Paper Citation Record · LEDGER

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge

As of 20 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2507.14651.

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

pith.paper-citation-record.v1
2507.14651 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:59:36.622417Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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 exact0
verified fuzzy25
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9dfbf6c5-a6b0-4632-8a56-d0468b244aa7 · outbound

This paper cites A convnet for the 2020s,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge A convnet for the 2020s,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4b9acf37-ae43-4054-9043-242baca31c2f · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:40.834466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ade8b2e9-859c-42ad-9356-65942c541d5d · outbound

This paper cites Deep residual learning for image recognition,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Deep residual learning for image recognition,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:40.635841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c97a5042-7595-4f52-b5c2-f23c71bb0250 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Very deep convolutional networks for large-scale image recognition,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:40.417656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f699abc2-62f2-4602-8e0f-60264fbfbf61 · outbound

This paper cites Mobilenets: Efficient convolutional neural networks for mobile vision applications,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Mobilenets: Efficient convolutional neural networks for mobile vision applications,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:40.180281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aca2f565-64cf-4f87-b5c1-07313734d80c · outbound

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

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:39.971194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9ae7d8c0-243e-4bf5-861f-31a20df7c25d · outbound

This paper cites Mnasnet: Platform-aware neural architecture search for mobile,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Mnasnet: Platform-aware neural architecture search for mobile,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:39.772403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3392b94c-3b92-4527-ad47-3ebd4cd40f27 · outbound

This paper cites Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:39.580881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4d33b2c8-245c-4617-9ea0-a43cbd438348 · outbound

This paper cites Train- ing data-efficient image transformers and distillation through attention,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Train- ing data-efficient image transformers and distillation through attention,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:39.427977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0fd1f61-d51c-4b96-b5b7-e7411b4e98ee · outbound

This paper cites Separable self-attention for mobile vision transformers,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Separable self-attention for mobile vision transformers,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:39.308669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ebd24d7e-bb95-4247-bb2e-d0e84411d6e6 · outbound

This paper cites Edgenext: Efficiently amalgamated cnn-transformer architecture for mobile vision applications,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Edgenext: Efficiently amalgamated cnn-transformer architecture for mobile vision applications,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:39.096955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 38187c80-4995-4561-858c-3335201fe14e · outbound

This paper cites 1.1 computing’s energy problem (and what we can do about it).

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge 1.1 computing’s energy problem (and what we can do about it)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:38.945270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 869d4ba6-4764-4af6-9020-c50c1ae0ce65 · outbound

This paper cites Understanding sources of inefficiency in general-purpose chips.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Understanding sources of inefficiency in general-purpose chips

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:38.823614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5cf5024a-833f-4bfc-9484-f12d05d530e6 · 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.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge 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 2026-08-06T15:59:38.688331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7ada2d47-bfef-4a90-b3a3-0bd58912f618 · outbound

This paper cites Diana: An end-to-end hybrid digital and analog neural network soc for the edge,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Diana: An end-to-end hybrid digital and analog neural network soc for the edge,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:38.514812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c8cff0af-b995-46aa-bd76-d3dc858793b9 · outbound

This paper cites Tinyvers: A tiny versatile system-on-chip with state-retentive emram for ml inference at the extreme edge,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Tinyvers: A tiny versatile system-on-chip with state-retentive emram for ml inference at the extreme edge,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:38.363192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0d3a3c7b-7f32-412c-840b-152c2641327d · outbound

This paper cites Vaqf: Fully automatic software-hardware co-design frame- work for low-bit vision transformer,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Vaqf: Fully automatic software-hardware co-design frame- work for low-bit vision transformer,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:38.161711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2e9baaf9-19dc-4fe5-83f3-70bfbe3a5279 · outbound

This paper cites Row-wise accelerator for vision trans- former,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Row-wise accelerator for vision trans- former,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:37.962952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 35197c85-e054-4bf6-a74f-624031a81ec7 · outbound

This paper cites Vitcod: Vision transformer acceleration via dedicated algorithm and accelerator co-design,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Vitcod: Vision transformer acceleration via dedicated algorithm and accelerator co-design,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:37.783070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b1965628-ce47-46bd-ab2e-e3355db132a1 · outbound

This paper cites 9.2a 28nm 12.1tops/w dual-mode cnn processor using effective-weight- based convolution and error-compensation-based prediction,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge 9.2a 28nm 12.1tops/w dual-mode cnn processor using effective-weight- based convolution and error-compensation-based prediction,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:37.578651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1c7d0736-de84-4cd7-a88a-ea7fc6ec3a1b · outbound

This paper cites Analog matrix processor for edge ai real-time video analytics,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Analog matrix processor for edge ai real-time video analytics,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:37.404854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 83a18f22-0402-4bf6-8f51-e2698be44a29 · outbound

This paper cites Ju and J.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Ju and J

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:37.230364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7aa65157-65e8-48e3-8150-7801beb6f8b7 · outbound

This paper cites A 1mw always-on computer vision deep learning neural decision processor,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge A 1mw always-on computer vision deep learning neural decision processor,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:37.083658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2a83a090-22dd-422b-9a0e-81c223dcbfd0 · outbound

This paper cites Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Unpu: An energy-efficient deep neural network accelerator with fully variable weight bit precision,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:36.950635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cf6a18c6-dde5-4c9d-a170-03908552e93c · outbound

This paper cites Zigzag: A memory-centric rapid dnn accelerator design space exploration frame- work,.

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge Zigzag: A memory-centric rapid dnn accelerator design space exploration frame- work,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:59:36.807395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.