Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2203.05025.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:19:35.866132Z
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 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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.