Paper Citation Record · LEDGER

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs

As of 9 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2606.30382.

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

pith.paper-citation-record.v1
2606.30382 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T03:36:26.865685Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact5
verified fuzzy0
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 63c46920-b783-46e2-9378-6fe410240796 · outbound

This paper cites Edge AI: a survey.Internet of Things and Cyber-Physical Systems, 3:71–92, 2023.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Edge AI: a survey.Internet of Things and Cyber-Physical Systems, 3:71–92, 2023

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb04d004-cd0a-4f0a-807d-d5d4980c1329 · outbound

This paper cites Fast inference of deep neural networks in FPGAs for particle physics.Journal of instrumentation, 13(07):P07027, 2018.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Fast inference of deep neural networks in FPGAs for particle physics.Journal of instrumentation, 13(07):P07027, 2018

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c2c8a6e-88ba-494e-b591-65b0ca154d81 · outbound

This paper cites End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml.IEEE Transactions on Quantum Engineering, 2025.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml.IEEE Transactions on Quantum Engineering, 2025

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1ec4a63-02e0-4b46-b1a3-76cbd675d595 · outbound

This paper cites An FPGA-based high- frequency trading system for 10 gigabit ethernet with a latency of 433 ns.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs An FPGA-based high- frequency trading system for 10 gigabit ethernet with a latency of 433 ns

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dbbb7d4-f6fc-4572-943c-b217e5613fbd · outbound

This paper cites LogicNets: Co-designed neural networks and circuits for extreme- throughput applications.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs LogicNets: Co-designed neural networks and circuits for extreme- throughput applications

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f81a740d-cd5d-4a7a-a5f8-bb16f8fb6429 · outbound

This paper cites Lutnet: Rethinking inference in FPGA soft logic.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Lutnet: Rethinking inference in FPGA soft logic

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f12c5250-dccc-427a-a53c-b1e4a3645678 · outbound

This paper cites PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:25:47.440977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1091c7b0-de89-4592-a370-5710141129aa · outbound

This paper cites Neuralut: Hiding neural network density in boolean synthesizable functions.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Neuralut: Hiding neural network density in boolean synthesizable functions

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c31eed40-feb3-4944-8d85-93b7d592249a · outbound

This paper cites Neuralut-assemble: Hardware-aware assembling of sub-neural networks for efficient lut inference.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Neuralut-assemble: Hardware-aware assembling of sub-neural networks for efficient lut inference

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c56587e-64b8-4c87-8193-922c069c9c35 · outbound

This paper cites Greater than the sum of its luts: Scaling up lut-based neural networks with amigolut.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Greater than the sum of its luts: Scaling up lut-based neural networks with amigolut

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation badbbdb8-e6f3-49ce-8652-d50ffb07a742 · outbound

This paper cites Ps and qs: Quantization- aware pruning for efficient low latency neural network inference.Fron- tiers in Artificial Intelligence, 4:676564, 2021.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Ps and qs: Quantization- aware pruning for efficient low latency neural network inference.Fron- tiers in Artificial Intelligence, 4:676564, 2021

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61371dee-dd75-456f-822d-ab2b8f2f87a9 · outbound

This paper cites HGQ: High granularity quantization for real-time neural networks on FPGAs.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs HGQ: High granularity quantization for real-time neural networks on FPGAs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69b6a542-256c-4370-b344-2794d97967c1 · outbound

This paper cites Optimal brain damage.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Optimal brain damage

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f3dd27c-5e73-4b0d-ae92-b63cd89ecc41 · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-01T15:25:47.431476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4b810503-a9f1-466c-968c-3c567200f4fa · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow.Advances in neural information processing systems, 33:6377–6389, 2020.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Pruning neural networks without any data by iteratively conserving synaptic flow.Advances in neural information processing systems, 33:6377–6389, 2020

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1ac8f57-f191-486a-bdd6-683e58ba9776 · outbound

This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:25:47.438055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d6a7a470-9242-4415-bff9-c127ee9240ef · outbound

This paper cites Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:25:47.434852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 73470dbe-9f72-4d72-8a11-91830699e732 · outbound

This paper cites Sub-microsecond Transformers for Jet Tagging on FPGAs.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Sub-microsecond Transformers for Jet Tagging on FPGAs

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:25:47.444658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 716387bf-2c04-41ff-ba67-4d6454e0d887 · outbound

This paper cites hls4ml LHC Jets HLF (OpenML Dataset 42468).

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs hls4ml LHC Jets HLF (OpenML Dataset 42468)

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e3a9ce5-cd56-4671-ab4d-d90dde6348f9 · outbound

This paper cites CERNBox LHC Jets Dataset.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs CERNBox LHC Jets Dataset

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6188231f-59e8-467b-84d6-11df663ae8a1 · outbound

This paper cites da4ml: Distributed arithmetic for real-time neural networks on FPGAs.ACM Transactions on Reconfigurable Technology and Systems, 2025.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs da4ml: Distributed arithmetic for real-time neural networks on FPGAs.ACM Transactions on Reconfigurable Technology and Systems, 2025

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2222a23-b6a2-43ef-8b68-9764da472ef8 · outbound

This paper cites ReducedLUT: Table Decomposition with” Don’t Care” Conditions.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs ReducedLUT: Table Decomposition with” Don’t Care” Conditions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed8bdc1c-2193-4f77-882c-b03fac5e1b8b · outbound

This paper cites Automatic heterogeneous quan- tization of deep neural networks for low-latency inference on the edge for particle detectors.Nature Machine Intelligence, 3(8):675–686, 2021.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Automatic heterogeneous quan- tization of deep neural networks for low-latency inference on the edge for particle detectors.Nature Machine Intelligence, 3(8):675–686, 2021

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ec08ac9-6087-4740-9e14-e85144e03c75 · outbound

This paper cites Polylut-add: FPGA-based LUT inference with wide inputs.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Polylut-add: FPGA-based LUT inference with wide inputs

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

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Unavailable: canonical work link unavailable.

Observation 8baa4c5c-8e72-4b61-af79-397d03f980aa · outbound

This paper cites Polylut: Ultra-low latency polynomial inference with hardware-aware structured pruning.IEEE Transactions on Computers, 2025.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Polylut: Ultra-low latency polynomial inference with hardware-aware structured pruning.IEEE Transactions on Computers, 2025

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-30T03:36:26.865685Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.