A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network (NN) autoencoder model can be imp...

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Detalles Bibliográficos
Autores principales: Di Guglielmo, Giuseppe, Fahim, Farah, Herwig, Christian, Valentin, Manuel Blanco, Duarte, Javier, Gingu, Cristian, Harris, Philip, Hirschauer, James, Kwok, Martin, Loncar, Vladimir, Luo, Yingyi, Miranda, Llovizna, Ngadiuba, Jennifer, Noonan, Daniel, Ogrenci-Memik, Seda, Pierini, Maurizio, Summers, Sioni, Tran, Nhan
Lenguaje:eng
Publicado: 2021
Materias:
hep-ex
Particle Physics - Experiment
cs.LG
Computing and Computers
physics.ins-det
Detectors and Experimental Techniques
Acceso en línea:https://dx.doi.org/10.1109/TNS.2021.3087100
http://cds.cern.ch/record/2770527

Internet

https://dx.doi.org/10.1109/TNS.2021.3087100
http://cds.cern.ch/record/2770527

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