Paper Citation Record · LEDGER

Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2203.05025.

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

pith.paper-citation-record.v1
2203.05025 v1

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 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:07:23.004840Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

16
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0aef0ee6-08d3-4cc0-bac2-5c3d61fce8f3 · inbound

A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications cites this paper.

A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T23:07:23.004840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0f24342-db3b-4382-9cef-3d8ef37ec080 · inbound

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data cites this paper.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:19:35.866132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89ada610-963c-498f-8888-7b66aafe082e · inbound

Power-of-Two (PoT) Weights in Large Language Models (LLMs) cites this paper.

Power-of-Two (PoT) Weights in Large Language Models (LLMs) Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:12:12.610791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b02f7ada-f2de-4ffc-9052-1fe78628b0a5 · inbound

PoTPTQ: A Two-step Power-of-Two Post-training for LLMs cites this paper.

PoTPTQ: A Two-step Power-of-Two Post-training for LLMs Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:06:35.789384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8acb6229-e923-4a9f-9c3c-873ec0b23145 · inbound

GRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware Accelerators cites this paper.

GRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware Accelerators Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T20:47:51.706464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c0290fa-5ad1-498e-a354-af76bd70cde9 · inbound

Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks cites this paper.

Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:33:16.898978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b7bf0810-8196-4a9d-a50a-692359acfe7b · inbound

ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization cites this paper.

ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:55:31.955496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f3763ec-79eb-46a7-8bcc-8bf3d49a77cd · inbound

ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA cites this paper.

ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:45:22.485425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 476ba736-9b7b-4886-aa4e-6cbbdd36711e · inbound

PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs cites this paper.

PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:51:17.826453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 684bbdb5-8870-4384-969d-3c2f6743dfa3 · inbound

$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic Space cites this paper.

$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic Space Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:36:55.866065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.