77 citations · 84 across the 6 of their papers we have counts for
7 papers
Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade
Zhixing Jiang, Scott Hauck, Dennis Yin +7
The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine…
Snowmass Topical Group Summary Report: IF04 -- Trigger and Data Acquisition Systems
Darin Acosta, Allison Deiana, Wesley Ketchum
A trend for future high energy physics experiments is an increase in the data bandwidth produced from the detectors. Datasets of the Petabyte scale have already become the norm, an…
Report of the Instrumentation Frontier Working Group for Snowmass 2021
Phillip S. Barbeau, Petra Merkel, Jinlong Zhang +42
Detector instrumentation is at the heart of scientific discoveries. Cutting edge technologies enable US particle physics to play a leading role worldwide. This report summarizes th…
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…
A prototype optical link board with redundancy design for the ATLAS liquid argon calorimeter Phase-2 upgrade
X. Huang, C. Liu, A. Deiana +9
A prototype optical-link board has been developed for the ATLAS Liquid Argon Calorimeter Phase-2 upgrade. The board consists of 24 lpGBT chips and 8 VTRx+ modules and demonstrates…
The Phase-I Trigger Readout Electronics Upgrade of the ATLAS Liquid Argon Calorimeters
G. Aad, A. V. Akimov, K. Al Khoury +195
The Phase-I trigger readout electronics upgrade of the ATLAS Liquid Argon calorimeters enhances the physics reach of the experiment during the upcoming operation at increasing Larg…
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…