Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective | Paper Citation Record

Observation d49c1d64-e5ac-49fc-bd79-8628fd287a58 · outbound

This paper cites Improving aircraft per- formance using machine learning: A review.Aerospace Science and Technology, page 108354, 2023.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Improving aircraft per- formance using machine learning: A review.Aerospace Science and Technology, page 108354, 2023

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Observation 58367bd5-c65b-41af-8050-1a6efd101a47 · outbound

This paper cites Guidelines for Development of Civil AircraftandSystems.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Guidelines for Development of Civil AircraftandSystems

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Observation 4beeeffa-0f20-41fd-b2cf-51a40052fd4d · outbound

This paper cites Road vehicles – Functional safety.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Road vehicles – Functional safety

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Observation 0580b965-f371-4860-baad-6f06dfa914de · outbound

This paper cites Impact of terrestrial neutrons on the reliability of sic vd-mosfet technologies.IEEE Transactions on Nuclear Science, 68(5):634–641, 2021.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Impact of terrestrial neutrons on the reliability of sic vd-mosfet technologies.IEEE Transactions on Nuclear Science, 68(5):634–641, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.954358Z

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Observation b82bcd3a-3046-46cd-af4b-f835f24a60fa · outbound

This paper cites Soft errors in advanced computer systems.IEEE design & test of computers, 22(3):258–266, 2005.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Soft errors in advanced computer systems.IEEE design & test of computers, 22(3):258–266, 2005

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.946318Z

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Observation d738510b-40fa-4ade-afc9-b47b3a7caf4a · outbound

This paper cites Multiple sensitive volume based soft error rate estimation with machine learning.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Multiple sensitive volume based soft error rate estimation with machine learning

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Observation e9320e3d-41da-4c9c-8521-df371c0145a6 · outbound

This paper cites When single event upset meets deep neural networks: Observations, explorations, and remedies.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective When single event upset meets deep neural networks: Observations, explorations, and remedies

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.929154Z

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Observation 0f30d2db-a3eb-44f7-8c3e-0f0711d7888c · outbound

This paper cites Advances in emerging photonic memristive and memristive-like devices.Ad- vanced Science, 9(28):2105577, 2022.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Advances in emerging photonic memristive and memristive-like devices.Ad- vanced Science, 9(28):2105577, 2022

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.920509Z

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Observation df9f2147-6af3-4311-8a5f-fd75796d7cac · outbound

This paper cites Wesco: Weight-encoded reliability and security co-design for in-memory computing systems.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Wesco: Weight-encoded reliability and security co-design for in-memory computing systems

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.911580Z

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Observation 4addafbe-262f-4789-847c-ba3ccc4bcc2e · outbound

This paper cites Flip- ping bits in memory without accessing them: An experimental study of dram disturbance errors.ACM SIGARCH Computer Architecture News, 42(3):361–372, 2014.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Flip- ping bits in memory without accessing them: An experimental study of dram disturbance errors.ACM SIGARCH Computer Architecture News, 42(3):361–372, 2014

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.902795Z

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Observation 97789d29-c72f-4631-bd82-f8ca81218185 · outbound

This paper cites Variation-aware static and dynamic writability analysis for voltage-scaled bit-interleaved 8-t srams.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Variation-aware static and dynamic writability analysis for voltage-scaled bit-interleaved 8-t srams

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.893820Z

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Observation f7544f47-13a0-4d16-96ea-8f073bdcb554 · outbound

This paper cites Flip feng shui: Hammering a needle in the software stack.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Flip feng shui: Hammering a needle in the software stack

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.884509Z

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Observation 19076d0c-2700-401a-b318-889a97a796ab · outbound

This paper cites {DeepHammer}: Depleting the intelligence of deep neural networks through targeted chain of bit flips.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective {DeepHammer}: Depleting the intelligence of deep neural networks through targeted chain of bit flips

