Paper Citation Record · LEDGER

Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2403.08151.

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

pith.paper-citation-record.v1
2403.08151 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:02:16.463283Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T14:59:55.532477Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1c2ba5fc-4075-4f69-bd47-6fa91c6d2057 · inbound

A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning cites this paper.

A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:15.075194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0398e3a8-1649-4df8-8ee3-6a3674d82741 · inbound

Neuromorphic Computing with Microfluidic Memristors cites this paper.

Neuromorphic Computing with Microfluidic Memristors Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:35:13.388946Z

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 d000562a-907d-493d-a27d-38e7997de03d · inbound

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments cites this paper.

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T18:02:16.463283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0810ab0a-1703-404d-9086-94797917c247 · inbound

Racing to Idle: Energy Efficiency of Matrix Multiplication on Heterogeneous CPU and GPU Architectures cites this paper.

Racing to Idle: Energy Efficiency of Matrix Multiplication on Heterogeneous CPU and GPU Architectures Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T17:55:04.230859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53845b63-b8e3-442c-9ce6-0d1353f403c1 · inbound

Energy Consumption in Parallel Neural Network Training cites this paper.

Energy Consumption in Parallel Neural Network Training Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T21:59:28.682858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e743e44-80ff-400f-9422-e350b16f8d6f · inbound

Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid cites this paper.

Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:35:32.860118Z

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 d4f1dfbb-c82d-4085-817d-8c5a9d81699c · inbound

Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid cites this paper.

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:55:29.875100Z

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 5f013518-8d56-4f45-950c-1cc56d254832 · inbound

Energy-Aware Routing to Large Reasoning Models cites this paper.

Energy-Aware Routing to Large Reasoning Models Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T20:33:24.071020Z

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 63654293-9076-4bad-a7fe-2dcdc6b63d5e · inbound

Stochastic Thermodynamics of Associative Memory cites this paper.

Stochastic Thermodynamics of Associative Memory Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-16T17:38:11.283289Z

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 b8443be4-a89b-43d9-9b19-f2f13e22f14e · inbound

AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models cites this paper.

AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-13T23:54:54.872017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4ad2ab9-4b7e-4dea-b1f6-198816e7c267 · inbound

COMPASS: A Unified Decision-Intelligence System for Navigating Performance Trade-off in HPC cites this paper.

COMPASS: A Unified Decision-Intelligence System for Navigating Performance Trade-off in HPC Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 144

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:26:14.515264Z

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 40bdec9d-1144-4fa8-9a72-26863ac90cf8 · inbound

From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint cites this paper.

From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 92

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:31:12.717275Z

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 81db4731-6c64-4aa7-a1af-8751ece989be · inbound

Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning cites this paper.

Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:59:55.534131Z

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 82f23ace-06a8-4794-8aba-be3708398463 · inbound

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery cites this paper.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.696176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.