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CEPC Technical Design Report: Accelerator
/ CEPC Study Group Collaboration
The Circular Electron Positron Collider (CEPC) is a large scientific project initiated and hosted by China, fostered through extensive collaboration with international partners. The complex comprises four accelerators: a 30 GeV Linac, a 1.1 GeV Damping Ring, a Booster capable of achieving energies up to 180 GeV, and a Collider operating at varying energy modes (Z, W, H, and ttbar). [...]
arXiv:2312.14363; IHEP-CEPC-DR-2023-01; IHEP-AC-2023-01.-
2024-06-03 - 1106 p.
- Published in : Radiat. Detect. Technol. Methods 8 (2024) 1-1105
Fulltext: 2312.14363 - PDF; Publication - PDF;
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Cold atoms in space: community workshop summary and proposed road-map
/ Alonso, Iván (Balearic Islands U.) ; Alpigiani, Cristiano (Washington U., Seattle) ; Altschul, Brett (South Carolina U.) ; Araújo, Henrique (Imperial Coll., London) ; Arduini, Gianluigi (CERN) ; Arlt, Jan (Aarhus U.) ; Badurina, Leonardo (King's Coll. London) ; Balaž, Antun (Belgrade, Inst. Phys.) ; Bandarupally, Satvika (Florence U. ; INFN, Florence) ; Barish, Barry C. (LIGO Lab., Caltech) et al.
We summarize the discussions at a virtual Community Workshop on Cold Atoms in Space concerning the status of cold atom technologies, the prospective scientific and societal opportunities offered by their deployment in space, and the developments needed before cold atoms could be operated in space. The cold atom technologies discussed include atomic clocks, quantum gravimeters and accelerometers, and atom interferometers. [...]
arXiv:2201.07789; FERMILAB-CONF-22-694-V; CERN-TH-2022-004.-
2022-11-20 - 64 p.
- Published in : EPJ Quant. Technol.: 9 (2022) , no. 1, pp. 30
Fulltext: bb4d1d31d6edd562e94939489bfc05d6 - PDF; 2201.07789 - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
/ Shanahan, Phiala (MIT) ; Terao, Kazuhiro (SLAC) ; Whiteson, Daniel (UC, Irvine) ; Aarts, Gert (Swansea U. ; ECT, Trento ; Fond. Bruno Kessler, Trento) ; Adelmann, Andreas (Northeastern U. ; PSI, Villigen) ; Akchurin, N. (Texas Tech.) ; Alexandru, Andrei (George Washington U. ; Maryland U.) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Apresyan, Artur (Fermilab) et al.
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. [...]
arXiv:2209.07559 ; FERMILAB-CONF-22-719-ND-PPD-QIS-SCD.
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Fermilab Library Server - eConf - Fulltext - Fulltext
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Applications and Techniques for Fast Machine Learning in Science
/ Deiana, Allison McCarn (Southern Methodist U.) ; Tran, Nhan (Fermilab ; Northwestern U. (main)) ; Agar, Joshua (Lehigh U. (main)) ; Blott, Michaela (Xilinx, Dublin) ; Di Guglielmo, Giuseppe (Columbia U. (main)) ; Duarte, Javier (UC, San Diego) ; Harris, Philip (MIT) ; Hauck, Scott (George Washington U. (main)) ; Liu, Mia (Purdue U.) ; Neubauer, Mark S. (Illinois U., Urbana) et al.
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. [...]
arXiv:2110.13041; FERMILAB-PUB-21-502-AD-E-SCD.-
2022-04-12 - 56 p.
- Published in : Front. Big Data 5 (2022) 787421
Fulltext: 2110.13041 - PDF; fermilab-pub-21-502-ad-e-scd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
/ Heintz, Aneesh (Cornell U.) ; Razavimaleki, Vesal (UC, San Diego) ; Duarte, Javier (UC, San Diego) ; DeZoort, Gage (Princeton U.) ; Ojalvo, Isobel (Princeton U.) ; Thais, Savannah (Princeton U.) ; Atkinson, Markus (Illinois U., Urbana) ; Neubauer, Mark (Illinois U., Urbana) ; Gray, Lindsey (Fermilab) ; Jindariani, Sergo (Fermilab) et al.
