Paper Citation Record · LEDGER

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference

As of 9 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.01798.

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

pith.paper-citation-record.v1
2607.01798 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T08:37:19.893536Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

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Outbound references

Observation e13e3c70-159d-43ca-ae1d-6f732523246c · outbound

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

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 1

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Observation 209d6200-8ab9-4feb-a3d6-9dd83a9e6007 · outbound

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

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Training data-efficient image trans- formers & distillation through attention,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation d08ee893-7a50-4af5-82cd-2cb5427944d5 · outbound

This paper cites Edge intelligence for energy-efficient computation offloading and resource management in IoT-enabled smart grid,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Edge intelligence for energy-efficient computation offloading and resource management in IoT-enabled smart grid,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 65a73f16-6ee1-4d19-a96a-b087d6f85283 · outbound

This paper cites Energy and policy considerations for deep learning in NLP,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Energy and policy considerations for deep learning in NLP,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 226dc8a1-5946-4493-b5bf-0c0d9f8b815d · outbound

This paper cites The carbon footprint of machine learning training will plateau, then shrink,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference The carbon footprint of machine learning training will plateau, then shrink,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 4a80dc5c-d076-4285-9a7d-95f403099986 · outbound

This paper cites Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation b7d15f41-e36a-42da-a860-c31866e8c995 · outbound

This paper cites Pdet: A progressive deformable transformer for photovoltaic panel defect seg- mentation,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Pdet: A progressive deformable transformer for photovoltaic panel defect seg- mentation,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation e51b4b2e-e801-4322-b622-73c65e738160 · outbound

This paper cites Deep learning model-transformer based wind power forecasting approach,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Deep learning model-transformer based wind power forecasting approach,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 02d2b4e8-5701-4198-9600-3e53d5f8f8d0 · outbound

This paper cites Transformer-based model for electrical load forecasting,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Transformer-based model for electrical load forecasting,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Unavailable: canonical work link unavailable.

Observation 11bc894b-7e47-4aba-9c6d-88c927fad513 · outbound

This paper cites Accurately computing the log-sum-exp and softmax functions,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Accurately computing the log-sum-exp and softmax functions,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 2b593858-c1d8-46fa-a10a-f53efdfa4377 · outbound

This paper cites FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 1582c48a-9599-4b6b-9817-d538616388eb · outbound

This paper cites Hardware implementation of multi-rate input softmax activation function,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Hardware implementation of multi-rate input softmax activation function,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation f5b0753e-bc73-446b-ad05-d30d2d672d0b · outbound

This paper cites An empirical evaluation of en- hanced performance softmax function in deep learning,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference An empirical evaluation of en- hanced performance softmax function in deep learning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 03c7a484-7a5b-4fdd-9ba2-58ef42aff980 · outbound

This paper cites An energy-efficient architecture of approximate softmax functions for Transformer in edge computing,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference An energy-efficient architecture of approximate softmax functions for Transformer in edge computing,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 4348b068-db13-4fc0-b24e-0c94e57a3fff · outbound

This paper cites Hardware-oriented and precisely approximated online soft- max for deep learning models,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Hardware-oriented and precisely approximated online soft- max for deep learning models,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 89b745d4-91b7-49c9-a060-6d7dee42c670 · outbound

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

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference ViTA: A vision transformer inference accelerator for edge applications,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation d5b15168-e664-46bf-9904-761bee70ebcc · outbound

This paper cites A high speed reconfigurable architecture for softmax and GELU in vision transformer,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference A high speed reconfigurable architecture for softmax and GELU in vision transformer,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 51656e4d-4f5f-44c3-9c36-4653c9eda45c · outbound

This paper cites Hyft: A reconfigurable softmax accelerator with hybrid numeric format for both training and inference,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Hyft: A reconfigurable softmax accelerator with hybrid numeric format for both training and inference,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 35d32a95-ef33-4d26-ae7e-8d8ec4c8388c · outbound

This paper cites FPGA implementation and analysis on parallel and pipeline approximate softmax for transformer,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference FPGA implementation and analysis on parallel and pipeline approximate softmax for transformer,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation e7b729a9-fdb2-419a-8fd8-08867f027a70 · outbound

This paper cites I-BERT: Integer-only BERT quantization,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference I-BERT: Integer-only BERT quantization,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation d958f430-87ad-49fc-b0be-21da7e16a7ee · outbound

This paper cites ITA: An energy- efficient attention and softmax accelerator for quantized transformers,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference ITA: An energy- efficient attention and softmax accelerator for quantized transformers,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 9962d692-7efd-424c-b9b9-8d3a12a53e71 · outbound

This paper cites A low power attention and softmax accelerator for large language models inference,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference A low power attention and softmax accelerator for large language models inference,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 3c382715-e715-4be1-8aac-b8c7f0d33eea · outbound

This paper cites Piecewise-linear approximation of self-attention and its accuracy- aware training for area-efficient vision transformer inference accelerator,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Piecewise-linear approximation of self-attention and its accuracy- aware training for area-efficient vision transformer inference accelerator,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation f37053cf-bbc8-4f56-9510-909c6c6a9b9e · outbound

This paper cites Imagenette: A smaller subset of ImageNet,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Imagenette: A smaller subset of ImageNet,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation 0c8216ec-9ebc-4222-85c6-cea675e1c3f7 · outbound

This paper cites PyTorch image models (timm),.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference PyTorch image models (timm),

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation c9daeacf-99e6-4ee4-9625-94b17ea1ed33 · outbound

This paper cites Hardware-efficient softmax approximation for self-attention networks,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference Hardware-efficient softmax approximation for self-attention networks,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Observation eed036f0-bbba-41c9-b254-d7b606a94d58 · outbound

This paper cites A generalizable low-precision softmax approximation for small-FPGA de- ployment of vision transformers,.

Approximate Attention Weighting for Sustainable FPGA-Based Vision Transformer Inference A generalizable low-precision softmax approximation for small-FPGA de- ployment of vision transformers,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T08:37:19.893536Z

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Pith citing papers

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