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Paper Citation Record · LEDGER

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow

As of 13 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2510.14393.

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

pith.paper-citation-record.v1
2510.14393 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T06:44:16.236747Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:24:55.670377Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:24:57.755945Z

Reference resolution

22 of 22 outbound references displayed

  • verified exact0
  • verified fuzzy22
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 109cb5fa-7632-4fe1-bfb5-26c08ab5ff08 · outbound

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

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Training data-efficient image transformers & distillation through attention

Reference 1

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raw_fallback, observed 2026-05-18T06:46:01.246409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:b303b051ae593fc802fe2ddf5643023ccfbd0749ebae7e3f229682036c79658b

Observation 2a3356ca-404a-4fb5-86b1-d7b8c6a7c4aa · outbound

This paper cites A 3: Accelerating attention mechanisms in neural networks with approximation.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow A 3: Accelerating attention mechanisms in neural networks with approximation

Reference 2

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raw_fallback, observed 2026-05-18T06:46:01.240647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 37bb5194-f43c-4245-a8e0-a1e192bb4f59 · outbound

This paper cites ELSA: hardware-software co-design for efficient, lightweight self- attention mechanism in neural networks.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow ELSA: hardware-software co-design for efficient, lightweight self- attention mechanism in neural networks

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation e4ac2999-d93e-4a15-98c3-f5c109167654 · outbound

This paper cites A 28nm 27.5TOPS/W approximate- computing-based transformer processor with asymptotic sparsity spec- ulating and out-of-order computing.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow A 28nm 27.5TOPS/W approximate- computing-based transformer processor with asymptotic sparsity spec- ulating and out-of-order computing

Reference 4

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raw_fallback, observed 2026-05-18T06:46:01.276622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:dd933cfd5db47833b5abec09b6dcfac3a50a1ae890e4b6017fbe0a0712a0d6cf

Observation fbda2085-a61d-433b-968e-09801b1ba685 · outbound

This paper cites SpAtten: Efficient sparse attention architecture with cascade token and head pruning.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow SpAtten: Efficient sparse attention architecture with cascade token and head pruning

Reference 5

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raw_fallback, observed 2026-05-18T06:46:01.268990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:8c2f21b8a4f04d2129308bc1ec96e140ab947e9f36bc46b94fd3ae4b32556d06

Observation 3ee6eaf0-0e8a-4867-b905-34b616ec05ff · outbound

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

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow FACT: FFN-attention co-optimized transformer architecture with eager correlation prediction

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:722c40fed1908136433e9c424edf16191171e172d8683f9cf4fca1c0f1581cd2

Observation dcb8b004-7cf8-4e2b-8bec-9739982580d5 · outbound

This paper cites Bsvit: A bit-serial vision transformer accelerator exploiting dynamic patch and weight bit-group quantization.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Bsvit: A bit-serial vision transformer accelerator exploiting dynamic patch and weight bit-group quantization

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation a9473c4e-e41e-489f-b9d4-00f0b66304e7 · outbound

This paper cites Evo-ViT: Slow-fast token evolution for dynamic vision transformer.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Evo-ViT: Slow-fast token evolution for dynamic vision transformer

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:432be78801e0ca165ffc8eeb0e70707446fbc4ccac09a77e9b8c0b50b294b649

Observation 28eec9e9-48ca-48fb-8ea2-e320a14faee1 · outbound

This paper cites Not all patches are what you need: Expediting vision transformers via token reorganiza- tions.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Not all patches are what you need: Expediting vision transformers via token reorganiza- tions

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:4174cd3dd6e90336bcd4d5db33ca138732c9ab901430275e4e128d6a6d0b18ef

Observation 6c63d297-8841-4ea4-972a-3b5f19926b0d · outbound

This paper cites A-ViT: adaptive tokens for efficient vision transformer.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow A-ViT: adaptive tokens for efficient vision transformer

Reference 10

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raw_fallback, observed 2026-05-18T06:46:01.301065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:037561fd35cc4032b4bbbfcda5e341a764debc661e2ea5008324f3e9aab46009

