Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Edward Kreinar"'
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
Autor:
Duc Hoang, Nhan Tran, Sioni Summers, Christoffer Petersson, Javier Duarte, Sergo Jindariani, Giuseppe Di Guglielmo, Edward Kreinar, Jennifer Ngadiuba, Kevin Pedro, Dylan Rankin, Maurizio Pierini, Nicolò Ghielmetti, Zhenbin Wu, Philip Harris, Mia Liu, Yutaro Iiyama, Hampus Linander, Vladimir Loncar, Thea Klaeboe Aarrestad
Publikováno v:
Machine Learning Science and Technology
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, ta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05fc325c7500b44ac1f69bc4e6110cc5
http://arxiv.org/abs/2101.05108
http://arxiv.org/abs/2101.05108
Autor:
Sergo Jindariani, Edward Kreinar, Dylan Rankin, Nhan Tran, Phil Harris, Rivera Rivera, Jennifer Ngadiuba, Song Han, Ben Kreis, Javier Duarte, Zhenbin Wu, Guiseppe di Guglielmo, Vladimir Loncar, Maurizio Pierini, Sioni Summers
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d5e39f22b038a8b76e0720d2b96e199
https://doi.org/10.2172/1630707
https://doi.org/10.2172/1630707
Autor:
Maurizio Pierini, Sioni Summers, Philip Harris, Sergo Jindariani, Edward Kreinar, Dylan Rankin, Song Han, Jennifer Ngadiuba, Vladimir Loncar, Ryan Rivera, Javier Duarte, Zhenbin Wu, Nhan Tran, Benjamin Kreis
Publikováno v:
FPGA
Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just
Autor:
Javier Duarte, Zhenbin Wu, Ryan Rivera, Maurizio Pierini, Jennifer Ngadiuba, Sergo Jindariani, Benjamin Kreis, Nhan Tran, Song Han, Edward Kreinar, Philip Harris
Publikováno v:
Journal of Instrumentation
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 p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81bf3b6816117ab5374b20ad58dc8cd2
http://cds.cern.ch/record/2316331
http://cds.cern.ch/record/2316331
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
Frontiers in Big Data, Vol 3 (2021)
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
Autor:
G. Di Guglielmo, Maurizio Pierini, Sioni Summers, Javier Duarte, Vladimir Loncar, Zhenbin Wu, Philip Harris, Sergo Jindariani, Jennifer Ngadiuba, Duc Hoang, Edward Kreinar, Dylan Rankin, Nhan Viet Tran
Publikováno v:
Journal of Instrumentation
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 in