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Paper Citation Record · LEDGER

Performance Isolation for Inference Processes in Edge GPU Systems

As of 7 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2601.07600.

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

pith.paper-citation-record.v1
2601.07600 v3

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:05:03.351273Z

measured 24 of 24 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T05:03:10.471589Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T21:36:13.551351Z

Reference resolution

23 of 23 outbound references displayed

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External citation measurements

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Outbound references

Observation 5302b093-3c0f-451b-aa65-bb54dc5bf7b3 · outbound

This paper cites Road vehicles — functional safety,.

Performance Isolation for Inference Processes in Edge GPU Systems Road vehicles — functional safety,

Reference 1

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source=pdf_text observed=2026-08-03T11:05:02.299616Z digest=sha256:a49dee6c8e73c1119313297ab07b336df2ab87b124566d6be0aeab11544c4647

Observation 96d5b14a-c309-4c9c-bed3-4f0d99d1b41b · outbound

This paper cites Artificial intelligence — functional safety and ai systems,.

Performance Isolation for Inference Processes in Edge GPU Systems Artificial intelligence — functional safety and ai systems,

Reference 2

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source=pdf_text observed=2026-08-03T11:05:02.382348Z digest=sha256:38d9e001722d29b8035d30dbd9f999d54b77dd53f86de5c1f7d556ed0de27ddf

Observation 2b824959-5909-44a9-97fd-6586104eb033 · outbound

This paper cites On Neural Networks Redundancy and Diversity for Their Use in Safety-Critical Systems,.

Performance Isolation for Inference Processes in Edge GPU Systems On Neural Networks Redundancy and Diversity for Their Use in Safety-Critical Systems,

Reference 3

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source=pdf_text observed=2026-08-03T11:05:02.487709Z digest=sha256:f7795d0e37a964d98e43ec0df5d2ecb10644b662d3b27a8e56019a1a9628ffd4

Observation 0a47f648-f63d-45f5-b421-983be08f8ffc · outbound

This paper cites Exploring diversity in neural architectures for safety,.

Performance Isolation for Inference Processes in Edge GPU Systems Exploring diversity in neural architectures for safety,

Reference 4

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source=pdf_text observed=2026-08-03T11:05:02.555572Z digest=sha256:af49e75d1d56ceaae123301c3143d0f83a2a77cede6f54ec704801c1726a6f3d

Observation 2c747c5d-9ce6-4e99-bfd1-cfdefa6e8574 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Performance Isolation for Inference Processes in Edge GPU Systems Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 5

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source=pdf_text observed=2026-08-03T11:05:02.591109Z digest=sha256:58428096c7ec93555e23263ef3ef1343f043fbb1167a6062b40ca1cbfe3a96fe

Observation 620e9b5e-91b9-40d9-b464-432979652712 · outbound

This paper cites Ensemble learning: A survey,.

Performance Isolation for Inference Processes in Edge GPU Systems Ensemble learning: A survey,

Reference 6

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source=pdf_text observed=2026-08-03T11:05:02.606932Z digest=sha256:ae15728a7c19a21acb0c3fc4a47c0eb5d781c2b7852587da230db0abba6cb27e

Observation d319dd07-d89f-47d7-9111-c28e0f7a5499 · outbound

This paper cites Edge intelligence: A review of deep neural network inference in resource-limited environments,.

Performance Isolation for Inference Processes in Edge GPU Systems Edge intelligence: A review of deep neural network inference in resource-limited environments,

Reference 7

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source=pdf_text observed=2026-08-03T11:05:02.652317Z digest=sha256:8f050bd46fbd2806da7e0a79c48d626a4bbb5e7fc0578fd383bd7a38fe9b1196

Observation 72cd0539-ef32-4bb5-ae0e-29e71e1e5070 · outbound

This paper cites Multi-process service (mps) overview.

Performance Isolation for Inference Processes in Edge GPU Systems Multi-process service (mps) overview

Reference 8

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source=pdf_text observed=2026-08-03T11:05:02.674936Z digest=sha256:f1533cc9a5b6a5b3b77a33d00ce02d49f41d067eb6c46b89f8da9195e34af638

Observation 52904837-79d0-433a-b521-cdb655992531 · outbound

This paper cites Multi-instance gpu (mig) technology.

Performance Isolation for Inference Processes in Edge GPU Systems Multi-instance gpu (mig) technology

Reference 9

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source=pdf_text observed=2026-08-03T11:05:02.724280Z digest=sha256:79f68bdc0c92ec70e7f9103051dbd2f590b9428b39bda70bb684227d3ffd0f05

Observation 1fb03585-9618-4594-85f5-a7f3ea99e854 · outbound

This paper cites Cuda driver api documentation, section 6.35: Green contexts.

Performance Isolation for Inference Processes in Edge GPU Systems Cuda driver api documentation, section 6.35: Green contexts

Reference 10

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source=pdf_text observed=2026-08-03T11:05:02.794636Z digest=sha256:7ffa31064ffd12a57a8e6e99db2633496856b084ac40aa66cc8c7e53214de987

Observation a8f93222-c144-4fd3-9e4b-7f71e747f620 · outbound

This paper cites Cuda programming guide, section 4.6: Green contexts.

Performance Isolation for Inference Processes in Edge GPU Systems Cuda programming guide, section 4.6: Green contexts

Reference 11

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source=pdf_text observed=2026-08-03T11:05:02.857404Z digest=sha256:86126dd9e04eda3cb78c4d8de6893879ee06efdaef4b4307b51665286ae4ef01

Observation cf2ea594-cdf0-408d-9822-ca4c4d878a44 · outbound

This paper cites Hugging face platform.