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.875262Z

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Observation 12172015-a909-45a9-a6f6-85ba3e94b808 · outbound

This paper cites Aegis: Mitigating targeted bit-flip attacks against deep neural networks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Aegis: Mitigating targeted bit-flip attacks against deep neural networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.865810Z

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Observation cf7d837c-6602-4387-b922-66e80030ad7f · outbound

This paper cites Internimage: Exploring large-scale vision foundation models with deformable convolutions, 2023.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Internimage: Exploring large-scale vision foundation models with deformable convolutions, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.856788Z

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Observation a42fc3ab-296b-4927-987a-2f7613b2b1d1 · outbound

This paper cites Reorda,andA.Paccagnella.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Reorda,andA.Paccagnella

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Observation 9d8213d3-6df7-4f17-b8d6-87e60a658afe · outbound

This paper cites Selective hardening of critical neurons in deep neural networks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Selective hardening of critical neurons in deep neural networks

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Observation 0518b4dd-3ed3-4e75-b162-c85a8b83ee0f · outbound

This paper cites Explo- ration of activation fault reliability in quantized systolic array-based dnn accelerators, 2024.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Explo- ration of activation fault reliability in quantized systolic array-based dnn accelerators, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.837683Z

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Observation 5d8e8f25-c4bc-41ec-bbff-d0e9953ae284 · outbound

This paper cites A survey of quantization methods for efficient neural network inference.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective A survey of quantization methods for efficient neural network inference

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.829387Z

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Observation 9a0e46e5-93ff-49b2-9855-74e244e6c583 · outbound

This paper cites parameterprotection, 2024.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective parameterprotection, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.821041Z

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Observation f7b69219-9544-4f2e-9c83-54528a612585 · outbound

This paper cites Tensorfi2, 2024.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Tensorfi2, 2024

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Observation 649e0452-1299-4980-9d40-f65b5ece312a · outbound

This paper cites Fault injection for ten- sorflow applications.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Fault injection for ten- sorflow applications

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Observation 232c259b-4a9a-4c8c-803e-ab255b4d17d4 · outbound

This paper cites A systematic literature review onhardwarereliabilityassessmentmethodsfordeepneuralnetworks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective A systematic literature review onhardwarereliabilityassessmentmethodsfordeepneuralnetworks

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Resolution: unresolved; no resolver link, observed 2026-08-11T22:26:28.925438Z

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Observation 0bb6d436-cb29-48fb-a586-8449161e2dbc · outbound

This paper cites The robustness of modern deep learning architectures against single event upset errors.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective The robustness of modern deep learning architectures against single event upset errors

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Observation 6f4d6c36-cef5-4661-8177-994adaca8067 · outbound

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

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective The impact of faults on dnns: A case study

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Observation adcc7950-5fee-4430-a04a-9334a72168c1 · outbound

This paper cites The effect of weight errors on neural networks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective The effect of weight errors on neural networks

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Observation fc599c34-2e6c-487e-b479-a2688294067a · outbound

This paper cites Are cnns reliable enough for critical applications? an exploratory study.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Are cnns reliable enough for critical applications? an exploratory study

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Observation 8d0eed6f-296b-442e-b4e7-8bd3a55366c2 · outbound

This paper cites Eval- uating fault resiliency of compressed deep neural networks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Eval- uating fault resiliency of compressed deep neural networks

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Observation 82858280-3e2d-4b97-b692-e87f18e56d0c · outbound

This paper cites Reliability evaluation of compresseddeeplearningmodels.In 2020IEEE11thLatinAmerican SymposiumonCircuits&Systems(LASCAS) ,pages1–5.IEEE,2020.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Reliability evaluation of compresseddeeplearningmodels.In 2020IEEE11thLatinAmerican SymposiumonCircuits&Systems(LASCAS) ,pages1–5.IEEE,2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.758623Z

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Observation 9ae8efed-d285-4499-8daf-d2c23e661683 · outbound

