Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2410.12032.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:35:09.253025Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T05:55:24.700925Z
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 9ab746a1-98af-4c7c-964b-ff8a880927df · 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 MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:50:13.865187Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4cb71fae-f410-49ca-8bf7-1a7f87843592 · inbound
Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts cites this paper.
Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T15:20:32.519003Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 51ea54c0-3b57-44f7-9511-bfd34df8ddcd · inbound
EcoServe: Designing Carbon-Aware AI Inference Systems cites this paper.
EcoServe: Designing Carbon-Aware AI Inference Systems MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T20:31:35.827403Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 68ac4371-0e56-495c-a375-d259dc6a865f · inbound
Energy-Aware Deep Learning on Resource-Constrained Hardware cites this paper.
Energy-Aware Deep Learning on Resource-Constrained Hardware MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Reference 148
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:35:09.253025Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 702965d7-d447-42e8-a886-19971e214975 · inbound
SwizzlePerf: Hardware-Aware LLMs for GPU Kernel Performance Optimization cites this paper.
SwizzlePerf: Hardware-Aware LLMs for GPU Kernel Performance Optimization MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:52:48.865474Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f9422b36-3986-45de-bfc4-2149acf16be2 · inbound
Position Paper: From Edge AI to Adaptive Edge AI cites this paper.
Position Paper: From Edge AI to Adaptive Edge AI MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T23:58:28.795505Z
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 44886b5f-9788-400b-ac70-ba0fa1b9f714 · inbound
EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization cites this paper.
EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T02:48:33.238301Z
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 924c8b00-1d5d-4a71-bec9-aa6993bd87e2 · inbound
Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems cites this paper.
Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T05:55:24.703327Z
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 a513ad35-6fa5-449e-ae28-4253884eed9a · inbound
From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs cites this paper.
From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T13:11:40.152056Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.