Paper Citation Record · LEDGER

Refining Datapath for Microscaling ViTs

As of 8 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2505.22194.

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

pith.paper-citation-record.v1
2505.22194 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:20.761016Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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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

50 of 50 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b1d80f47-8063-4e5b-b245-7cadb6bc7630 · outbound

This paper cites FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs.

Refining Datapath for Microscaling ViTs FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs

Reference 1

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Observation 93b91800-a221-433c-8f8a-9e5e5c9aaa6e · outbound

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

Refining Datapath for Microscaling ViTs Heatvit: Hardware-efficient adaptive token pruning for vision transformers,

Reference 2

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Observation 7aaed175-22b6-412c-8903-6606231d4add · outbound

This paper cites Auto-vit-acc: An fpga-aware automatic acceleration framework for vision transformer with mixed-scheme quantization,.

Refining Datapath for Microscaling ViTs Auto-vit-acc: An fpga-aware automatic acceleration framework for vision transformer with mixed-scheme quantization,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:30.170844Z

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Observation d0500b1e-db1b-4f46-b68b-3db1a0a2fd1c · outbound

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

Refining Datapath for Microscaling ViTs An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

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Observation 24584f6a-7a90-4a42-861b-0a2eeb3d912f · outbound

This paper cites Sda: Low-bit stable diffusion acceleration on edge fpgas,.

Refining Datapath for Microscaling ViTs Sda: Low-bit stable diffusion acceleration on edge fpgas,

Reference 5

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Observation 34bc6a8f-ad87-4d52-b725-5876218dc734 · outbound

This paper cites Pushing the limits of narrow precision inferencing at cloud scale with microsoft floating point,.

Refining Datapath for Microscaling ViTs Pushing the limits of narrow precision inferencing at cloud scale with microsoft floating point,

Reference 6

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Source-reported events for the cited work

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Observation f2c2aef5-7751-4aa4-9ee8-0c8182264bd5 · outbound

This paper cites Revisiting block-based quantisation: What is important for sub-8-bit llm inference?,.

Refining Datapath for Microscaling ViTs Revisiting block-based quantisation: What is important for sub-8-bit llm inference?,

Reference 7

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Source-reported events for the cited work

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Observation d83a95f4-5b83-4428-9d78-aceca25b0058 · outbound

This paper cites Ex- ploring fpga designs for mx and beyond,.

Refining Datapath for Microscaling ViTs Ex- ploring fpga designs for mx and beyond,

Reference 8

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Observation 92390164-b325-43d4-9eb4-ce62abb587b3 · outbound

This paper cites An integer- only and group-vector systolic accelerator for efficiently mapping vision transformer on edge,.

Refining Datapath for Microscaling ViTs An integer- only and group-vector systolic accelerator for efficiently mapping vision transformer on edge,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:29.180303Z

Source-reported events for the cited work

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Observation 810b85f5-3e70-4806-a317-fbdd153a705e · outbound

This paper cites Deit iii: Revenge of the vit,.

Refining Datapath for Microscaling ViTs Deit iii: Revenge of the vit,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:28.975518Z

Source-reported events for the cited work

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Observation 09fa846a-e671-4ea7-98cb-3080e7e18a93 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Refining Datapath for Microscaling ViTs Imagenet: A large-scale hierarchical image database,

Reference 11

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Source-reported events for the cited work

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Observation 3b193b75-27c2-428f-84dc-4203107c1238 · outbound

This paper cites Ieee standard 754 for binary floating-point arithmetic,.

Refining Datapath for Microscaling ViTs Ieee standard 754 for binary floating-point arithmetic,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:28.773838Z

Source-reported events for the cited work

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Observation f4b21b32-3869-4dd1-9650-3dbcf1bd2d66 · outbound

This paper cites Understanding the potential of fpga-based spatial accel- eration for large language model inference,.

Refining Datapath for Microscaling ViTs Understanding the potential of fpga-based spatial accel- eration for large language model inference,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:28.522640Z

Source-reported events for the cited work

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Observation 713475d0-1cf2-4cd2-b937-fb329f6e340c · outbound

This paper cites fpgaconvnet: A framework for map- ping convolutional neural networks on fpgas,.

Refining Datapath for Microscaling ViTs fpgaconvnet: A framework for map- ping convolutional neural networks on fpgas,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:28.379137Z

Source-reported events for the cited work

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Observation 546c268d-d9e4-49b9-bdb6-e8bffe9661f9 · outbound

This paper cites Finn: A framework for fast, scalable binarized neural network inference,.

Refining Datapath for Microscaling ViTs Finn: A framework for fast, scalable binarized neural network inference,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:28.145064Z

Source-reported events for the cited work

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Observation 3d543e4f-ace0-4ed0-98e5-538e57ba6ede · outbound

This paper cites HIDA: A Hierarchical Dataflow Compiler for High-Level Synthesis.

