Paper Citation Record · LEDGER

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization

As of 23 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2412.10261.

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

pith.paper-citation-record.v1
2412.10261 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:17:53.009786Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T03:44:09.156934Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:06:35.692628Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy24
unresolved14
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a74d870f-90aa-46a6-84eb-18883a2b2f8e · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.849425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed12547f-4897-4690-94a1-56f60a53be23 · outbound

This paper cites Towards convolutional neural networks compression via global&progressive product quanti- zation.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Towards convolutional neural networks compression via global&progressive product quanti- zation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.609356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 08bc2f73-c294-4f00-a9af-7aa35e6f5357 · outbound

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

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.597039Z

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Observation 6fff42f6-e877-432c-a058-97c34a15504e · outbound

This paper cites Vahid, Saurabh Adya, and Mohammad Raste- gari.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Vahid, Saurabh Adya, and Mohammad Raste- gari

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.583748Z

Source-reported events for the cited work

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Observation 55aca96a-60ec-44dc-a677-63207520b89b · outbound

This paper cites Learned Step Size Quantization.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Learned Step Size Quantization

Reference 5

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Source-reported events for the cited work

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Observation 82486fff-75c2-4d79-ba59-f77cd626f09e · outbound

This paper cites The pascal visual object classes (voc) challenge.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization The pascal visual object classes (voc) challenge

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.875679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a67d587a-e19c-4872-b8ea-fc3e444e43d0 · outbound

This paper cites Gemmini: Enabling systematic deep-learning architecture evaluation via full-stack integration.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Gemmini: Enabling systematic deep-learning architecture evaluation via full-stack integration

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.561025Z

Source-reported events for the cited work

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Observation 29fbde60-6b91-49f8-8031-35cfb58bb6ea · outbound

This paper cites Sparten: A sparse tensor accelerator for convolutional neural networks.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Sparten: A sparse tensor accelerator for convolutional neural networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.548581Z

Source-reported events for the cited work

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Observation 230a9ab1-8dcd-4295-857e-1c27738fb242 · outbound

This paper cites Compressing Deep Convolutional Networks using Vector Quantization.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Compressing Deep Convolutional Networks using Vector Quantization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.887716Z

Source-reported events for the cited work

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Observation 4ebce0de-1984-4ebd-9ef8-cf717310c7ae · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.892061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83fc48e4-4807-4e8f-b1ab-6510fe5631c8 · outbound

This paper cites Boosting the performance of cnn accelerators with dy- namic fine-grained channel gating.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Boosting the performance of cnn accelerators with dy- namic fine-grained channel gating

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.534505Z

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Observation 81d489ad-54db-4c39-8e1c-3ff70d0c3b66 · outbound

This paper cites Sibia: Signed bit-slice architecture for dense dnn acceleration with slice-level sparsity exploitation.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Sibia: Signed bit-slice architecture for dense dnn acceleration with slice-level sparsity exploitation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.521468Z

Source-reported events for the cited work

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Observation 0c99698b-fc8d-4206-b7c0-b351e15bf762 · outbound

This paper cites Product quantiza- tion for nearest neighbor search.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Product quantiza- tion for nearest neighbor search

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.508878Z

Source-reported events for the cited work

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Observation 92173d48-dfd7-4044-a974-4bfa480b8bde · outbound

This paper cites In-datacenter performance analysis of a tensor pro- cessing unit.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization In-datacenter performance analysis of a tensor pro- cessing unit

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.909399Z

Source-reported events for the cited work

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Observation 5477ba97-74e8-4991-8ae6-8b437c84a9df · outbound

This paper cites Adam: A Method for Stochastic Optimization.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Adam: A Method for Stochastic Optimization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.913987Z

Source-reported events for the cited work

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Observation 9f8e67a1-63f2-47b8-a4e8-8c2f86a22479 · outbound

This paper cites Pruning vs quantization: Which is better?, 2023.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Pruning vs quantization: Which is better?, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.488377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 35bcdd70-4a1e-4236-9896-4b6ae4f2d4db · outbound

This paper cites Convolu- tional neural network accelerator with vector quantization.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Convolu- tional neural network accelerator with vector quantization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.476317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 60cfa20b-e96e-4f5d-a3a2-b5885f75a431 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Pruning Filters for Efficient ConvNets

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.926533Z

Source-reported events for the cited work

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Observation f5b98827-4957-47fd-9673-5e2105e66a84 · outbound

This paper cites Microsoft coco: Common objects in context.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Microsoft coco: Common objects in context

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.464802Z

Source-reported events for the cited work

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Observation ffb9051a-9da9-4d83-989f-f3d7f465a5c0 · outbound

This paper cites Systolic tensor array: An efficient structured-sparse gemm accelerator for mo- bile cnn inference.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Systolic tensor array: An efficient structured-sparse gemm accelerator for mo- bile cnn inference

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.452500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 88468eaf-48f2-4733-82f6-780eda9bfe8e · outbound

This paper cites S2ta: Exploiting structured sparsity for energy-efficient mobile cnn ac- celeration.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization S2ta: Exploiting structured sparsity for energy-efficient mobile cnn ac- celeration

