Paper Citation Record · LEDGER

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation

As of 10 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2608.01343.

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

pith.paper-citation-record.v1
2608.01343 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:24:57.534726Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

38 of 38 outbound references displayed

verified exact2
verified fuzzy28
unresolved8
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d8a9a7d5-47ee-4f95-aeb9-ce943174380d · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:53.447812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74f916c2-4f63-4236-9740-ce1cf57908be · outbound

This paper cites End-to-end object detection with transformers,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation End-to-end object detection with transformers,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:53.507572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a19c456-fd25-4c3b-b4c8-88d953c5fae2 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:53.590585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e1b8358-4d19-44b8-aa83-f236316c4423 · outbound

This paper cites Similarity-guided layer-adaptive vision transformer for UA V tracking,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Similarity-guided layer-adaptive vision transformer for UA V tracking,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:04.029306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff7f01fa-9e5d-4106-87b2-0e2cac6f7607 · outbound

This paper cites Stream-ViT: learning streamlined convolutions in vision transformer,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Stream-ViT: learning streamlined convolutions in vision transformer,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:03.825433Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 983c2929-8561-4435-bdee-4f42b5cd4c5c · outbound

This paper cites Daneshtalab and M.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Daneshtalab and M

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:03.606646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4488ef7a-9bf5-44dc-9472-8a69ccd77580 · outbound

This paper cites PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:03.408004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b39d9018-f7bd-4194-baee-207d266b7929 · outbound

This paper cites I-ViT: Integer-only quantization for efficient vision transformer inference,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation I-ViT: Integer-only quantization for efficient vision transformer inference,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:03.162129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 481aa083-e58a-491b-a42e-3bab8fdd011d · outbound

This paper cites Q-ViT: Fully Differentiable Quantization for Vision Transformer.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Q-ViT: Fully Differentiable Quantization for Vision Transformer

Reference 9

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Source-reported events for the cited work

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Observation a87f0219-4819-4f40-9709-447378d8ae0f · outbound

This paper cites ∆NN: Power-efficient neural network acceleration using differential weights,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation ∆NN: Power-efficient neural network acceleration using differential weights,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:02.934590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 24ce5dd6-4b2a-4261-8639-4b9230157a54 · outbound

This paper cites Energy-efficient acceleration of con- volutional neural networks using computation reuse,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Energy-efficient acceleration of con- volutional neural networks using computation reuse,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:02.622361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 30cb78ef-58d2-42ab-8a39-2da59ed93694 · outbound

This paper cites Power-efficient accelerator design for neural networks using computation reuse,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Power-efficient accelerator design for neural networks using computation reuse,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:02.441276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 49bb8f07-38ce-4f7e-a4c1-b04bf389ea7f · outbound

This paper cites UCNN: Exploiting computational reuse in deep neural networks via weight repetition,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation UCNN: Exploiting computational reuse in deep neural networks via weight repetition,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:02.154752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dddf7409-be40-4da1-9f6c-b254114d194e · outbound

This paper cites ReMove: Leveraging motion estimation for computation reuse in CNN-based video processing,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation ReMove: Leveraging motion estimation for computation reuse in CNN-based video processing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:01.982022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9bbefd47-064b-46b0-a969-b330249ed2d4 · outbound

This paper cites SkippyNN: An embedded stochastic- computing accelerator for convolutional neural networks,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation SkippyNN: An embedded stochastic- computing accelerator for convolutional neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:01.688192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ab9a22c-c1f8-40b7-9dcf-9ae376860659 · outbound

This paper cites Computation reuse in DNNs by exploiting input similarity,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Computation reuse in DNNs by exploiting input similarity,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:01.451019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 24955af7-7a36-4582-bdec-256b11250c16 · outbound

This paper cites SIRENA: Sparsity-repetition aware nibble- based hardware accelerator for convolutional neural networks,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation SIRENA: Sparsity-repetition aware nibble- based hardware accelerator for convolutional neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:01.176956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation acea3a43-3b7a-4b04-aa6e-52428e87a729 · outbound

This paper cites DeltaRNN: A power-efficient recurrent neural network accelerator,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation DeltaRNN: A power-efficient recurrent neural network accelerator,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:00.905697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 90344acf-0873-41c7-bf83-e1f86072db79 · outbound

