Paper Citation Record · LEDGER

FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2207.07958.

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

pith.paper-citation-record.v1
2207.07958 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:18:10.331039Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T13:06:05.550926Z

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 e588f07b-ddbc-4345-9919-554dd14ead35 · inbound

Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition cites this paper.

Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T18:18:10.331039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb295f5d-0617-4fa8-b594-812764a165e2 · 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 FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-05T22:14:24.069395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fdfc673-40db-4544-ae5e-da6be270856c · 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 FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T23:44:05.578748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12668a66-826d-4dea-a82a-ba78b59568ac · inbound

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

Design Rules for Extreme-Edge Scientific Computing on AI Engines FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:06:05.554194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.