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.440973Z

Source-reported events for the cited work

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Observation 9347173e-f707-405f-9ab9-4c1380aebcaf · outbound

This paper cites Learning Sparse Neural Networks through $L_0$ Regularization.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Learning Sparse Neural Networks through $L_0$ Regularization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.945714Z

Source-reported events for the cited work

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Observation 8f6a5f31-7cf4-4ad8-bfd4-4e6f1bde1157 · outbound

This paper cites Permute, quantize, and fine-tune: Efficient compression of neural networks.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Permute, quantize, and fine-tune: Efficient compression of neural networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.316015Z

Source-reported events for the cited work

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Observation 3b0b8964-19c5-47e9-af42-b02906057578 · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Accelerating Sparse Deep Neural Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.953072Z

Source-reported events for the cited work

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Observation aa96e62b-d598-451a-9e98-6e144d800c0a · outbound

This paper cites Importance estimation for neural network pruning.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Importance estimation for neural network pruning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.303371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 25386ead-8c18-45d7-85bc-ce4419ec443d · outbound

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MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-11T16:17:53.290677Z

Source-reported events for the cited work

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Observation f9fd7d0c-b19c-47a5-9179-a198f3ba9bfa · outbound

This paper cites Comparing Rewinding and Fine-tuning in Neural Network Pruning.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Comparing Rewinding and Fine-tuning in Neural Network Pruning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:52.964812Z

Source-reported events for the cited work

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Observation 6f9200f8-1d08-44c9-8e75-7640c859b6eb · outbound

This paper cites 4.4 a 1.3 tops/w@ 32gops fully integrated 10-core soc for iot end-nodes with 1.7 𝜇w cognitive wake-up from mram-based state-retentive sleep mode.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization 4.4 a 1.3 tops/w@ 32gops fully integrated 10-core soc for iot end-nodes with 1.7 𝜇w cognitive wake-up from mram-based state-retentive sleep mode

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.274572Z

Source-reported events for the cited work

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Observation 4a639695-d1f6-47b5-90a7-4b767e8453da · outbound

This paper cites An energy-efficient deep convolutional neural network inference processor with enhanced output stationary dataflow in 65-nm cmos.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization An energy-efficient deep convolutional neural network inference processor with enhanced output stationary dataflow in 65-nm cmos

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.261772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2056092a-da0c-4d31-913b-c523d259ae24 · outbound

This paper cites An accelerator for sparse convolutional neural networks leveraging systolic general matrix-matrix multiplication.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization An accelerator for sparse convolutional neural networks leveraging systolic general matrix-matrix multiplication

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.249555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 29cdace3-8835-4244-b216-7dea4f69b3f2 · outbound

This paper cites Clustering con- volutional kernels to compress deep neural networks.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Clustering con- volutional kernels to compress deep neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.237109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8f87aea2-ca18-492d-95cf-983e0fa24f57 · outbound

This paper cites Scaling equations for the accu- rate prediction of cmos device performance from 180 nm to 7 nm.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Scaling equations for the accu- rate prediction of cmos device performance from 180 nm to 7 nm

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.224675Z

Source-reported events for the cited work

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Observation 2630ae3c-afa4-43c9-8d68-cad003903177 · outbound

This paper cites And the Bit Goes Down: Revisiting the Quantization of Neural Networks.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization And the Bit Goes Down: Revisiting the Quantization of Neural Networks

Reference 33

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Source-reported events for the cited work

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Observation a082972d-853a-488c-8147-b2ecc80aa2ef · outbound

This paper cites Dominosearch: Find layer-wise fine-grained n: M sparse schemes from dense neural networks.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Dominosearch: Find layer-wise fine-grained n: M sparse schemes from dense neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.211754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 83fe967f-2423-45ce-81cb-51d8797ee996 · outbound

This paper cites Ews: An energy-efficient cnn accelera- tor with enhanced weight stationary dataflow.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Ews: An energy-efficient cnn accelera- tor with enhanced weight stationary dataflow

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.198831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b0d0b948-a248-4df6-bc6a-361748f8e0c8 · outbound

This paper cites Quantized convolutional neural networks for mobile devices.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Quantized convolutional neural networks for mobile devices

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.182963Z

Source-reported events for the cited work

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Observation f2ae85e8-cc11-488a-8385-86a40012af19 · outbound

This paper cites Accelerator design for vector quantized convolutional neural network.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Accelerator design for vector quantized convolutional neural network

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:17:53.169891Z

Source-reported events for the cited work

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Observation 323986c3-0819-4a00-91eb-339ba1e7cd01 · outbound

This paper cites Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch.

MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T16:17:53.009786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0de6ade0-9d54-4cbb-a906-cdbddb5dc7fa · inbound

31.1 A 14.08-to-135.69Token/s ReRAM-on-Logic Stacked Outlier-Free Large-Language-Model Accelerator with Block-Clustered Weight-Compression and Adaptive Parallel-Speculative-Decoding cites this paper.

31.1 A 14.08-to-135.69Token/s ReRAM-on-Logic Stacked Outlier-Free Large-Language-Model Accelerator with Block-Clustered Weight-Compression and Adaptive Parallel-Speculative-Decoding MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:06:35.697571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.