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Paper Citation Record · LEDGER

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2103.05579.

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

pith.paper-citation-record.v1
2103.05579 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:02:39.307671Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T05:16:38.809864Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e06cf657-006b-4937-ad6b-15144e0102ec · inbound

da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs cites this paper.

da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T05:32:05.824485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T05:28:59.077993Z digest=sha256:cb48002e1a3a321952ba0494a0c51a4658c21da6811c18872678e429ef4a0591

Observation 651d4507-f7b3-46ff-b558-6e3b0d11ef48 · inbound

Neural Network Acceleration on MPSoC board: Integrating SLAC's SNL, Rogue Software and Auto-SNL cites this paper.

Neural Network Acceleration on MPSoC board: Integrating SLAC's SNL, Rogue Software and Auto-SNL hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-05T14:02:39.307671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:02:39.307671Z digest=sha256:11d7dfb31490c8fe4b4e46cd1fff0da17a15e19d21d0ac08c22e8bc57bac4bb9

Observation 0854bfaf-0dab-4463-bed1-f376e9c53386 · inbound

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation cites this paper.

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-03T23:44:05.757102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:44:05.757102Z digest=sha256:f8f2e15816507f9a8871e5705e85f17dbc6a72507258fb2848b4a32c258bcf8d

Observation 03fb3044-363c-4a22-b3f6-10a116dee780 · inbound

KANEL\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation cites this paper.

KANEL\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-03T16:38:07.875843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:38:07.875843Z digest=sha256:a08c7db0b045ccc97ce4ddaac68319bd4d48bc3b8b2d20bb3bfd2f1736f209be

Observation 4763eb8e-7508-4b52-a33a-959fcada76bd · inbound

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml cites this paper.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-03T02:38:13.406043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:38:13.406043Z digest=sha256:31f6e7f43ddc244c78ad88e1aab6a6ae4dbbf0f47fec4f2bd26a130feac3ddf6

Observation f5ce19b0-063b-4eaa-ab76-b8cf9c568906 · inbound

On-chip probabilistic inference for charged-particle tracking at the sensor edge cites this paper.

On-chip probabilistic inference for charged-particle tracking at the sensor edge hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-15T21:40:21.195016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-15T21:37:21.829563Z digest=sha256:41cbf2bd27eb410e13f0630e7d68647e48ba5f7165472604bbc816aeaf99b6f9

Observation ca909962-dd52-4fca-93ea-2241e6f6559d · inbound

Design Rules for Extreme-Edge Scientific Computing on AI Engines cites this paper.

Design Rules for Extreme-Edge Scientific Computing on AI Engines hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:06:05.586636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T02:19:21.143588Z digest=sha256:ed10ec85c59a1e8d846ecc4add6dc4464f48bbb74c0f8da425313e859d65711b

Observation b15990a0-a00e-492e-9ba5-f2afb6428f8b · inbound

HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference cites this paper.

HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:21:08.835480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T09:33:37.506415Z digest=sha256:8f10a8ed9e0ffa8f167b1fcb4df1bab82195f57b1d80b11a94298753e1f6175c

Observation f4fad903-ee1f-46e4-b02d-6af121246afd · inbound

WaveDriver: a Laser Guide Star AO System for HWO cites this paper.

WaveDriver: a Laser Guide Star AO System for HWO hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-20T07:43:08.397153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-20T07:41:30.397198Z digest=sha256:5e117be7c64331828f90abce291a84e7846bd8ed93325f87a3a89b68690c73fe

Observation 330d6e63-627c-4613-8eb8-585d2ec3b82a · inbound

The impact of source and survey modelling on the connection between [O III] emitters and Ly $\alpha$ forest transmission at z ~ 6 cites this paper.

The impact of source and survey modelling on the connection between [O III] emitters and Ly $\alpha$ forest transmission at z ~ 6 hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 172

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T05:16:38.811653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-28T08:30:12.037696Z digest=sha256:7934c737573ac834030834639051cdb9a0b32d99dbb973eb4d9790ac13288b7e