Refining Datapath for Microscaling ViTs HIDA: A Hierarchical Dataflow Compiler for High-Level Synthesis

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:20:21.362760Z

Source-reported events for the cited work

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Observation a15371d6-aec2-49f4-9ca4-423393c3170c · outbound

This paper cites Automating constraint- aware datapath optimization using e-graphs,.

Refining Datapath for Microscaling ViTs Automating constraint- aware datapath optimization using e-graphs,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:27.892641Z

Source-reported events for the cited work

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Observation 408b7846-7e39-491a-a5f5-f82ddb2772f1 · outbound

This paper cites Online alignment and ad- dition in multiterm floating-point adders,.

Refining Datapath for Microscaling ViTs Online alignment and ad- dition in multiterm floating-point adders,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:27.628940Z

Source-reported events for the cited work

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Observation c88b84c8-2f4b-47d6-8ed8-3627c6249412 · outbound

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Refining Datapath for Microscaling ViTs Unresolved cited work

Reference 19

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Observation 59ad3361-e666-4df0-b4ca-254189269fd1 · outbound

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Refining Datapath for Microscaling ViTs Unresolved cited work

Reference 22

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Observation c2730087-e6d4-4500-afea-6caf382f9889 · outbound

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

Refining Datapath for Microscaling ViTs I-vit: Integer-only quantization for efficient vision transformer inference,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:26.990389Z

Source-reported events for the cited work

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Observation 1f0dd621-5734-4f90-97fa-340760f7ab64 · outbound

This paper cites Packqvit: Faster sub-8-bit vision transformers via full and packed quantization on the mobile,.

Refining Datapath for Microscaling ViTs Packqvit: Faster sub-8-bit vision transformers via full and packed quantization on the mobile,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:26.731802Z

Source-reported events for the cited work

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Observation a934d7d2-87b8-4405-b442-a21b150bad02 · outbound

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Refining Datapath for Microscaling ViTs Unresolved cited work

Reference 26

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Source-reported events for the cited work

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Observation 4712e211-b82f-4160-ba88-97d83826e801 · outbound

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Refining Datapath for Microscaling ViTs Unresolved cited work

Reference 27

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Observation 9032c207-d2cd-45b8-8ede-268ff8e141ca · outbound

This paper cites Towards accurate binary convolutional neural network,.

Refining Datapath for Microscaling ViTs Towards accurate binary convolutional neural network,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:25.977907Z

Source-reported events for the cited work

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Observation ccb3feee-360d-4434-aa1e-0a2db0d53319 · outbound

This paper cites Lq-nets: Learned quantization for highly accurate and compact deep neural networks,.

Refining Datapath for Microscaling ViTs Lq-nets: Learned quantization for highly accurate and compact deep neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:25.740459Z

Source-reported events for the cited work

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Observation 37a05b00-9283-4024-9987-4eb4d9ec0293 · outbound

This paper cites Training and Inference with Integers in Deep Neural Networks.

Refining Datapath for Microscaling ViTs Training and Inference with Integers in Deep Neural Networks

Reference 30

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Source-reported events for the cited work

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Observation 407a4a88-9a1c-4882-962d-3d9049ce74ed · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

Refining Datapath for Microscaling ViTs Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:17.699844Z

Source-reported events for the cited work

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Observation 422d7bdb-d9ca-4a2a-97cc-9896d9604b23 · outbound

This paper cites Vs-quant: Per-vector scaled quantization for accurate low-precision neural network inference,.

Refining Datapath for Microscaling ViTs Vs-quant: Per-vector scaled quantization for accurate low-precision neural network inference,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:17.884330Z

Source-reported events for the cited work

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Observation af4683c7-d53f-41a3-9408-7896a35855db · outbound

This paper cites Accuracy Booster: Enabling 4-bit Fixed-point Arithmetic for DNN Training.

Refining Datapath for Microscaling ViTs Accuracy Booster: Enabling 4-bit Fixed-point Arithmetic for DNN Training

Reference 33

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Source-reported events for the cited work

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Observation 94b9584b-dfb4-4ca5-bad2-bae5fce029ae · outbound

This paper cites With shared microexponents, a little shifting goes a long way,.

Refining Datapath for Microscaling ViTs With shared microexponents, a little shifting goes a long way,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:25.557938Z

Source-reported events for the cited work

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Observation b6acd6af-d94f-4653-9e60-515c52b25e04 · outbound

This paper cites Going further with winograd convolutions: Tap-wise quantization for efficient inference on 4x4 tiles,.

Refining Datapath for Microscaling ViTs Going further with winograd convolutions: Tap-wise quantization for efficient inference on 4x4 tiles,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:25.293868Z

Source-reported events for the cited work

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Observation ccb85a02-28b9-4d80-91c8-f5c55bc49ea0 · outbound

This paper cites Drq: dynamic region-based quantization for deep neural network ac- celeration,.

