Paper Citation Record · LEDGER

Neural Network Quantization for Efficient Inference: A Survey

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2112.06126.

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

pith.paper-citation-record.v1
2112.06126 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:26:47.127024Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T19:33:55.748295Z

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 2d118b4c-5729-4657-876c-cab429e244d6 · inbound

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices cites this paper.

Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices Neural Network Quantization for Efficient Inference: A Survey

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T04:52:43.752151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21e09a4c-352b-430e-8a12-4d6480de9e8a · inbound

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs cites this paper.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs Neural Network Quantization for Efficient Inference: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.127024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b7b93d1-975e-4ebc-86af-4ed9e49e2b90 · inbound

Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression cites this paper.

Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression Neural Network Quantization for Efficient Inference: A Survey

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T08:41:08.413337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 784cdaf8-6ab5-4037-82e6-9e1e44c2de02 · inbound

SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign cites this paper.

SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign Neural Network Quantization for Efficient Inference: A Survey

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:33:55.751909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c60f0486-b71f-4218-9cf2-cf0a4cc3135f · inbound

Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic cites this paper.

Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic Neural Network Quantization for Efficient Inference: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-11T17:52:29.131365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.