This paper cites IEEE, 2019.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective IEEE, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.750099Z

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Observation 285ccfc9-a7e0-45e5-ba78-793496a813df · outbound

This paper cites Assessing convolutional neural net- works reliability through statistical fault injections.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Assessing convolutional neural net- works reliability through statistical fault injections

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Observation 8a836725-e305-4b77-8542-24a3ec980a1a · outbound

This paper cites Terminal brain damage: Exposing the graceless degradation in deep neural networks under hardware fault attacks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Terminal brain damage: Exposing the graceless degradation in deep neural networks under hardware fault attacks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.732790Z

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Observation b4de616e-022d-4b27-b65e-ada2c3f8f7c3 · outbound

This paper cites FaultinjectioninMachineLearningapplica- tions.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective FaultinjectioninMachineLearningapplica- tions

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Observation 2414d508-7843-488b-8459-c2a086e144b0 · outbound

This paper cites Evaluating convolutional neural networks reliability de- pending on their data representation.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Evaluating convolutional neural networks reliability de- pending on their data representation

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Observation cecb4816-569e-4e46-a499-7812b7454017 · outbound

This paper cites Investigating data representation for efficientandreliableconvolutionalneuralnetworks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Investigating data representation for efficientandreliableconvolutionalneuralnetworks

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Observation 06492f0c-1f08-49d8-b91e-76f2cf95929b · outbound

This paper cites In 2021 IEEE 32nd International Conference on Microelectronics (MIEL), pages 275–279.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective In 2021 IEEE 32nd International Conference on Microelectronics (MIEL), pages 275–279

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.698071Z

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Observation b7c0d44b-25c9-49ae-b070-0808747e288a · outbound

This paper cites Defending and harnessing the bit-flip based adversarial weight attack.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Defending and harnessing the bit-flip based adversarial weight attack

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.689366Z

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Observation e3e3750e-a411-416f-9b71-557b8fbba746 · outbound

This paper cites Defending bit-flip attack through dnn weight reconstruction.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Defending bit-flip attack through dnn weight reconstruction

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.680394Z

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Observation 279a0b65-e6e1-4aa2-829d-2d79ebe8bd0b · outbound

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Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Bit-flip attack: Crushing neural network with progressive bit search

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Observation 1242aab6-0d0e-43fe-891a-e33497fae496 · outbound

This paper cites On pixel- wise explanations for non-linear classifier decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective On pixel- wise explanations for non-linear classifier decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.666111Z

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Observation 92d785a5-ba94-4188-bf98-6686d2e2aa57 · outbound

This paper cites Sensitivity based error resilient techniques for energy efficient deep neural network accelerators.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Sensitivity based error resilient techniques for energy efficient deep neural network accelerators

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Source-reported events for the cited work

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Observation 7fb1d80f-1759-4c73-9689-643d00247604 · outbound

This paper cites Estimating vulnerability of all model parameters in dnn with a small number of fault injections.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Estimating vulnerability of all model parameters in dnn with a small number of fault injections

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.648785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 78ce9d0f-6c16-49fb-a151-db93565c281c · outbound

This paper cites Binfi: An efficient fault injector for safety-critical machine learning systems.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Binfi: An efficient fault injector for safety-critical machine learning systems

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cbc53573-b34f-4f07-9040-1c7bd6874431 · outbound

This paper cites Improving fault tolerance for reliable dnn using boundary-aware activation.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Improving fault tolerance for reliable dnn using boundary-aware activation

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 02540ebe-c3a7-48e1-812d-56d8a4024eaa · outbound

This paper cites Improvingdnn fault tolerance in semantic segmentation applications.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Improvingdnn fault tolerance in semantic segmentation applications

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.622675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9de400cc-9d18-4cee-8f23-70a9bd3d7625 · outbound

This paper cites ReliabilityEvaluationofSplitComputingNeural Networks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective ReliabilityEvaluationofSplitComputingNeural Networks

