Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Kreinar, Edward"'
Autor:
Fahim, Farah, Hawks, Benjamin, Herwig, Christian, Hirschauer, James, Jindariani, Sergo, Tran, Nhan, Carloni, Luca P., Di Guglielmo, Giuseppe, Harris, Philip, Krupa, Jeffrey, Rankin, Dylan, Valentin, Manuel Blanco, Hester, Josiah, Luo, Yingyi, Mamish, John, Orgrenci-Memik, Seda, Aarrestad, Thea, Javed, Hamza, Loncar, Vladimir, Pierini, Maurizio, Pol, Adrian Alan, Summers, Sioni, Duarte, Javier, Hauck, Scott, Hsu, Shih-Chieh, Ngadiuba, Jennifer, Liu, Mia, Hoang, Duc, Kreinar, Edward, Wu, Zhenbin
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drasticall
Externí odkaz:
http://arxiv.org/abs/2103.05579
Autor:
Aarrestad, Thea, Loncar, Vladimir, Ghielmetti, Nicolò, Pierini, Maurizio, Summers, Sioni, Ngadiuba, Jennifer, Petersson, Christoffer, Linander, Hampus, Iiyama, Yutaro, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Rankin, Dylan, Jindariani, Sergo, Pedro, Kevin, Tran, Nhan, Liu, Mia, Kreinar, Edward, Wu, Zhenbin, Hoang, Duc
Publikováno v:
Mach. Learn.: Sci. Technol. 2 045015 (2021)
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:
http://arxiv.org/abs/2101.05108
Autor:
Heintz, Aneesh, Razavimaleki, Vesal, Duarte, Javier, DeZoort, Gage, Ojalvo, Isobel, Thais, Savannah, Atkinson, Markus, Neubauer, Mark, Gray, Lindsey, Jindariani, Sergo, Tran, Nhan, Harris, Philip, Rankin, Dylan, Aarrestad, Thea, Loncar, Vladimir, Pierini, Maurizio, Summers, Sioni, Ngadiuba, Jennifer, Liu, Mia, Kreinar, Edward, Wu, Zhenbin
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms
Externí odkaz:
http://arxiv.org/abs/2012.01563
Autor:
Iiyama, Yutaro, Cerminara, Gianluca, Gupta, Abhijay, Kieseler, Jan, Loncar, Vladimir, Pierini, Maurizio, Qasim, Shah Rukh, Rieger, Marcel, Summers, Sioni, Van Onsem, Gerrit, Wozniak, Kinga, Ngadiuba, Jennifer, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Rankin, Dylan, Jindariani, Sergo, Liu, Mia, Pedro, Kevin, Tran, Nhan, Kreinar, Edward, Wu, Zhenbin
Publikováno v:
Frontiers in Big Data 3 (2021) 44
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:
http://arxiv.org/abs/2008.03601
Autor:
Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hoang, Duc, Jindariani, Sergo, Kreinar, Edward, Liu, Mia, Loncar, Vladimir, Ngadiuba, Jennifer, Pedro, Kevin, Pierini, Maurizio, Rankin, Dylan, Sagear, Sheila, Summers, Sioni, Tran, Nhan, Wu, Zhenbin
Publikováno v:
Mach. Learn.: Sci. Technol. 2, 015001 (2020)
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
Externí odkaz:
http://arxiv.org/abs/2003.06308
Autor:
Summers, Sioni, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hoang, Duc, Jindariani, Sergo, Kreinar, Edward, Loncar, Vladimir, Ngadiuba, Jennifer, Pierini, Maurizio, Rankin, Dylan, Tran, Nhan, Wu, Zhenbin
Publikováno v:
JINST 15 P05026 (2020)
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
Externí odkaz:
http://arxiv.org/abs/2002.02534
Autor:
Duarte, Javier, Han, Song, Harris, Philip, Jindariani, Sergo, Kreinar, Edward, Kreis, Benjamin, Ngadiuba, Jennifer, Pierini, Maurizio, Rivera, Ryan, Tran, Nhan, Wu, Zhenbin
Publikováno v:
JINST 13 P07027 (2018)
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:
http://arxiv.org/abs/1804.06913
Autor:
Kreinar, Edward J.
Complete-coverage robots, such as a lawnmower or snowplow, require a centimeter-level localization solution in order to navigate reliably. Unmodeled wheel slip or other odometry errors may cause localization to diverge beyond the bounds of uncertaint
Externí odkaz:
http://rave.ohiolink.edu/etdc/view?acc_num=case1367256203
Akademický článek
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Autor:
Loncar, Vladimir, Ngadiuba, Jennifer, Duarte, Javier, Harris, Philip, Hoang, Duc, Pedro, Kevin, Pierini, Maurizio, Rankin, Dylan, Sagear, Sheila, Summers, Sioni, Tran, Nhan, Jindariani, Sergo, Wu, Zhenbin, Liu, Mia, Di Guglielmo, Giuseppe, Kreinar, Edward
Publikováno v:
Machine Learning: Science and Technology
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::060c0fe3b4feb74db65978af140fffdd
http://arxiv.org/abs/2003.06308
http://arxiv.org/abs/2003.06308