26TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY & NUCLEAR PHYSICS (CHEP2023)

Fast inference on FPGA for the ATLAS Muon Trigger

Not scheduled

1h

Hampton Roads Ballroom and Foyer Area (Norfolk Waterside Marriott)

Poster Poster Poster Session

Speaker

Carnesale, Maria

Description

Track finding in high-density environments is a key challenge for experiments at mod-
ern accelerators. In this presentation we describe the performance obtained running
machine learning models studied for the ATLAS Muon High Level Trigger. These mod-
els are designed for hit position reconstruction and track pattern recognition with a
tracking detector, on a commercially available Xilinx Alveo U50 and Alveo U250. We
compare the inference times obtained on a CPU, on a GPU and on the Alveo cards.
These tests are done using TensorFlow libraries as well as the TensorRT framework,
and software frameworks for AI-based applications acceleration. The inference times
obtained are compared to the needs of present and future experiments at LHC.

Presentation materials

CHEP_MC.pdf