Paper Citation Record · LEDGER

Compute and Energy Consumption Trends in Deep Learning Inference

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2109.05472.

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

pith.paper-citation-record.v1
2109.05472 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:18:30.131644Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T21:15:49.390225Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 a3a60006-d9f1-4b4d-a26b-c8024ce1c7cf · inbound

Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness cites this paper.

Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness Compute and Energy Consumption Trends in Deep Learning Inference

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:15:49.393891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0a003c79-c45e-42c3-92ca-6974a5b1b473 · inbound

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy cites this paper.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy Compute and Energy Consumption Trends in Deep Learning Inference

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T05:32:45.160589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e91bbf6a-b862-4d2f-8f86-6948881b6a2b · inbound

Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for Binary Classifiers cites this paper.

Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for Binary Classifiers Compute and Energy Consumption Trends in Deep Learning Inference

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T00:50:13.750507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb14421e-76cf-4633-aca0-cbe6b3990aeb · inbound

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models cites this paper.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Compute and Energy Consumption Trends in Deep Learning Inference

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T18:41:32.417291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2e0044b-5498-4854-bbce-786d0a6b1935 · inbound

OscNet: Machine Learning on CMOS Oscillator Networks cites this paper.

OscNet: Machine Learning on CMOS Oscillator Networks Compute and Energy Consumption Trends in Deep Learning Inference

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T13:38:02.274615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c46647d-3107-4bfb-bcd2-65c50cf48237 · inbound

MCMComm: Hardware-Software Co-Optimization for End-to-End Communication in Multi-Chip-Modules cites this paper.

MCMComm: Hardware-Software Co-Optimization for End-to-End Communication in Multi-Chip-Modules Compute and Energy Consumption Trends in Deep Learning Inference

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T05:18:30.131644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d451bf7b-1548-498a-b8d2-0ff3e2e5dd27 · inbound

OscNet v1.5: Energy Efficient Hopfield Network on CMOS Oscillators for Image Classification cites this paper.

OscNet v1.5: Energy Efficient Hopfield Network on CMOS Oscillators for Image Classification Compute and Energy Consumption Trends in Deep Learning Inference

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T00:51:48.992724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 975ceabd-6ae1-42c3-a578-9ded4b81d03b · inbound

Fast Cross-Operator Optimization of Attention Dataflow cites this paper.

Fast Cross-Operator Optimization of Attention Dataflow Compute and Energy Consumption Trends in Deep Learning Inference

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T17:53:05.240294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.