T. Aarrestad

activity

20202026

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

196 citations · 763 across the 20 of their papers we have counts for

collaborators

Showing 2020Show all

3 papers · 1 filter

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…

hep-ex2020

Searching for diboson resonances in the boosted all-hadronic final state at $s = 13$ TeV with CMS

Thea Aarrestad

This article summarises three searches for diboson resonances in the all-hadronic final state using data collected at a center-of-mass energy of $s = 13$ TeV with the CMS…

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…

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

Searching for diboson resonances in the boosted all-hadronic final state at s​=13 TeV with CMS

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

Searching for diboson resonances in the boosted all-hadronic final state at $s = 13$ TeV with CMS