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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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A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC / Di Guglielmo, Giuseppe (Columbia U.) ; Fahim, Farah (Fermilab ; Northwestern U.) ; Herwig, Christian (Fermilab) ; Valentin, Manuel Blanco (Northwestern U.) ; Duarte, Javier (UC, San Diego) ; Gingu, Cristian (Fermilab) ; Harris, Philip (MIT) ; Hirschauer, James (Fermilab) ; Kwok, Martin (Brown U.) ; Loncar, Vladimir (CERN ; Belgrade, Inst. Phys.) et al.
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. [...]
arXiv:2105.01683; FERMILAB-PUB-21-217-CMS-E-SCD.- 2021-06-07 - 9 p. - Published in : IEEE Trans. Nucl. Sci. 68 (2021) 2179 Fulltext: 5a82c8a6b63c02fc015568642085785c - PDF; 2105.01683 - PDF; fermilab-pub-21-217-cms-e-scd - PDF; External link: Fermilab Accepted Manuscript

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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. - 10 p.
Fermilab Library Server - Fulltext - Fulltext

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Development of a large pixel chip demonstrator in RD53 for ATLAS and CMS upgrades / Conti, Elia (CERN) ; Barbero, Marlon (Marseille, CPPM) ; Fougeron, Denis (Marseille, CPPM) ; Godiot, Stephanie (Marseille, CPPM) ; Menouni, Mohsine (Marseille, CPPM) ; Pangaud, Patrick (Marseille, CPPM) ; Rozanov, Alexandre (Marseille, CPPM) ; Breugnon, Patrick (Marseille, CPPM) ; Bomben, Marco (Paris U., VI-VII) ; Calderini, Giovanni (Paris U., VI-VII) et al. /RD53
RD53A is a large scale 65 nm CMOS pixel demonstrator chip that has been developed by the RD53 collaboration for very high rate (3 GHz/cm$^2$) and very high radiation levels (500 Mrad, possibly 1 Grad) for ATLAS and CMS phase 2 upgrades. It features serial powering operation and design variations in the analog and digital pixel matrix for different testing purposes. [...]
SISSA, 2017 - 5 p. - Published in : PoS TWEPP-17 (2017) 005 Fulltext: PDF; External link: PoS server
In : Topical Workshop on Electronics for Particle Physics, Santa Cruz, Ca, United States Of America, 11 - 15 Sep 2017, pp.005

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8	Fahim, F
7	Fahim, F.

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