This paper cites Low-power online ECG analysis using neural networks,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Low-power online ECG analysis using neural networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:00.702797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c6564b39-8a5e-44e0-b492-4052031e1527 · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Training data-efficient image transformers & distillation through attention,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:00.431056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dd80e6c1-e9e6-4f8d-b58e-d28a70013357 · outbound

This paper cites Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8ba57d15-e1cb-49b7-97bb-3520375753b0 · outbound

This paper cites RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers

Reference 22

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Source-reported events for the cited work

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Observation dfa73457-49cd-440f-a3b7-94de0aa2fc6b · outbound

This paper cites DynamicViT: Efficient vision transformers with dynamic token sparsification,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation DynamicViT: Efficient vision transformers with dynamic token sparsification,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:55.897557Z

Source-reported events for the cited work

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Observation 05ac50b1-454c-423a-b421-ec638694103b · outbound

This paper cites Token merging: Your ViT but faster,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Token merging: Your ViT but faster,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:00.269056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 75971155-408f-46c3-b6d3-76a564f96830 · outbound

This paper cites ViTCoD: Vision transformer acceleration via dedicated algo- rithm and accelerator co-design,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation ViTCoD: Vision transformer acceleration via dedicated algo- rithm and accelerator co-design,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:25:00.068162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6a384ec3-61e8-4552-98af-f46711919963 · outbound

This paper cites HeatViT: Hardware-efficient adaptive token pruning for vision transformers,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation HeatViT: Hardware-efficient adaptive token pruning for vision transformers,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:59.772779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 30bf6e20-884a-4b73-b9c5-39b82b2665a9 · outbound

This paper cites Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers

Reference 27

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Source-reported events for the cited work

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Observation 5e4fc2c1-9a7d-4e13-aa02-f60800086419 · outbound

This paper cites An algorithm-hardware co-optimized framework for accelerating N:M sparse transformers,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation An algorithm-hardware co-optimized framework for accelerating N:M sparse transformers,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:59.615116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c60afc93-9858-48c8-a11c-e5e107c34bad · outbound

This paper cites ShiftAddViT: Mixture of multi- plication primitives towards efficient vision transformer,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation ShiftAddViT: Mixture of multi- plication primitives towards efficient vision transformer,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:59.404751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f2460ae-990f-49ee-b97a-c4561d42541c · outbound

This paper cites ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:56.808041Z

Source-reported events for the cited work

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Observation 4cb2d83b-3a58-4a23-93ee-d379f1fa4e54 · outbound

This paper cites LUT tensor core: A software-hardware co-design for LUT-based low-bit LLM inference,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation LUT tensor core: A software-hardware co-design for LUT-based low-bit LLM inference,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:59.190743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6442f140-485c-408e-b463-36dd959dc4ad · outbound

This paper cites ShiftAddNet: A hardware-inspired deep network,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation ShiftAddNet: A hardware-inspired deep network,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:59.026715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3f095140-b0e5-4b5b-9ea7-25833b20ec17 · outbound

This paper cites AccelTran: A sparsity-aware accelerator for dynamic inference with transformers,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation AccelTran: A sparsity-aware accelerator for dynamic inference with transformers,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:58.774751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 76f8bdfc-1f37-4e10-b3fa-2715328b7eea · outbound

This paper cites FACT: FFN-attention co-optimized transformer architecture with eager correlation prediction,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation FACT: FFN-attention co-optimized transformer architecture with eager correlation prediction,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:58.588630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2d1c373e-849f-43e7-99a9-e0b7a036717a · outbound

This paper cites SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:24:57.662867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fae72c8f-3000-4895-bb24-ca0fb9a6879d · outbound

This paper cites Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:58.386558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 47fb1518-1ce1-46b0-8345-f180246759d8 · outbound

This paper cites A novel deep learning-based approach for video quality enhancement,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation A novel deep learning-based approach for video quality enhancement,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:58.249106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 59e1ddc2-ca58-4613-96c9-ed6365ff18cc · outbound

This paper cites Photo-realistic single image super-resolution using a generative adversarial network,.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Photo-realistic single image super-resolution using a generative adversarial network,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:58.045985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.