BibTex format
@article{Odagiu:2024:2632-2153/ad5f10,
author = {Odagiu, P and Que, Z and Duarte, J and Haller, J and Kasieczka, G and Lobanov, A and Loncar, V and Luk, W and Ngadiuba, J and Pierini, M and Rincke, P and Seksaria, A and Summers, S and Sznajder, A and Tapper, A and Årrestad, TK},
doi = {2632-2153/ad5f10},
journal = {Machine Learning: Science and Technology},
title = {Ultrafast jet classification at the HL-LHC},
url = {http://dx.doi.org/10.1088/2632-2153/ad5f10},
volume = {5},
year = {2024}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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 input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN large hadron collider during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that O(100) ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.
AU - Odagiu,P
AU - Que,Z
AU - Duarte,J
AU - Haller,J
AU - Kasieczka,G
AU - Lobanov,A
AU - Loncar,V
AU - Luk,W
AU - Ngadiuba,J
AU - Pierini,M
AU - Rincke,P
AU - Seksaria,A
AU - Summers,S
AU - Sznajder,A
AU - Tapper,A
AU - Årrestad,TK
DO - 2632-2153/ad5f10
PY - 2024///
SN - 2632-2153
TI - Ultrafast jet classification at the HL-LHC
T2 - Machine Learning: Science and Technology
UR - http://dx.doi.org/10.1088/2632-2153/ad5f10
UR - http://hdl.handle.net/10044/1/114044
VL - 5
ER -