Paper Citation Record · LEDGER

Pruning and Quantization for Deep Neural Network Acceleration: A Survey

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2101.09671.

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

pith.paper-citation-record.v1
2101.09671 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:02:16.419474Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T15:45:48.364216Z

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 44123b4e-d907-49bf-82a0-b962070711d7 · inbound

Neural Architecture Codesign for Fast Physics Applications cites this paper.

Neural Architecture Codesign for Fast Physics Applications Pruning and Quantization for Deep Neural Network Acceleration: A Survey

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:54.035691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28b890b2-6d1c-43ac-ab87-e0eeb80b26a5 · inbound

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments cites this paper.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Pruning and Quantization for Deep Neural Network Acceleration: A Survey

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.419474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fedaca92-9384-4feb-ad7b-de90d7c20074 · inbound

Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees cites this paper.

Train-Once Plan-Anywhere Kinodynamic Motion Planning via Diffusion Trees Pruning and Quantization for Deep Neural Network Acceleration: A Survey

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T14:42:40.270613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24c9c73c-6729-45a1-b3a5-b266ece0f77e · inbound

SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models cites this paper.

SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models Pruning and Quantization for Deep Neural Network Acceleration: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T19:47:08.845268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46126293-59fa-4a8f-9c87-db6e49d3a900 · inbound

The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks cites this paper.

The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks Pruning and Quantization for Deep Neural Network Acceleration: A Survey

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:45:48.365848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bfe2f797-66a8-451a-9e54-d3a0fb532862 · inbound

The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks cites this paper.

The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks Pruning and Quantization for Deep Neural Network Acceleration: A Survey

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:35:40.048425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.