Paper Citation Record · LEDGER

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration

As of 8 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2506.01166.

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

pith.paper-citation-record.v1
2506.01166 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:55:29.258479Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

17 of 17 outbound references displayed

verified exact0
verified fuzzy9
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 724b1829-4334-4bd1-97a6-60e910b0a329 · outbound

This paper cites A survey of convolutional neural networks: analysis, applications, and prospects,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration A survey of convolutional neural networks: analysis, applications, and prospects,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:29.200805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 424cec5d-0ab4-4796-b445-6c86bd1fa044 · outbound

This paper cites In-datacenter performance analysis of a tensor pro- cessing unit,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration In-datacenter performance analysis of a tensor pro- cessing unit,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:29.433043Z

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 2b26b5d7-06c9-4a99-bea6-1483b22b7dd9 · outbound

This paper cites An accelerator for sparse convolutional neural networks leveraging systolic general matrix- matrix multiplication,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration An accelerator for sparse convolutional neural networks leveraging systolic general matrix- matrix multiplication,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:29.209462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfcfcd10-588f-44a3-b1e3-21987a772761 · outbound

This paper cites Sense: Model-hardware codesign for accelerating sparse cnns on systolic arrays,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Sense: Model-hardware codesign for accelerating sparse cnns on systolic arrays,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:29.413944Z

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 f71d791b-5cdd-40a2-a21e-8643fc50fd1d · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:29.217471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be6c2955-9d85-4a7c-8c04-5b65047bbc05 · outbound

This paper cites Eie: Efficient inference engine on compressed deep neural network,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Eie: Efficient inference engine on compressed deep neural network,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:29.401370Z

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 3a6f4499-1389-4003-acdf-0047b46fd10c · outbound

This paper cites Synapse compression for event-based convolutional-neural-network ac- celerators,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Synapse compression for event-based convolutional-neural-network ac- celerators,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:29.390545Z

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 c40d8077-3e4b-4a12-97d8-8c096bdf2b20 · outbound

This paper cites Exploiting neural-network statistics for low-power DNN inference,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Exploiting neural-network statistics for low-power DNN inference,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:29.380532Z

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 2125ba85-8404-45a2-a711-739c6e614d22 · outbound

This paper cites Why systolic architectures?.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Why systolic architectures?

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:29.370099Z

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 f1211558-a3c0-4a09-9642-52b8249e9b39 · outbound

This paper cites SCALE-Sim: Systolic CNN Accelerator Simulator.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration SCALE-Sim: Systolic CNN Accelerator Simulator

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:29.235438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2029e7b2-4904-49b1-8b69-5a696c9a9b74 · outbound

This paper cites Cnvlutin: Ineffectual-neuron-free deep neural network computing,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Cnvlutin: Ineffectual-neuron-free deep neural network computing,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:29.360307Z

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 a8805433-98c7-4c46-be94-4023ff0b063e · outbound

This paper cites Eyeriss: An energy- efficient reconfigurable accelerator for deep convolutional neural net- works,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Eyeriss: An energy- efficient reconfigurable accelerator for deep convolutional neural net- works,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:29.242055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2691a899-e225-4688-982d-ca5f3209d007 · outbound

This paper cites SparseZoo: Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration SparseZoo: Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:29.343119Z

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 015bb9d5-4218-4b54-90d6-8fb0ad744151 · outbound

This paper cites Deep residual learning for image recognition,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Deep residual learning for image recognition,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:29.248393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c045d81d-c5de-48ff-b021-296997253ce0 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:29.251613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7086c92d-67e9-4bcc-9925-3b5b52bf5843 · outbound

This paper cites S2ta: Exploiting structured sparsity for energy-efficient mobile cnn acceleration,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration S2ta: Exploiting structured sparsity for energy-efficient mobile cnn acceleration,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:29.254628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9f53e92-4dd3-41dd-9e7a-3f61c18b6e6c · outbound

This paper cites Scnn: An accelerator for compressed-sparse convolutional neural networks,.

VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration Scnn: An accelerator for compressed-sparse convolutional neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:55:29.315551Z

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.