Paper Citation Record · LEDGER

FP8 versus INT8 for efficient deep learning inference

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2303.17951.

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

pith.paper-citation-record.v1
2303.17951 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:35:55.927509Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:06:26.126499Z

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 9f6ff70b-9dcf-4760-b790-c0e63e647dc1 · inbound

A Power-Efficient Hardware Implementation of L-Mul cites this paper.

A Power-Efficient Hardware Implementation of L-Mul FP8 versus INT8 for efficient deep learning inference

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T04:24:03.825093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a62e610-6562-4d3f-8266-de584b3ecf2e · inbound

Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing cites this paper.

Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing FP8 versus INT8 for efficient deep learning inference

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T05:32:34.852822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40a72d16-bfc1-49c1-a365-f495bba7c8a8 · inbound

Resource-Efficient Language Models: Quantization for Fast and Accessible Inference cites this paper.

Resource-Efficient Language Models: Quantization for Fast and Accessible Inference FP8 versus INT8 for efficient deep learning inference

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:07.846294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eb5b9e2-2d15-4d7e-a109-12e46ba393d6 · inbound

Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference cites this paper.

Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference FP8 versus INT8 for efficient deep learning inference

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:05.626348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5530f14-8c65-472e-b013-bfafdf5d73b6 · inbound

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning cites this paper.

Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning FP8 versus INT8 for efficient deep learning inference

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:45.220121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ff0c0d9-aa1c-404e-9a8d-bacc5aa10192 · inbound

Analysis of Floating-Point Matrix Multiplication Computed via Integer Arithmetic cites this paper.

Analysis of Floating-Point Matrix Multiplication Computed via Integer Arithmetic FP8 versus INT8 for efficient deep learning inference

Reference 50

Resolution: malformed identifier; arxiv_id, observed 2026-05-19T09:12:14.853141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a0879e73-781c-4dcd-a536-8001cd9782b6 · inbound

P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formats cites this paper.

P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formats FP8 versus INT8 for efficient deep learning inference

Reference 70

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T00:20:32.176875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2181953e-4a3c-40af-bae4-5cebd2f7a20d · inbound

SnapMLA: Efficient Long-Context MLA Decoding via Hardware-Aware FP8 Quantized Pipelining cites this paper.

SnapMLA: Efficient Long-Context MLA Decoding via Hardware-Aware FP8 Quantized Pipelining FP8 versus INT8 for efficient deep learning inference

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T06:00:40.804585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6e5290f6-f705-442f-a0db-af0c68c9fe8b · inbound

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats cites this paper.

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats FP8 versus INT8 for efficient deep learning inference

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:06:26.128760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9278231b-8ec9-4fc8-a0cb-8329141765f4 · inbound

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats cites this paper.

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats FP8 versus INT8 for efficient deep learning inference

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-15T10:56:54.155632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adbd1a6a-5354-443e-b6e1-14b8cbc2bca6 · inbound

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference cites this paper.

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference FP8 versus INT8 for efficient deep learning inference

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-01T17:12:37.713808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 141eceff-fef8-4451-83d0-7da8968eb6ed · inbound

Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra cites this paper.

Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra FP8 versus INT8 for efficient deep learning inference

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T00:35:55.927509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.