Zobrazeno 1 - 10
of 1 685
pro vyhledávání: '"Summers , Sioni"'
Autor:
Odagiu, Patrick, Que, Zhiqiang, Duarte, Javier, Haller, Johannes, Kasieczka, Gregor, Lobanov, Artur, Loncar, Vladimir, Luk, Wayne, Ngadiuba, Jennifer, Pierini, Maurizio, Rincke, Philipp, Seksaria, Arpita, Summers, Sioni, Sznajder, Andre, Tapper, Alexander, Aarrestad, Thea K.
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:
http://arxiv.org/abs/2402.01876
The Phase-2 Upgrade of the CMS Level-1 Trigger (L1T) will reconstruct particles using the Particle Flow algorithm, connecting information from the tracker, muon, and calorimeter detectors, and enabling fine-grained reconstruction of high level physic
Externí odkaz:
http://arxiv.org/abs/2310.08062
Publikováno v:
EPJ Web of Conferences. 5/6/2024, Vol. 295, p1-8. 8p.
Autor:
Khoda, Elham E, Rankin, Dylan, de Lima, Rafael Teixeira, Harris, Philip, Hauck, Scott, Hsu, Shih-Chieh, Kagan, Michael, Loncar, Vladimir, Paikara, Chaitanya, Rao, Richa, Summers, Sioni, Vernieri, Caterina, Wang, Aaron
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:
http://arxiv.org/abs/2207.00559
Autor:
Pappalardo, Alessandro, Umuroglu, Yaman, Blott, Michaela, Mitrevski, Jovan, Hawks, Ben, Tran, Nhan, Loncar, Vladimir, Summers, Sioni, Borras, Hendrik, Muhizi, Jules, Trahms, Matthew, Hsu, Shih-Chieh, Hauck, Scott, Duarte, Javier
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce support for low precision quantization in existing ONNX-based quantizat
Externí odkaz:
http://arxiv.org/abs/2206.07527
Autor:
Ghielmetti, Nicolò, Loncar, Vladimir, Pierini, Maurizio, Roed, Marcel, Summers, Sioni, Aarrestad, Thea, Petersson, Christoffer, Linander, Hampus, Ngadiuba, Jennifer, Lin, Kelvin, Harris, Philip
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:
http://arxiv.org/abs/2205.07690
Autor:
Pol, Adrian Alan, Aarrestad, Thea, Govorkova, Ekaterina, Halily, Roi, Klempner, Anat, Kopetz, Tal, Loncar, Vladimir, Ngadiuba, Jennifer, Pierini, Maurizio, Sirkin, Olya, Summers, Sioni
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:
http://arxiv.org/abs/2202.04499
Autor:
Deiana, Allison McCarn, Tran, Nhan, Agar, Joshua, Blott, Michaela, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hauck, Scott, Liu, Mia, Neubauer, Mark S., Ngadiuba, Jennifer, Ogrenci-Memik, Seda, Pierini, Maurizio, Aarrestad, Thea, Bahr, Steffen, Becker, Jurgen, Berthold, Anne-Sophie, Bonventre, Richard J., Bravo, Tomas E. Muller, Diefenthaler, Markus, Dong, Zhen, Fritzsche, Nick, Gholami, Amir, Govorkova, Ekaterina, Hazelwood, Kyle J, Herwig, Christian, Khan, Babar, Kim, Sehoon, Klijnsma, Thomas, Liu, Yaling, Lo, Kin Ho, Nguyen, Tri, Pezzullo, Gianantonio, Rasoulinezhad, Seyedramin, Rivera, Ryan A., Scholberg, Kate, Selig, Justin, Sen, Sougata, Strukov, Dmitri, Tang, William, Thais, Savannah, Unger, Kai Lukas, Vilalta, Ricardo, Krosigk, Belinavon, Warburton, Thomas K., Flechas, Maria Acosta, Aportela, Anthony, Calvet, Thomas, Cristella, Leonardo, Diaz, Daniel, Doglioni, Caterina, Galati, Maria Domenica, Khoda, Elham E, Fahim, Farah, Giri, Davide, Hawks, Benjamin, Hoang, Duc, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Johnson, Iris, Kansal, Raghav, Kastner, Ryan, Katsavounidis, Erik, Krupa, Jeffrey, Li, Pan, Madireddy, Sandeep, Marx, Ethan, McCormack, Patrick, Meza, Andres, Mitrevski, Jovan, Mohammed, Mohammed Attia, Mokhtar, Farouk, Moreno, Eric, Nagu, Srishti, Narayan, Rohin, Palladino, Noah, Que, Zhiqiang, Park, Sang Eon, Ramamoorthy, Subramanian, Rankin, Dylan, Rothman, Simon, Sharma, Ashish, Summers, Sioni, Vischia, Pietro, Vlimant, Jean-Roch, Weng, Olivia
Publikováno v:
Front. Big Data 5, 787421 (2022)
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.
Externí odkaz:
http://arxiv.org/abs/2110.13041
Autor:
Govorkova, Ekaterina, Puljak, Ema, Aarrestad, Thea, James, Thomas, Loncar, Vladimir, Pierini, Maurizio, Pol, Adrian Alan, Ghielmetti, Nicolò, Graczyk, Maksymilian, Summers, Sioni, Ngadiuba, Jennifer, Nguyen, Thong Q., Duarte, Javier, Wu, Zhenbin
Publikováno v:
Nature Machine Intelligence 4, 154 (2022)
In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely challenging environment of a real-time event selection system at the Lar
Externí odkaz:
http://arxiv.org/abs/2108.03986
Autor:
Que, Zhiqiang, Wang, Erwei, Marikar, Umar, Moreno, Eric, Ngadiuba, Jennifer, Javed, Hamza, Borzyszkowski, Bartłomiej, Aarrestad, Thea, Loncar, Vladimir, Summers, Sioni, Pierini, Maurizio, Cheung, Peter Y, Luk, Wayne
This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational interferometers such as the LIGO detectors capture cosmic events such a
Externí odkaz:
http://arxiv.org/abs/2106.14089