Reply to Lee, S.Y. Comment on “Sung et al. Body Fat Reduction Effect of Bifidobacterium breve B-3: A Randomized, Double-Blind, Placebo Comparative Clinical Trial. Nutrients 2023, 15, 28”

Sung, Hyun-Kyung; Youn, Sang Jun; Choi, Yong; Eun, Sangwon; Shin, Seon Mi

doi:10.3390/nu15051094

Cited by 3 publications

(1 citation statement)

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“…Energy efficiency and data throughput are essential metrics for any tracking solution currently being developed for the ATLAS Event Filter system. Previous studies of implementing graph neural networks on FPGAs for high energy physics applications show a large potential for speed-up and energy savings compared to CPU and GPU implementations [22][23][24], though many of these studies target low latency applications with tighter constraints than the Event Filter system. However, this does not diminish the challenge of retaining the performance of e.g.…”

Section: Track Reconstruction With Graph Neural Network On Fpgasmentioning

confidence: 99%

Track reconstruction for the ATLAS Phase-II Event Filter using GNNs on FPGAs

Dittmeier

2024

EPJ Web of Conf.

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The High-Luminosity LHC (HL-LHC) will provide an order of magnitude increase in integrated luminosity and enhance the discovery reach for new phenomena. The increased pile-up necessitates major upgrades to the ATLAS detector and trigger. The Phase-II trigger will consist of two levels, a hardware-based Level-0 trigger and an Event Filter (EF) with tracking capabilities. Within the Trigger and Data Acquisition group, a heterogeneous computing farm consisting of CPUs and potentially GPUs and/or FPGAs is under study, together with the use of modern machine learning algorithms such as Graph Neural Networks (GNNs). GNNs are a powerful class of geometric deep learning methods for modelling spatial dependencies via message passing over graphs. They are well-suited for track reconstruction tasks by learning on an expressive structured graph representation of hit data and considerable speedup over CPU-based execution is possible on FPGAs. The focus of this publication is a study of track reconstruction for the Phase-II EF system using GNNs on FPGAs. We explore each of the steps in a GNN-based EF tracking pipeline: graph construction, edge classification using an interaction network, and track reconstruction. Several methods and hardware platforms are under evaluation, studying resource utilisation and minimization of model size using quantization aware training, while simultaneously retaining high track reconstruction efficiency and low fake rates required for the EF tracking system.

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Section: Track Reconstruction With Graph Neural Network On Fpgasmentioning

confidence: 99%