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1.
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. -
Fermilab Library Server - eConf - Fulltext - Fulltext
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2.
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark / Borras, Hendrik (Heidelberg U.) ; Di Guglielmo, Giuseppe (Columbia U.) ; Duarte, Javier (UC, San Diego) ; Ghielmetti, Nicolò (CERN) ; Hawks, Ben (Fermilab) ; Hauck, Scott (Washington U., Seattle) ; Hsu, Shih-Chieh (Washington U., Seattle) ; Kastner, Ryan (UC, San Diego) ; Liang, Jason (UC, San Diego) ; Meza, Andres (UC, San Diego) et al.
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. [...]
arXiv:2206.11791 ; FERMILAB-CONF-22-479-SCD. -
Fermilab Library Server - Fulltext - Fulltext
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3.
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml / Khoda, Elham E. (Washington U., Seattle) ; Rankin, Dylan (MIT) ; de Lima, Rafael Teixeira (SLAC) ; Harris, Philip (MIT) ; Hauck, Scott (Washington U., Seattle) ; Hsu, Shih-Chieh (Washington U., Seattle) ; Kagan, Michael (SLAC) ; Loncar, Vladimir (CERN) ; Paikara, Chaitanya (Washington U., Seattle) ; Rao, Richa (Washington U., Seattle) et al.
Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). [...]
arXiv:2207.00559.- 2023-04-10 - 12 p. - Published in : Mach. Learn. Sci. Tech. 4 (2023) 025004 Fulltext: 2207.00559 - PDF; document - PDF;
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4.
QONNX: Representing Arbitrary-Precision Quantized Neural Networks / Pappalardo, Alessandro (Unlisted, IE) ; Umuroglu, Yaman (Unlisted, IE) ; Blott, Michaela (Unlisted, IE) ; Mitrevski, Jovan (Fermilab) ; Hawks, Ben (Fermilab) ; Tran, Nhan (Fermilab) ; Loncar, Vladimir (MIT, LNS) ; Summers, Sioni (CERN) ; Borras, Hendrik (U. Heidelberg) ; Muhizi, Jules (Harvard U. (main)) et al.
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. [...]
arXiv:2206.07527 ; FERMILAB-CONF-22-471-SCD. - 9 p.
Fermilab Library Server - Fulltext - Fulltext
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5.
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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6.
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. - 10 p.
Fermilab Library Server - Fulltext - Fulltext
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7.
FPGA-accelerated machine learning inference as a service for particle physics computing / Duarte, Javier (Fermilab) ; Harris, Philip (MIT) ; Hauck, Scott (Washington U., Seattle) ; Holzman, Burt (Fermilab) ; Hsu, Shih-Chieh (Washington U., Seattle) ; Jindariani, Sergo (Fermilab) ; Khan, Suffian (Microsoft, Redmond) ; Kreis, Benjamin (Fermilab) ; Lee, Brian (Microsoft, Redmond) ; Liu, Mia (Fermilab) et al.
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. [...]
arXiv:1904.08986; FERMILAB-PUB-19-170-CD-CMS-E-ND.- 2019-10-14 - 16 p. - Published in : Comput. Softw. Big Sci. 3 (2019) 13 Fulltext: 1904.08986 - PDF; fermilab-pub-19-170-cd-cms-e-nd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Accepted Manuscript
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8.
Firmware development and testing of the ATLAS Pixel Detector / IBL ROD card / Gabrielli, Alessandro (INFN, Bologna ; Bologna U.) ; Backhaus, Malte (CERN) ; Balbi, Gabriele (INFN, Bologna ; Bologna U.) ; Bindi, Marcello (Gottingen U.) ; Chen, Shaw-pin (Washington U.) ; Falchieri, Davide (INFN, Bologna ; Bologna U.) ; Flick, Tobias (Wuppertal U.) ; Hauck, Scott Alan (Washington U.) ; Hsu, Shih-Chieh (Washington U.) ; Kretz, Moritz (Heidelberg U.) et al.
The ATLAS Experiment is reworking and upgrading systems during the current LHC shut down. [...]
ATL-INDET-PROC-2014-016. - 2015. - 7 p.
Original Communication (restricted to ATLAS) - Full text
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Vedi anche: autori con nomi simili
8 Hauck, S
1 Hauck, S A
2 Hauck, S.
3 Hauck, Scott Alan
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