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20202024
most citedAutomatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

196 citations · 823 across the 16 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

hep-ex2020★ 32 cited

Search for new physics in top quark production with additional leptons in proton-proton collisions at 13 TeV using effective field theory

CMS Collaboration

Events containing one or more top quarks produced with additional prompt leptons are used to search for new physics within the framework of an effective field theory (EFT). The dat…

physics.ins-det2020★ 32 cited

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

Aneesh Heintz, Vesal Razavimaleki, Javier Duarte +18

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 fram…

physics.comp-ph2020

HL-LHC Computing Review: Common Tools and Community Software

HEP Software Foundation, :, Thea Aarrestad +107

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will b…

physics.ins-det2020★ 50 cited

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta +19

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…

physics.ins-det2020★ 196 cited

Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Claudionor N. Coelho, Aki Kuusela, Shan Li +7

Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore r…

cs.LG2020★ 114 cited

Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML

Giuseppe Di Guglielmo, Javier Duarte, Philip Harris +13

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 FPG…