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 23629e88-60e1-4f4f-9886-c282842ea216 · outbound

This paper cites A fast reliability analysis of image segmentation neural networks exploiting statistical fault injections.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective A fast reliability analysis of image segmentation neural networks exploiting statistical fault injections

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Source-reported events for the cited work

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Observation cb1b8b85-07ba-4a91-a28c-cdc774aeedcd · outbound

This paper cites Ontheevaluationofsoft-errorsdetectiontechniquesfor gpgpus.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Ontheevaluationofsoft-errorsdetectiontechniquesfor gpgpus

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.597402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 87397268-133e-437b-9b16-090420126f34 · outbound

This paper cites Modern gpus radiation sensitivity evaluation and mitigation through duplication with com- parison.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Modern gpus radiation sensitivity evaluation and mitigation through duplication with com- parison

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.589948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 799b0f6e-dba7-4325-9469-0e03b1ee3ecb · outbound

This paper cites Kernel vulnerability factor and efficient hardening for his- togramoforientedgradients.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Kernel vulnerability factor and efficient hardening for his- togramoforientedgradients

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.581766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0f367441-572b-4243-83e1-252122b5ba22 · outbound

This paper cites Selective hardeningforneuralnetworksinfpgas.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Selective hardeningforneuralnetworksinfpgas

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.573465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 887160c0-3ce2-4c69-a754-3c8e71b7f07d · outbound

This paper cites Selective hardening of cnns based on layer vulnerability estimation.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Selective hardening of cnns based on layer vulnerability estimation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.564751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12566de7-a262-4e1e-8e85-abe5e3409114 · outbound

This paper cites Analyzing and increasing the reliability of convolutional neural net- works on gpus.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Analyzing and increasing the reliability of convolutional neural net- works on gpus

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.555977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5e0c89e7-16cf-4d95-ba01-6066f48aabdc · outbound

This paper cites A methodology for selective protection of matrixmultiplications:Adiagnosticcoverageandperformancetrade- off for cnns executed on gpus.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective A methodology for selective protection of matrixmultiplications:Adiagnosticcoverageandperformancetrade- off for cnns executed on gpus

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.547829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d4e6dd0d-a62b-463f-b99e-ad9952a7baf5 · outbound

This paper cites Reduced precision dwc: An efficient hardening strategy for mixed- precisionarchitectures.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Reduced precision dwc: An efficient hardening strategy for mixed- precisionarchitectures

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.539005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 640611f0-5fa0-4f67-8470-f040402f7a19 · outbound

This paper cites Ft-clipact: Resilience analysis of deep neural networks and improving their fault tolerance using clipped activation.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Ft-clipact: Resilience analysis of deep neural networks and improving their fault tolerance using clipped activation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.530225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b745c9a-d0f2-440c-8a68-98231118ee3c · outbound

This paper cites Alow-costfault corrector for deep neural networks through range restriction.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Alow-costfault corrector for deep neural networks through range restriction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.521652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 269e04a0-6260-4d4d-8560-afb2d0633e91 · outbound

This paper cites Fitact: Error resilient deep neural networks via fine-grained post- trainable activation functions.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Fitact: Error resilient deep neural networks via fine-grained post- trainable activation functions

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.512350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5452d111-2d71-4b5b-ac70-617028abf6f9 · outbound

This paper cites An efficient bit-flip resilience optimization method for deep neural networks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective An efficient bit-flip resilience optimization method for deep neural networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.503662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0432b3c8-2bb1-433e-bb3e-b10855edc2a7 · outbound

This paper cites Mate:Memory-andretraining- free error correction for convolutional neural network weights.Jour- nal of Information & Communication Convergence Engineering, 19 (1), 2021.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Mate:Memory-andretraining- free error correction for convolutional neural network weights.Jour- nal of Information & Communication Convergence Engineering, 19 (1), 2021

