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20142023
most citedFast inference of deep neural networks in FPGAs for particle physics

438 citations · 3.2k across the 52 of their papers we have counts for

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5 papers · 1 filter

physics.ins-det2022★ 6 cited

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Gabriella Carini, Grzegorz Deptuch, Jennet Dickinson +19

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noi…

physics.ins-det2021★ 56 cited

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

Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig +15

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…

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.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-det2018★ 438 cited

Fast inference of deep neural networks in FPGAs for particle physics

Javier Duarte, Song Han, Philip Harris +8

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 learni…