Paper Citation Record · LEDGER

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision

As of 18 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2506.11431.

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

pith.paper-citation-record.v1
2506.11431 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:18:09.241612Z

measured 41 of 41 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:53:20.211127Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T11:30:18.609532Z

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 009e068a-e6ee-4451-b549-8b5281906e30 · outbound

This paper cites Dory: Automatic end-to-end deployment of real-world dnns on low-cost iot mcus,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Dory: Automatic end-to-end deployment of real-world dnns on low-cost iot mcus,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.619750Z

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Observation c412955f-981d-4708-8289-9b1f03fbe8eb · outbound

This paper cites Quantization networks,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Quantization networks,

Reference 2

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Observation 293db58b-fc4d-4683-beba-d4052bee30c5 · outbound

This paper cites Post training 4-bit quantization of convolutional networks for rapid-deployment,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Post training 4-bit quantization of convolutional networks for rapid-deployment,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.603640Z

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Observation 12b79af7-02bf-474d-8f5d-6a7c418d90f7 · outbound

This paper cites Up or down? adaptive rounding for post-training quantization,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Up or down? adaptive rounding for post-training quantization,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.593184Z

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Observation c0af4064-7763-49bd-a0dc-a212fd890eb2 · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:18:09.146393Z

Source-reported events for the cited work

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Observation 708b18e9-3c20-4499-89a8-c51c6f1a3ed5 · outbound

This paper cites Slimmable Neural Networks.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Slimmable Neural Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:18:09.149665Z

Source-reported events for the cited work

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Observation 0c7d70f5-063f-40ca-9950-038764374171 · outbound

This paper cites Universally slimmable networks and improved training techniques,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Universally slimmable networks and improved training techniques,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:18:09.153209Z

Source-reported events for the cited work

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Observation 9c598bbb-7478-48e6-8778-bbe945903b5d · outbound

This paper cites Efficientnet: Rethinking model scaling for con- volutional neural networks,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Efficientnet: Rethinking model scaling for con- volutional neural networks,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:18:09.155920Z

Source-reported events for the cited work

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Observation 4d054e4a-d393-486c-a47e-e858b689fcea · outbound

This paper cites BranchyNet: Fast inference via early exiting from deep neural networks,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision BranchyNet: Fast inference via early exiting from deep neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.570172Z

Source-reported events for the cited work

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Observation a9515d5b-6285-4a4a-a381-8333eecc97f8 · outbound

This paper cites Adaptive inference through early-exit networks: Design, challenges and directions,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Adaptive inference through early-exit networks: Design, challenges and directions,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.559325Z

Source-reported events for the cited work

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Observation d695a3d8-ea7e-415f-81bd-b1229ce219d1 · outbound

This paper cites Review and analysis of variable bit-precision mac microarchi- tectures for energy-efficient ai computation,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Review and analysis of variable bit-precision mac microarchi- tectures for energy-efficient ai computation,

Reference 11

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Source-reported events for the cited work

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Observation bc9c030a-df58-4451-9f69-3ce26fda9247 · outbound

This paper cites Review and bench- marking of precision-scalable multiply-accumulate unit architectures for embedded neural-network processing,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Review and bench- marking of precision-scalable multiply-accumulate unit architectures for embedded neural-network processing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.536672Z

Source-reported events for the cited work

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Observation 11d34c85-6ec6-4c2a-8aac-7af75f069184 · outbound

This paper cites Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.525937Z

Source-reported events for the cited work

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Observation 6658da27-9c92-4dc0-b91f-ad7127843c3f · outbound

This paper cites 14.5 en- vision: A 0.26-to-10tops/w subword-parallel dynamic-voltage-accuracy- frequency-scalable convolutional neural network processor in 28nm fdsoi,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision 14.5 en- vision: A 0.26-to-10tops/w subword-parallel dynamic-voltage-accuracy- frequency-scalable convolutional neural network processor in 28nm fdsoi,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.515767Z

Source-reported events for the cited work

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Observation c514425f-2a2b-408d-bfc8-f9a40e3ac869 · outbound

This paper cites Bitblade: Area and energy- efficient precision-scalable neural network accelerator with bitwise summation,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Bitblade: Area and energy- efficient precision-scalable neural network accelerator with bitwise summation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.504664Z

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 236c6347-cb0a-4dcb-be34-6043129a8d1b · outbound

This paper cites FlexiBit: Fully Flexible Precision Bit-parallel Accelerator Architecture for Arbitrary Mixed Precision AI.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision FlexiBit: Fully Flexible Precision Bit-parallel Accelerator Architecture for Arbitrary Mixed Precision AI

Reference 16

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Source-reported events for the cited work

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Observation ec638b13-6959-404a-ab7f-efa21b503cd9 · outbound

This paper cites Eq-net: Elastic quanti- zation neural networks,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Eq-net: Elastic quanti- zation neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.493717Z

