constructing upgraded system for the "High Luminosity LHC", with collisions due to start in 2029. In order to deliver an order of magnitude more data than previous LHC runs, 14 TeV protons will collide with an instantaneous luminosity of up to 7.5 x 10e34 cm^-2s^-1, resulting in much higher pileup and data rates than the current experiment was designed to handle [...]
ATL-DAQ-SLIDE-2025-364.- Geneva : CERN, 2025 - 16 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 13th Edition of the Large Hadron Collider Physics Conference (LHCP2025), Taipei, Tw, 5 - 9 May 2025
Úplný záznam
7.
Guidelines for FPGA Gateware Development in LHCb / Perro, Alberto (Universite d'Aix-Marseille III (FR)) ; Vodnik, Mitja (CERN) ; Durante, Paolo (CERN) ; Vouters, Guillaume (Centre National de la Recherche Scientifique (FR)) ; Alessio, Federico (CERN)
This technical note outlines best practices and methodologies for FPGA develop- ment, with a focus on coding standards, verification techniques, and reusable design components tailored for LHCb gateware community. It introduces coding guidelines to ensure consistency, readability, and maintainability in FPGA designs, followed by an exploration of simulation methods and formal verification techniques to guarantee functional correctness and comprehensive coverage. [...]
LHCb-PUB-2025-009; CERN-LHCb-PUB-2025-009.- Geneva : CERN, 2025 - 21. Fulltext: PDF;
Úplný záznam
8.
QDIPS: Deep Sets Network for FPGA investigated for high speed inference on ATLAS / Antel, Claire (Universite de Geneve (CH))
We adapted DIPS (Deep Impact Parameter Sets), a deep sets neural network flavour tagging algorithm previously used in ATLAS offline low-level flavour tagging and online b-jet trigger preselections, for use on FPGA with the aim to assess its performance and resource costs. [...]
ATL-DAQ-PROC-2025-008. - 2025 - 5.
Original Communication (restricted to ATLAS) - Full text
Úplný záznam
9.
Online track reconstruction with graph neural networks on FPGAs for the ATLAS experiment / Dittmeier, Sebastian (Heidelberg University (DE))
For the HL-LHC upgrade of the ATLAS TDAQ system, a heterogeneous computing farm deploying GPUs and/or FPGAs is considered to be used for the Event Filter system, together with the use of modern machine learning algorithms such as Graph Neural Networks (GNNs) to solve computationally complex tasks within that system. [...]
ATL-DAQ-PROC-2025-003. - 2025 - 8.
Original Communication (restricted to ATLAS) - Full text
Úplný záznam
10.
AthXRT: Centralized FPGA Management for Accelerated Algorithms in Athena / Berthet, Quentin (Universite de Geneve (CH))
The integration of FPGAs as heterogeneous hardware accelerators in high-performance computing environments, such as the Athena framework used in the ATLAS experiment, presents significant opportunities for algorithm ac- celeration. [...]
ATL-DAQ-PROC-2025-002. - 2025 - 5.
Original Communication (restricted to ATLAS) - Full text
Úplný záznam

CERN Document Server : 310 záznamov nájdených   1 - 10ďalšíkoniec  skoč na záznam:
Prajete si byť pravidelne informovaný o výsledkoch tohto hľadania?
Založte si osobný email alert alebo použite RSS feed.
Nenašli ste čo ste hľadali? Skúste Vašu otázku na iných serveroch:
FPGA v Amazon
FPGA v CERN EDMS
FPGA v CERN Intranet
FPGA v CiteSeer
FPGA v Google Books
FPGA v Google Scholar
FPGA v Google Web
FPGA v IEC		FPGA v IHS
FPGA v INSPIRE
FPGA v ISO
FPGA v KISS Books/Journals
FPGA v KISS Preprints
FPGA v NEBIS
FPGA v SLAC Library Catalog
FPGA v Scirus