Zobrazeno 1 - 10 of 27 pro vyhledávání: '"Vladimir Loncar"'
1
Akademický článek
Ultrafast jet classification at the HL-LHC
Autor: Patrick Odagiu, Zhiqiang Que, Javier Duarte, Johannes Haller, Gregor Kasieczka, Artur Lobanov, Vladimir Loncar, Wayne Luk, Jennifer Ngadiuba, Maurizio Pierini, Philipp Rincke, Arpita Seksaria, Sioni Summers, Andre Sznajder, Alexander Tapper, Thea K Årrestad
Publikováno v: Machine Learning: Science and Technology, Vol 5, Iss 3, p 035017 (2024)
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the inpu
Externí odkaz: https://doaj.org/article/4541642a69904f4eb05a8f8a4651282e
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2
Akademický článek
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Autor: Elham E Khoda, Dylan Rankin, Rafael Teixeira de Lima, Philip Harris, Scott Hauck, Shih-Chieh Hsu, Michael Kagan, Vladimir Loncar, Chaitanya Paikara, Richa Rao, Sioni Summers, Caterina Vernieri, Aaron Wang
Publikováno v: Machine Learning: Science and Technology, Vol 4, Iss 2, p 025004 (2023)
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 impl
Externí odkaz: https://doaj.org/article/466e78a8486f42c9a5dabee5d438eddd
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3
Akademický článek
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Autor: Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim, Marcel Rieger, Sioni Summers, Gerrit Van Onsem, Kinga Anna Wozniak, Jennifer Ngadiuba, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Mia Liu, Kevin Pedro, Nhan Tran, Edward Kreinar, Zhenbin Wu
Publikováno v: Frontiers in Big Data, Vol 3 (2021)
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 FGP
Externí odkaz: https://doaj.org/article/c0766df513ec43ff8f6284d42859ca59
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6
AIgean : An Open Framework for Deploying Machine Learning on Heterogeneous Clusters
Autor: Naif Tarafdar, Giuseppe Di Guglielmo, Philip C. Harris, Jeffrey D. Krupa, Vladimir Loncar, Dylan S. Rankin, Nhan Tran, Zhenbin Wu, Qianfeng Shen, Paul Chow
Publikováno v: ACM Transactions on Reconfigurable Technology and Systems. 15:1-32
AIgean , pronounced like the sea, is an open framework to build and deploy machine learning (ML) algorithms on a heterogeneous cluster of devices (CPUs and FPGAs). We leverage two open source projects: Galapagos , for multi-FPGA deployment, and hls4m
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::42aad0c40f1e4e9f47062070ba970df3
https://doi.org/10.1145/3482854
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7
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Autor: Elham E Khoda, Dylan Rankin, Rafael Teixeira de Lima, Philip Harris, Scott Hauck, Shih-Chieh Hsu, Michael Kagan, Vladimir Loncar, Chaitanya Paikara, Richa Rao, Sioni Summers, Caterina Vernieri, Aaron Wang
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 impl
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32c09416e4d7fdfbb4a9a41df9ad42bc
http://cds.cern.ch/record/2816114
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8
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
Autor: Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini, Marcel Roed, Sioni Summers, Thea Aarrestad, Christoffer Petersson, Hampus Linander, Jennifer Ngadiuba, Kelvin Lin, Philip Harris
Publikováno v: Machine Learning: Science and Technology, 3 (4)
In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network ar
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88109b235b4c3c628cd2f34015476534
http://cds.cern.ch/record/2839971
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9
Lightweight Jet Reconstruction and Identification as an Object Detection Task
Autor: Adrian Alan Pol, Thea Aarrestad, Ekaterina Govorkova, Roi Halily, Anat Klempner, Tal Kopetz, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Olya Sirkin, Sioni Summers
Publikováno v: Machine Learning: Science and Technology, 3 (2)
We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN large hadron collider (LHC). Collision events produced at the LHC and represented as an image co
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d433a2ab5a491d8f332742decb47725c
http://cds.cern.ch/record/2801371
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