Paper Citation Record · LEDGER

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 2 inbound Pith citation observations for arXiv:2502.02304.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.02304 v4

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:40:45.096734Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:55:01.503235Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T00:16:16.768632Z

Reference resolution

25 of 25 outbound references displayed

verified exact5
verified fuzzy14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 750e3ff9-13bb-45ac-bd31-8b2ce87df5d5 · outbound

This paper cites Framework TDR for the LHCb Upgrade : Technical Design Report,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Framework TDR for the LHCb Upgrade : Technical Design Report,

Reference 1

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Observation 546927e8-bf27-4baf-946e-b061bbcff3aa · outbound

This paper cites LHCb VELO Upgrade Technical Design Report,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb LHCb VELO Upgrade Technical Design Report,

Reference 2

Resolution: verified exact; raw_fallback, observed 2026-08-09T12:40:45.641979Z

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Observation 21d24a9b-949c-4919-9d09-682f9d180437 · outbound

This paper cites LHCb Upgrade GPU High Level Trigger Technical Desig n Report,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb LHCb Upgrade GPU High Level Trigger Technical Desig n Report,

Reference 3

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Observation 3735af03-9ab6-4912-811d-2e64a340f49b · outbound

This paper cites Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?

Reference 4

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Observation a3b08196-8b71-4a68-aa32-765cb56e6a74 · outbound

This paper cites Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Network s,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Network s,

Reference 5

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Observation 30b82d1d-3085-40f9-a081-447f26681f98 · outbound

This paper cites Boosted Decision Trees in the Level-1 Muon Endcap Trigger at CMS,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Boosted Decision Trees in the Level-1 Muon Endcap Trigger at CMS,

Reference 6

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Observation c12f58ad-9ff6-4b0c-846d-9dbc02de21a9 · outbound

This paper cites Fast and Resource-Efﬁcient Deep Neural Netwo rk on FPGA for the Phase-II Level-0 Muon Barrel Trigger of the A TLAS Exp eri- ment,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Fast and Resource-Efﬁcient Deep Neural Netwo rk on FPGA for the Phase-II Level-0 Muon Barrel Trigger of the A TLAS Exp eri- ment,

Reference 7

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Observation fdb082aa-6dc9-4bf3-b83a-29639cac63d1 · outbound

This paper cites Physics Case for an LHCb Upgrade II - Opportunities in Flavour Physics, and Beyond, in the HL-LHC Era,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Physics Case for an LHCb Upgrade II - Opportunities in Flavour Physics, and Beyond, in the HL-LHC Era,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.948015Z

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Observation fd508b4e-5021-4cf6-b2d6-59c8d0c8da45 · outbound

This paper cites Fast inference of deep neural networks in FPGAs for particle physics.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Fast inference of deep neural networks in FPGAs for particle physics

Reference 9

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Observation 8ae322fe-62aa-4729-b752-b65a407fc2eb · outbound

This paper cites an unresolved cited work.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Unresolved cited work

Reference 10

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Source-reported events for the cited work

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Observation b5b81adb-7008-4334-af26-91c6de895e3a · outbound

This paper cites Keras: Deep Learning for Humans.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Keras: Deep Learning for Humans

Reference 11

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Observation 4956ba58-fc8d-40d3-8cf0-23515c9d8aaf · outbound

This paper cites TensorFlow: A System for Large-Scale Machine Learning,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb TensorFlow: A System for Large-Scale Machine Learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.904083Z

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Observation be4afc64-d6f2-46e9-b770-8bfd9c9800d8 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb PyTorch: An Imperative Style, High-Performance Deep Learning Library,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.890042Z

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Observation db1ff57a-b5c0-4b33-8851-54f1fbfa4e83 · outbound

This paper cites Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking,

Reference 14

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Observation db450c95-3300-4442-8246-b27e1ffb9a98 · outbound

This paper cites Graph Neural Network-Based Track Finding in the LHCb V erte x Detector,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Graph Neural Network-Based Track Finding in the LHCb V erte x Detector,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.875243Z

Source-reported events for the cited work

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Observation 21ea1236-44ce-4d13-9ac5-6cd94bbf92e8 · outbound

This paper cites Graph Neural Network-Based Pipeline for Track Fin ding in the VELO at LHCb,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Graph Neural Network-Based Pipeline for Track Fin ding in the VELO at LHCb,

Reference 16

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Observation e7cc4695-e9c7-49a6-af64-586439664747 · outbound

This paper cites NVIDIA TensorRT,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb NVIDIA TensorRT,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.846608Z

Source-reported events for the cited work

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Observation c7ae9584-5b49-4701-afda-0d04e4247a97 · outbound

This paper cites PyTorch-Quantization: Training and Eval- uating PyTorch Models with Simulated Quantization.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb PyTorch-Quantization: Training and Eval- uating PyTorch Models with Simulated Quantization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.832051Z

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Observation 974d2a60-d565-45ef-9fbd-32d8ce578210 · outbound

This paper cites PYNQ - Python Productivity to AMD Adap tive Compute Platforms.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb PYNQ - Python Productivity to AMD Adap tive Compute Platforms

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.816699Z

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Observation ab232e40-9560-4a8d-bfb2-4143219bfe34 · outbound

This paper cites Vivado HLS.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Vivado HLS

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.800759Z

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Observation e2af0c54-6549-40fa-931d-02628d635513 · outbound

This paper cites Alveo U250 Data Center Accelerator Card.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Alveo U250 Data Center Accelerator Card

Reference 21

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Observation 0eb10979-74a2-4ed9-a397-454bf0760a5c · outbound

This paper cites NVIDIA GeForce RTX 3090 Speciﬁcations,.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb NVIDIA GeForce RTX 3090 Speciﬁcations,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.771565Z

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Observation d8659070-fddf-4de6-b679-f3f85f2f6627 · outbound

This paper cites Alveo U50 Data Center Accelerator Car d.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Alveo U50 Data Center Accelerator Car d

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.757385Z

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Observation 2725286f-34ee-4bb3-9185-d543da183708 · outbound

This paper cites Energy Dashboard.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Energy Dashboard

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:40:45.741771Z

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Observation 01699d42-3b9a-42f6-98d3-7a5aa95c42c1 · outbound

This paper cites Available: https://dx.doi.org/10.1088/ 1748-0221/19/12/ P12022.

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Available: https://dx.doi.org/10.1088/ 1748-0221/19/12/ P12022

Reference 2024

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Pith citing papers

Observation cf2af3bf-5e24-43da-9695-cdeb76ac5d38 · inbound

Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb cites this paper.

Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb

Reference 60

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Observation 2eacf15e-5a19-4ade-93eb-dc34611c5c06 · inbound

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures cites this paper.

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb

Reference 13

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