Refining Datapath for Microscaling ViTs Drq: dynamic region-based quantization for deep neural network ac- celeration,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:25.054370Z

Source-reported events for the cited work

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Observation ee0f18cc-6ed1-47db-b2b3-2cae6d51fc44 · outbound

This paper cites Mokey: Enabling narrow fixed-point inference for out-of-the-box floating-point transformer models,.

Refining Datapath for Microscaling ViTs Mokey: Enabling narrow fixed-point inference for out-of-the-box floating-point transformer models,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:24.880913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 964d1f2e-b7b2-410d-8bd2-68143040524a · outbound

This paper cites Cambricon-q: A hybrid architecture for efficient training,.

Refining Datapath for Microscaling ViTs Cambricon-q: A hybrid architecture for efficient training,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:24.709085Z

Source-reported events for the cited work

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Observation a0abf53c-ee62-4502-9e30-561a023d4cab · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

Refining Datapath for Microscaling ViTs LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:18.868300Z

Source-reported events for the cited work

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Observation d31152a0-8288-4190-922b-694a8fb847c2 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Refining Datapath for Microscaling ViTs GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:19.087687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d37599af-d40b-4cd4-8737-7db987586645 · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

Refining Datapath for Microscaling ViTs SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:19.242196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4089cd28-7441-4add-a65f-bc84b86a7b12 · outbound

This paper cites Zeroquant: Efficient and affordable post-training quantization for large- scale transformers,.

Refining Datapath for Microscaling ViTs Zeroquant: Efficient and affordable post-training quantization for large- scale transformers,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:24.445910Z

Source-reported events for the cited work

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Observation e532194a-73d2-4e55-b5b3-0da2eba9ab79 · outbound

This paper cites Psq: An automatic search framework for data-free quantization on pim-based architecture,.

Refining Datapath for Microscaling ViTs Psq: An automatic search framework for data-free quantization on pim-based architecture,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:24.160943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a995ed6f-0779-4761-b0e0-df917764de87 · outbound

This paper cites Spark: Scalable and precision-aware acceleration of neural networks via efficient encoding,.

Refining Datapath for Microscaling ViTs Spark: Scalable and precision-aware acceleration of neural networks via efficient encoding,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:23.972753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 842aa567-9718-4b9e-9ec9-fb036c237450 · outbound

This paper cites Msd: Mixing signed digit representations for hardware-efficient dnn accelera- tion on fpga with heterogeneous resources,.

Refining Datapath for Microscaling ViTs Msd: Mixing signed digit representations for hardware-efficient dnn accelera- tion on fpga with heterogeneous resources,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:23.728158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3df7a2ae-3689-45ae-89df-52fd5ae9de63 · outbound

This paper cites Adaptable butterfly accelerator for attention-based nns via hardware and algorithm co-design,.

Refining Datapath for Microscaling ViTs Adaptable butterfly accelerator for attention-based nns via hardware and algorithm co-design,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:22.926207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a4c4702f-6e33-447d-a5f4-de7bb2619b3b · outbound

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

Refining Datapath for Microscaling ViTs Aˆ 3: Accelerating attention mechanisms in neural networks with approximation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:22.704664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5b6e130a-1f18-44a6-b621-6f3ee4ead198 · outbound

This paper cites Dfx: A low-latency multi-fpga appliance for accelerating transformer-based text generation,.

Refining Datapath for Microscaling ViTs Dfx: A low-latency multi-fpga appliance for accelerating transformer-based text generation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:22.593603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b45650bb-52a9-40ae-8188-3cb9c7c13eae · outbound

This paper cites Flat: An optimized dataflow for mitigating attention bottlenecks,.

Refining Datapath for Microscaling ViTs Flat: An optimized dataflow for mitigating attention bottlenecks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:22.411976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 11413356-36d0-4d8e-8aea-4b57b13254fb · outbound

This paper cites Sanger: A co-design framework for enabling sparse attention using reconfigurable architecture,.

Refining Datapath for Microscaling ViTs Sanger: A co-design framework for enabling sparse attention using reconfigurable architecture,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:22.290465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5bd772ea-e322-411f-9197-bfed3fed9ce2 · outbound

This paper cites Gobo: Quan- tizing attention-based nlp models for low latency and energy efficient inference,.

Refining Datapath for Microscaling ViTs Gobo: Quan- tizing attention-based nlp models for low latency and energy efficient inference,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:22.099546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 10fa9f0b-4741-4267-aabd-a4e4ce315d34 · outbound

This paper cites Edgebert: Sentence-level energy optimizations for latency-aware multi- task nlp inference,.

Refining Datapath for Microscaling ViTs Edgebert: Sentence-level energy optimizations for latency-aware multi- task nlp inference,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:21.918681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9c750e4a-9a59-4d1d-b10f-6704424c44ad · outbound

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

Refining Datapath for Microscaling ViTs Fact: Ffn-attention co-optimized transformer architecture with eager correlation prediction,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:21.652869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.