Observation 9c18cfca-9973-4d0f-b4d9-ec61b4b1be5a · outbound

This paper cites Adaptive token sampling for efficient vision transformers.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Adaptive token sampling for efficient vision transformers

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 32055b2e-539b-4287-a973-6ca42b821e31 · outbound

This paper cites Dynam- icViT: Efficient vision transformers with dynamic token sparsification.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Dynam- icViT: Efficient vision transformers with dynamic token sparsification

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:41b19cf8e602c4dbb4af37fd4e81e95ea18fa6c6020e58989dc02f4b1f1d1994

Observation dfff89ab-4891-4745-8230-5147314078ac · outbound

This paper cites Pruning self-attentions into convolutional layers in single path.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Pruning self-attentions into convolutional layers in single path

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:1036bdf8f7ce8e289e49e3d1d541d8aeeac7c28e70a891ca870dd4bcc85a92fb

Observation 3606f8f2-823f-4eee-8d62-bbe947b7eecd · outbound

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

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow HeatViT: hardware-efficient adaptive token pruning for vision transformers

Reference 14

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raw_fallback, observed 2026-05-18T06:46:01.263776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:114f3fb9aea1708b0dbd43daa4c0e3cac1ee8f03e2acb2c30ff516197bc8b216

Observation 6e220b56-723c-49bc-a88e-8fde0ab2a66a · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 15

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raw_fallback, observed 2026-05-18T06:46:01.279339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:cf8078cdeb246cf302224587f2eafc67f31b0555405758a52ae6bf993d253124

Observation 256ba5a2-c70e-4427-b680-1272311f6ac8 · outbound

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

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Swin transformer: Hierarchical vision transformer using shifted windows

Reference 16

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raw_fallback, observed 2026-05-18T06:46:01.266727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:e949565f75e51f2b5eba8eb9a888bf8ddfbd63a8936d63a3837a15f18a010ae3

Observation 1fbeeb40-1e0b-4104-b4b1-dc3983ecf4dd · outbound

This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Tokens-to-token vit: Training vision transformers from scratch on imagenet

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:32c3dd9dc61aa3f951c278dd03109f6c4e6e824aba10ab27e4b090e87522720c

Observation 3ba12419-29ee-4363-a162-57ca1f3613f9 · outbound

This paper cites Go- ing deeper with image transformers.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow Go- ing deeper with image transformers

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:fe636e736c1a5c2baafb5412bd90e5a42b3a9c495c28c8987c7a0d1a498c70e9

Observation 089cd5f4-2a8d-4f2f-8111-83cc72975518 · outbound

This paper cites ViTA: A vision transformer inference accelerator for edge applications.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow ViTA: A vision transformer inference accelerator for edge applications

Reference 19

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raw_fallback, observed 2026-05-18T06:46:01.255931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:6012ec9a1616403824d3af5fbd16005e848843b4133c7bb8d5296bee813fcee6

Observation 994b3618-e2a1-4cc4-8a95-8c4aa49d0a21 · outbound

This paper cites A comparison-free hardware sorting engine.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow A comparison-free hardware sorting engine

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:7098b05c1d3b89cdfadcdaa30a0cdd7dc1fc7929822ebb4a75aac96399efa78f

Observation 4c99b13e-8f89-4592-8f9d-b15d44724660 · outbound

This paper cites K-degree parallel comparison-free hardware sorter for complete sorting.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow K-degree parallel comparison-free hardware sorter for complete sorting

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:9967c464ad844f14437456eeaecf2f9b970c7ec82a2a1f5d152c8bbaf6621cdd

Observation b9ddb3f5-1eb6-464b-9782-e4a09ba17ebd · outbound

This paper cites ImageNet: a large-scale hierarchical image database.

Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow ImageNet: a large-scale hierarchical image database

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T06:44:16.236747Z digest=sha256:c671181b34d4a579601ab447d6ae6391a2b7d60186c3555a27bb7d93a0a2eb63

Pith citing papers

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

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation cites this paper.

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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local_arxiv, observed 2026-08-06T00:24:57.819571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T00:24:55.670377Z digest=sha256:9bd8837364c2c5b92da2d7584ab17abada9897b7e2a474efe2df50e449767f8e