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. [...]
arXiv:2012.01563 ; FERMILAB-CONF-20-622-CMS-SCD.
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8 p.
Fermilab Library Server - Fulltext - Fulltext
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hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
/ Fahim, Farah (Northwestern U. ; Fermilab) ; Hawks, Benjamin (Fermilab) ; Herwig, Christian (Fermilab) ; Hirschauer, James (Fermilab) ; Jindariani, Sergo (Fermilab) ; Tran, Nhan (Fermilab) ; Carloni, Luca P. (Columbia U.) ; Di Guglielmo, Giuseppe (Columbia U.) ; Harris, Philip (MIT) ; Krupa, Jeffrey (MIT) et al.
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. [...]
arXiv:2103.05579 ; FERMILAB-CONF-21-080-SCD.
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10 p.
Fermilab Library Server - Fulltext - Fulltext
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Fast convolutional neural networks on FPGAs with hls4ml
/ Aarrestad, Thea (CERN) ; Loncar, Vladimir (CERN ; Belgrade, Inst. Phys.) ; Ghielmetti, Nicolò (CERN ; Belgrade, Inst. Phys.) ; Pierini, Maurizio (CERN) ; Summers, Sioni (CERN) ; Ngadiuba, Jennifer (Caltech, Pasadena (main)) ; Petersson, Christoffer (Unlisted, SE ; Chalmers U. Tech.) ; Linander, Hampus (Unlisted, SE) ; Iiyama, Yutaro (Tokyo U., ICEPP) ; Di Guglielmo, Giuseppe (Columbia U. (main)) et al.
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate an inference latency of $5\,\mu$s using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. [...]
arXiv:2101.05108; FERMILAB-PUB-21-130-SCD.-
2021-07-16 - 25 p.
- Published in : Mach. Learn. Sci. Technol. 2 (2021) 045015
Fulltext: 2101.05108 - PDF; fermilab-pub-21-130-scd - PDF; document - HTM; Fulltext from Publisher: PDF; Fulltext from publisher: PDF; External link: Fermilab Library Server
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Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
/ Iiyama, Yutaro (Tokyo U., ICEPP) ; Cerminara, Gianluca (CERN) ; Gupta, Abhijay (CERN) ; Kieseler, Jan (CERN) ; Loncar, Vladimir (CERN) ; Pierini, Maurizio (CERN) ; Qasim, Shah Rukh (CERN) ; Rieger, Marcel (CERN) ; Summers, Sioni (CERN) ; Van Onsem, Gerrit (CERN) et al.
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. [...]
arXiv:2008.03601; FERMILAB-PUB-20-405-E-SCD.-
2021-01-12 - 15 p.
- Published in : Front. Big Data 3 (2020) 598927
Fulltext: 2008.03601 - PDF; fermilab-pub-20-405-e-scd - PDF; Fulltext from Publisher: PDF; Fulltext from publisher: PDF; External link: Fermilab Library Server (fulltext available)
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Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
/ Loncar, Vladimir (Belgrade, Inst. Phys. ; CERN) ; Hoang, Duc (Rhodes Coll.) ; Di Guglielmo, Giuseppe (Columbia U.) ; Duarte, Javier (UC, San Diego) ; Harris, Philip (MIT, Cambridge, CTP) ; Jindariani, Sergo (Fermilab) ; Kreinar, Edward (Unlisted, US, VA) ; Liu, Mia (Fermilab) ; Ngadiuba, Jennifer (CERN) ; Pedro, Kevin (Fermilab) et al.
We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with FPGA firmware. Starting from benchmark models trained with floating point precision, we investigate different strategies to reduce the network's resource consumption by reducing the numerical precision of the network parameters to binary or ternary. [...]
arXiv:2003.06308; FERMILAB-PUB-20-167-PPD-SCD; FERMILAB-PUB-20-167-PPD-SCD.-
2020-12-01 - 12 p.
- Published in : Mach. Learn. Sci. Tech. 2 (2021) 015001
Fulltext: fermilab-pub-20-167-ppd-scd - PDF; 2003.06308 - PDF; Fulltext from publisher: PDF; External link: Fermilab Library Server (fulltext available)
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