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.495143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 72a42fb2-7a25-4d06-a261-90d836cf6549 · outbound

This paper cites Zero-overhead protection for cnn weights.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Zero-overhead protection for cnn weights

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.486541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dae536c9-5624-4d84-abde-e6bd104537ee · outbound

This paper cites Bipolarvectorclassifier for fault-tolerant deep neural networks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Bipolarvectorclassifier for fault-tolerant deep neural networks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.478807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f975b5c7-320d-49e1-99b0-30ba4473d5aa · outbound

This paper cites Accelerated radiation test on quantized neural networks trained with fault aware training.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Accelerated radiation test on quantized neural networks trained with fault aware training

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.470865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bc9dca79-2e3d-4023-ac79-cea66b47e9ee · outbound

This paper cites Detecting errors in convolutional neural networks using inter frame spatio-temporal correlation.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Detecting errors in convolutional neural networks using inter frame spatio-temporal correlation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.462849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 67c618f2-344e-4a49-b420-ba5a83eaf9a7 · outbound

This paper cites Hashtag: Hash signatures foronlinedetectionoffault-injectionattacksondeepneuralnetworks.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Hashtag: Hash signatures foronlinedetectionoffault-injectionattacksondeepneuralnetworks

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T22:26:29.058383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 890c2ded-7074-4dd1-907b-ca1185ff68ab · outbound

This paper cites Hsi-drivev2.0:Moredata fornewchallengesinsceneunderstandingforautonomousdriving.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Hsi-drivev2.0:Moredata fornewchallengesinsceneunderstandingforautonomousdriving

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.453616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 93e0bb2c-f3a7-479f-806d-e32d62779aed · outbound

This paper cites Device reliability report.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Device reliability report

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.444067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 90c9bb81-eff9-435e-ab02-29518813f40f · outbound

This paper cites Comparative study: Autodpr-sem for enhancing cnn reliability in sram-based fpgas through au- tonomousreconfiguration.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Comparative study: Autodpr-sem for enhancing cnn reliability in sram-based fpgas through au- tonomousreconfiguration

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.434936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 78f80df7-0998-4b97-93d6-25a8f03da214 · outbound

This paper cites Quantization and training of neural networks for efficient integer- arithmetic-only inference.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Quantization and training of neural networks for efficient integer- arithmetic-only inference

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.424835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d58ead66-ec00-4a1a-b795-eb78a47eac72 · outbound

This paper cites Integer-only cnns with 4 bit weights and bit-shift quantization scales at full-precision accuracy.Electronics, 10(22):2823, 2021.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Integer-only cnns with 4 bit weights and bit-shift quantization scales at full-precision accuracy.Electronics, 10(22):2823, 2021

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.415356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e2f6a98c-0143-4b41-b8a1-7dd00446c0fc · outbound

This paper cites Efficient Post-training Quantization with FP8 Formats.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Efficient Post-training Quantization with FP8 Formats

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T22:26:29.081033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 596df608-6444-42e1-9825-f7ab3b5e55c2 · outbound

This paper cites Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T22:26:29.084544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8852adbe-93b9-4013-8230-4208c941ff63 · outbound

This paper cites Leveugle, A.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Leveugle, A

Reference 73

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T22:26:29.234630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d96f3db3-b59d-4a92-bb24-dc4a39f35c79 · outbound

This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T22:26:29.090927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b048478c-77ef-4317-b9d0-fb39d86cab22 · outbound

This paper cites Data-free quantization through weight equalization and bias correction.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective Data-free quantization through weight equalization and bias correction

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:26:29.405990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ce716dfc-114f-4f75-8bde-7a8b5f1e31df · outbound

This paper cites doi:https://doi.org/10.1016/j.microrel.2024.

Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective doi:https://doi.org/10.1016/j.microrel.2024

Reference 2024

Resolution: verified exact; doi, observed 2026-08-11T22:26:29.123655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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