Performance Isolation for Inference Processes in Edge GPU Systems Hugging face platform

Reference 12

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Observation 4baeb9e5-58bb-40cc-9294-6b47e0e591d8 · outbound

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

Performance Isolation for Inference Processes in Edge GPU Systems Imagenet: A large-scale hierarchical image database,

Reference 13

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source=pdf_text observed=2026-08-03T11:05:02.920475Z digest=sha256:7237076636e08e4b9ca1caa0ac8633d52742de19f6654f6ffac2f0650c53600b

Observation b7b67d51-e4b1-43fe-9383-5a493b71e479 · outbound

This paper cites On Accelerating Edge AI: Optimizing Resource-Constrained Environments.

Performance Isolation for Inference Processes in Edge GPU Systems On Accelerating Edge AI: Optimizing Resource-Constrained Environments

Reference 14

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source=pdf_text observed=2026-08-03T11:05:02.953719Z digest=sha256:f50285e50e22792a7d84b7d1d4c82ab32f09279623465bea1a5b5e85ee35ad13

Observation f47a2636-4f71-479a-87c0-8f93501253e5 · outbound

This paper cites Marine objects detection using deep learning on embedded edge devices,.

Performance Isolation for Inference Processes in Edge GPU Systems Marine objects detection using deep learning on embedded edge devices,

Reference 15

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source=pdf_text observed=2026-08-03T11:05:03.025999Z digest=sha256:2463e2b4a3b7edddc91c3aa292967157208869093626e11846ac9be81978fcd1

Observation 1324f576-8505-47b0-8e09-734a45f925c5 · outbound

This paper cites MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs.

Performance Isolation for Inference Processes in Edge GPU Systems MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs

Reference 16

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source=pdf_text observed=2026-08-03T11:05:03.061977Z digest=sha256:deb5cc8fb24a75e1c447b2d8924e2b8a28ebec23abeb83b4e2791019a8f059a1

Observation 90f5b6e3-953c-4ad0-9be9-b34ae5342bfa · outbound

This paper cites A convnet for the 2020s,.

Performance Isolation for Inference Processes in Edge GPU Systems A convnet for the 2020s,

Reference 17

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source=pdf_text observed=2026-08-03T11:05:03.138880Z digest=sha256:ce5f0c7c964b956b3e88bc634dde8acaede7560bc90812798ae213dfdeb99c22

Observation 06d2eaa9-38a7-4012-9494-fa862a0620bd · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

Performance Isolation for Inference Processes in Edge GPU Systems Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 18

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source=pdf_text observed=2026-08-03T11:05:03.157529Z digest=sha256:696d022fc814c88cd02ddf106c175f9484de62e234ecacc0db79db0a66abb4a2

Observation 1bef1f54-04a5-4466-9771-7c633c97320f · outbound

This paper cites Deep residual learning for image recognition,.

Performance Isolation for Inference Processes in Edge GPU Systems Deep residual learning for image recognition,

Reference 19

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source=pdf_text observed=2026-08-03T11:05:03.196398Z digest=sha256:0ce2ca649dfd3b276a919504ccaf92aab3e84ab4da2a2e1ff04627fd967ce145

Observation b33c4c69-a8be-47f5-a157-22fd9f37a332 · outbound

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

Performance Isolation for Inference Processes in Edge GPU Systems An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 20

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source=pdf_text observed=2026-08-03T11:05:03.211825Z digest=sha256:74e499975cbc7e163f35473fb963c2c5a3411057dda6de0a97be5b65b3925771

Observation 277f6737-ce40-4361-8ac3-235ecea738d1 · outbound

This paper cites Characterizing multi- instance gpu for machine learning workloads,.

Performance Isolation for Inference Processes in Edge GPU Systems Characterizing multi- instance gpu for machine learning workloads,

Reference 21

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Observation e5b5319e-20cc-4c53-9f62-be104f0165fb · outbound

This paper cites Latency and throughput characterization of convolutional neural networks for mobile computer vision,.

Performance Isolation for Inference Processes in Edge GPU Systems Latency and throughput characterization of convolutional neural networks for mobile computer vision,

Reference 22

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source=pdf_text observed=2026-08-03T11:05:03.297446Z digest=sha256:23fd5ba408f41e6a691ebd4d1c0ab4ddc3e75f116699ae910f47df75ee3580ab

Observation abc5f20a-d1ae-478c-8e9c-2f63e357b4dc · outbound

This paper cites A comprehensive performance comparison of dedicated and embedded gpu systems,.

Performance Isolation for Inference Processes in Edge GPU Systems A comprehensive performance comparison of dedicated and embedded gpu systems,

Reference 23

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source=pdf_text observed=2026-08-03T11:05:03.351273Z digest=sha256:29785a52c76d81758b4042f0f421254c121b94a03f3ed4232a7915ae68c1eee6

Pith citing papers

Observation 7570e73d-1c94-4857-a910-bf3afc473ef1 · inbound

Architectural Isolation as a Timing Safety Primitive for Edge AI Medical Devices: Controlled Experimental Evidence on a Shared-Silicon Platform cites this paper.

Architectural Isolation as a Timing Safety Primitive for Edge AI Medical Devices: Controlled Experimental Evidence on a Shared-Silicon Platform Performance Isolation for Inference Processes in Edge GPU Systems

Reference 3

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arxiv_id, observed 2026-07-14T02:21:27.268121Z

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

source=pdf_text observed=2026-05-08T05:03:10.471589Z digest=sha256:18b2419c4438027a33d4cadc6dcecebb039ae4a0a33b815beff4d9fc5aec1dee