Paper Citation Record · LEDGER

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies

As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2412.08466.

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

pith.paper-citation-record.v1
2412.08466 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:48:17.645705Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

29 of 29 outbound references displayed

verified exact0
verified fuzzy27
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bbf9ae37-d11d-4261-bc6e-45f4d32e2ba1 · outbound

This paper cites paella: Edge ai-based real-time malware detection in data centers,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies paella: Edge ai-based real-time malware detection in data centers,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:18.097552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 37b343d0-5b28-4fae-9186-8d0fb7cc1788 · outbound

This paper cites Nvidia drive for automotive,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Nvidia drive for automotive,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:18.083322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 597d8288-5230-4674-bee0-4450faf6f68a · outbound

This paper cites Amd instinct,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Amd instinct,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:18.068995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d1eb10e4-4893-4e55-80df-15d9304ff16b · outbound

This paper cites Silent Data Corruptions at Scale.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Silent Data Corruptions at Scale

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:17.531364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 228757cb-4590-475f-adda-48131c416fa1 · outbound

This paper cites Cores that don’t count,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Cores that don’t count,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:18.054910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 09ebfd32-e8d6-4152-9916-f7ea0bdf4224 · outbound

This paper cites Silent data errors: Sources, detection, and modeling,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Silent data errors: Sources, detection, and modeling,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:18.040393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0827df32-c3fe-4ec9-8e95-3a0ee1775a31 · outbound

This paper cites Transient-fault-aware design and training to enhance dnns reliability with zero-overhead,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Transient-fault-aware design and training to enhance dnns reliability with zero-overhead,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:18.024914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d76840e8-3887-4cba-9e4c-8c29b412ca4d · outbound

This paper cites Practical hardening of crash-tolerant systems,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Practical hardening of crash-tolerant systems,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:18.009544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c3e14e95-b33d-4419-a7af-b04982218f8b · outbound

This paper cites an unresolved cited work.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:48:17.993689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a67ed110-1a4f-496f-90b6-5498dac1a88c · outbound

This paper cites Algorithm based fault tolerance: Review and experimental study,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Algorithm based fault tolerance: Review and experimental study,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.980026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5150f61e-0896-4311-83b0-7e46daff8594 · outbound

This paper cites A low-cost fault corrector for deep neural networks through range restriction,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies A low-cost fault corrector for deep neural networks through range restriction,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.966208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3f39e97f-ff5d-462d-9aad-2fc3f3f18d27 · outbound

This paper cites Enhancing the reliability of split computing deep neural networks,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Enhancing the reliability of split computing deep neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.952122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 16c8cddc-c200-4c67-9de3-e671bf5d1164 · outbound

This paper cites Boosting bit-error resilience of dnn accelerators through median feature selection,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Boosting bit-error resilience of dnn accelerators through median feature selection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.939337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b556f746-3937-42e7-80ce-0bffb76295d5 · outbound

This paper cites Salvagednn: salvaging deep neural network accelerators with permanent faults through saliency-driven fault-aware mapping,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Salvagednn: salvaging deep neural network accelerators with permanent faults through saliency-driven fault-aware mapping,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.925929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1fea52c5-26a9-44ed-8550-7c15a28e2869 · outbound

This paper cites Reliability and security of ai hardware,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Reliability and security of ai hardware,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.912004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8a7972c4-cf51-4326-9802-6f4f5ee24d29 · outbound

This paper cites Pros and cons of fault injection approaches for the reliability assessment of deep neural networks,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Pros and cons of fault injection approaches for the reliability assessment of deep neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.898361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4939390e-c17d-47fe-a090-cc6e4b0527f3 · outbound

This paper cites Pytorchfi: A runtime perturbation tool for dnns,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Pytorchfi: A runtime perturbation tool for dnns,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.884143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 06c5f028-daeb-4227-a90f-ff97176c5789 · outbound

This paper cites Evaluating convolutional neural networks reliability depending on their data representation,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Evaluating convolutional neural networks reliability depending on their data representation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.869364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b5249595-c3f0-4386-8ece-42891029ee5e · outbound

This paper cites The impact of faults on dnns: A case study,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies The impact of faults on dnns: A case study,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.854775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b1fedacf-f2af-4ca7-8aef-4700953102cf · outbound

This paper cites Nvbitfi: Dynamic fault injection for gpus,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Nvbitfi: Dynamic fault injection for gpus,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.839654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a43ce94e-26cd-43fb-9d50-62e4f8eb1968 · outbound

This paper cites Sassifi: An architecture-level fault injection tool for gpu application resilience evaluation,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Sassifi: An architecture-level fault injection tool for gpu application resilience evaluation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.824559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3b4b0ff0-d1c1-4873-bd55-1485af222747 · outbound

This paper cites Evaluating the impact of permanent faults in a gpu running a deep neural network,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Evaluating the impact of permanent faults in a gpu running a deep neural network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.809713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0c5381ff-1663-4dfc-ba6f-12fd87007e6f · outbound

This paper cites Reliability assessment of neural networks in gpus: A framework for permanent faults injections,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Reliability assessment of neural networks in gpus: A framework for permanent faults injections,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.795489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f913df3e-509a-4705-b061-06bd533762aa · outbound

This paper cites A multi-level approach to evaluate the impact of gpu permanent faults on cnn’s reliability,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies A multi-level approach to evaluate the impact of gpu permanent faults on cnn’s reliability,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.779986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4c00658d-46fd-440d-b14a-3528f221097d · outbound

This paper cites Demystifying the system vulnerability stack: Transient fault effects across the layers,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Demystifying the system vulnerability stack: Transient fault effects across the layers,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.762501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a97ae1c3-ceb3-49e7-baa3-857f51e86448 · outbound

This paper cites Gufi: A framework for gpus reliability assessment,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Gufi: A framework for gpus reliability assessment,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.747206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8a1bc618-c2e7-44c5-8d34-4475e40dfb60 · outbound

This paper cites An effective method to identify microarchitectural vulnerabilities in gpus,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies An effective method to identify microarchitectural vulnerabilities in gpus,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.731901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f2eefb32-bfd1-413c-b6e5-f04983264683 · outbound

This paper cites Assessing convolutional neural networks reliability through statistical fault injections,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Assessing convolutional neural networks reliability through statistical fault injections,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.716386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c7039443-6d2e-4d96-be7e-a5776c2db497 · outbound

This paper cites Evaluating the reliability of supervised compression for split computing,.

Evaluating Different Fault Injection Abstractions on the Assessment of DNN SW Hardening Strategies Evaluating the reliability of supervised compression for split computing,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:17.701042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.