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1.
hls4ml: deploying deep learning on FPGAs for L1 trigger and Data Acquisition / Duarte, Javier (UC, San Diego) ; Jindariani, Sergo (Fermilab) ; Kreis, Ben (Fermilab) ; Rivera, Rivera (Fermilab) ; Tran, Nhan (Fermilab) ; Ngadiuba, Jennifer (CERN) ; Pierini, Maurizio (CERN) ; Summers, Sioni (CERN) ; Loncar, Vladimir (CERN) ; Kreinar, Edward et al.
FERMILAB-SLIDES-19-706-SCD-V. - 2019. - 26 p.
FERMILABSLIDES - Fulltext
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2.
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. - 8 p.
Fermilab Library Server - Fulltext - Fulltext
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3.
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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4.
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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5.
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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6.
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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7.
Fast inference of Boosted Decision Trees in FPGAs for particle physics / Summers, Sioni (CERN) ; Guglielmo, Giuseppe Di (Columbia U.) ; Duarte, Javier (Fermilab) ; Harris, Philip (MIT) ; Hoang, Duc (Rhodes Coll.) ; Jindariani, Sergo (Fermilab) ; Kreinar, Edward (Unlisted, US, VA) ; Loncar, Vladimir (CERN ; Belgrade, Inst. Phys.) ; Ngadiuba, Jennifer (CERN) ; Pierini, Maurizio (CERN) et al.
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process. Thanks to its fully on-chip implementation, hls4ml performs inference of Boosted Decision Tree models with extremely low latency. [...]
arXiv:2002.02534; FERMILAB-PUB-20-400-CMS-SCD.- 2020-05-29 - 14 p. - Published in : JINST 15 (2020) P05026 Fulltext: 2002.02534 - PDF; fermilab-pub-20-400-cms-scd - PDF; Fulltext from Publisher: PDF; External link: FERMILABPUB
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8.
Fast inference of deep neural networks in FPGAs for particle physics / Duarte, Javier (Fermilab) ; Han, Song (MIT) ; Harris, Philip (MIT) ; Jindariani, Sergo (Fermilab) ; Kreinar, Edward (EIS Intl., Herndon) ; Kreis, Benjamin (Fermilab) ; Ngadiuba, Jennifer (CERN) ; Pierini, Maurizio (CERN) ; Rivera, Ryan (Fermilab) ; Tran, Nhan (Fermilab) et al.
Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. [...]
arXiv:1804.06913; FERMILAB-PUB-18-089-E.- 2018-07-27 - 30 p. - Published in : JINST 13 (2018) P07027 Fulltext: 1804.06913 - PDF; fulltext1668914 - PDF; fermilab-pub-18-089-e - PDF; Fulltext from Publisher: PDF; Preprint: PDF; External link: Fermilab Accepted Manuscript
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