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 3322023e-6574-4cc8-aaed-388d9ae8e31c · outbound

This paper cites Robust quantization: One model to rule them all,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Robust quantization: One model to rule them all,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.483799Z

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 0c0c90fc-570c-4a23-b9a9-d21ef2481861 · outbound

This paper cites Multiquant: Training once for multi-bit quantization of neural networks.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Multiquant: Training once for multi-bit quantization of neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.473005Z

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 d29c4167-84b6-4e85-93e2-8e83e223750f · outbound

This paper cites MBQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantization.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision MBQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantization

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:18:09.315344Z

Source-reported events for the cited work

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Observation 1fdfce46-69d7-4bf6-865c-dc59015834b5 · outbound

This paper cites FlashAttention: Fast and memory-efficient exact attention with IO-awareness,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision FlashAttention: Fast and memory-efficient exact attention with IO-awareness,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.461375Z

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 88fc9793-0bb3-4ca5-82e5-32409f794e7b · outbound

This paper cites Any- precision deep neural networks,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Any- precision deep neural networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.449966Z

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 fafb3ea0-5ba4-4b2d-843a-ee794a5fcb9e · outbound

This paper cites A survey of quantization methods for efficient neural network infer- ence,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision A survey of quantization methods for efficient neural network infer- ence,

Reference 23

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Source-reported events for the cited work

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Observation 0d7957d6-890d-47ae-a223-a8036f7542ba · outbound

This paper cites Binary neural networks: A survey,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Binary neural networks: A survey,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:18:09.199506Z

Source-reported events for the cited work

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Observation 10229167-7a88-45be-b36c-ae94d34200c7 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 25

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Source-reported events for the cited work

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Observation 07d7a2a6-20c0-47b9-939c-faf30ae1bf2e · outbound

This paper cites Learned Step Size Quantization.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Learned Step Size Quantization

Reference 26

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Source-reported events for the cited work

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Observation 21210e79-6066-488d-9a86-36ef99899a42 · outbound

This paper cites Once quantization-aware training: High performance extremely low-bit architecture search,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Once quantization-aware training: High performance extremely low-bit architecture search,

Reference 27

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Source-reported events for the cited work

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Observation 450e82b8-c9dd-481f-8417-b85e2d0218b1 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 28

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Source-reported events for the cited work

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Observation fd1ac299-7625-4f9d-83ee-d294e03bfee2 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 29

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Observation fc4904b4-e7cc-46ce-8c23-edbed74bdbd9 · outbound

This paper cites Nonuniform- to-uniform quantization: Towards accurate quantization via generalized straight-through estimation,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Nonuniform- to-uniform quantization: Towards accurate quantization via generalized straight-through estimation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.410651Z

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 cb82b2b3-a395-4cdd-b3b6-fa12e57b52c8 · outbound

This paper cites Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets

Reference 31

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Observation d07b5b94-fbfb-47e2-8849-17e4da35ba56 · outbound

This paper cites Deep residual learning for image recognition,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Deep residual learning for image recognition,

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 029ef405-26a3-4ea1-bcc3-881bd26d8c51 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 33

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Source-reported events for the cited work

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Observation 61ac0a70-cb23-493e-a70f-66173a628f8c · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Imagenet classification with deep convolutional neural networks,

Reference 34

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Source-reported events for the cited work

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Observation 10e8cf68-0f24-44b3-a2ad-9bea25e7e1e8 · outbound

This paper cites Learning multiple layers of features from tiny images,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Learning multiple layers of features from tiny images,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T04:18:09.231011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d890329a-72ce-437d-bc77-fde5f4be5058 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Imagenet: A large-scale hierarchical image database,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T04:18:09.233778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e2494c5-e261-48bb-ad11-bb4ad4df2fc5 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Reading digits in natural images with unsupervised feature learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.369286Z

Source-reported events for the cited work

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Observation 05fd65b7-17fa-4326-aa0e-682933ec30bb · outbound

This paper cites Timeloop: A systematic approach to dnn accelerator evaluation,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Timeloop: A systematic approach to dnn accelerator evaluation,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T04:18:09.238875Z

Source-reported events for the cited work

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Observation 85843974-2048-4caf-a94a-072ff4db6bff · outbound

This paper cites Eyeriss: An energy- efficient reconfigurable accelerator for deep convolutional neural net- works,.

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision Eyeriss: An energy- efficient reconfigurable accelerator for deep convolutional neural net- works,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:18:09.352527Z

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.

Pith citing papers

Observation 79cde160-0886-48e4-bcac-3135eb5ff04c · inbound

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM cites this paper.

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision

Reference 7

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Observation 16f7baf3-70d3-48b2-8da9-70652c4fd8c3 · inbound

ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving cites this paper.

ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision

Reference 30

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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.