Paper Citation Record · LEDGER

On the Sustainability of AI Inferences in the Edge

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2507.23093.

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

pith.paper-citation-record.v1
2507.23093 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:07:59.024285Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T12:28:38.809950Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T12:30:00.230060Z

Reference resolution

38 of 38 outbound references displayed

verified exact2
verified fuzzy32
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d3034478-becb-4d31-9273-b8d3eb99eb83 · outbound

This paper cites pcamp: Performance comparison of machine learning packages on the edges,.

On the Sustainability of AI Inferences in the Edge pcamp: Performance comparison of machine learning packages on the edges,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.695703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation baa58b39-56cd-4107-9a68-297ba13c4693 · outbound

This paper cites Deep learning for edge computing applications: A state-of-the-art survey,.

On the Sustainability of AI Inferences in the Edge Deep learning for edge computing applications: A state-of-the-art survey,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.679290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 46b83ca5-1e84-4a12-acf2-7c5308b801ec · outbound

This paper cites Green edge ai: A contemporary survey,.

On the Sustainability of AI Inferences in the Edge Green edge ai: A contemporary survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.664180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9d6d4f1-ec41-4e2f-8e71-505fa9b6dac9 · outbound

This paper cites Edgellm: A highly efficient cpu-fpga heterogeneous edge accelerator for large language models,.

On the Sustainability of AI Inferences in the Edge Edgellm: A highly efficient cpu-fpga heterogeneous edge accelerator for large language models,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.650199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0df9d38f-d2b6-478b-bc36-8fb1c42ecd42 · outbound

This paper cites Green ai: do deep learning frameworks have different costs?.

On the Sustainability of AI Inferences in the Edge Green ai: do deep learning frameworks have different costs?

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T11:07:58.896872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d66dcac6-2174-4bd8-9ca7-6fe9c128b8f2 · outbound

This paper cites Edge devices inference performance comparison,.

On the Sustainability of AI Inferences in the Edge Edge devices inference performance comparison,

Reference 6

Resolution: verified exact; doi, observed 2026-08-06T11:07:59.059669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c44f7cd3-fe95-4431-bf2d-d2a8caba8eb6 · outbound

This paper cites Accessed: 2025-07-15.

On the Sustainability of AI Inferences in the Edge Accessed: 2025-07-15

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.635716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f3c0f441-fb59-4dc9-a5fa-d63448586bc1 · outbound

This paper cites Tinyml: Enabling of inference deep learning models on ultra-low-power iot edge devices for ai applications,.

On the Sustainability of AI Inferences in the Edge Tinyml: Enabling of inference deep learning models on ultra-low-power iot edge devices for ai applications,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.622269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 76705211-25d2-4bf0-916f-fc8fe52be2d2 · outbound

This paper cites Optimizing large language models for edge devices: A comparative study on reputation analysis,.

On the Sustainability of AI Inferences in the Edge Optimizing large language models for edge devices: A comparative study on reputation analysis,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.609533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 684f8e8a-a2b8-4e88-88f0-1083161e1b39 · outbound

This paper cites Enabling deep learning on iot edge: Approaches and evaluation,.

On the Sustainability of AI Inferences in the Edge Enabling deep learning on iot edge: Approaches and evaluation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.596063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 675332d6-0296-4f7e-8b2f-3ed2a58768b9 · outbound

This paper cites Large Language Models: A Survey.

On the Sustainability of AI Inferences in the Edge Large Language Models: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T11:07:58.920464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ced9d39-f03b-43c9-9093-28843cf64007 · outbound

This paper cites A survey of federated learning for edge computing: Research problems and solutions,.

On the Sustainability of AI Inferences in the Edge A survey of federated learning for edge computing: Research problems and solutions,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.582305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e27f004d-d387-4cd2-a3f6-29dd5a523478 · outbound

This paper cites Litert overview | google ai edge | google ai for developers,.

On the Sustainability of AI Inferences in the Edge Litert overview | google ai edge | google ai for developers,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.568822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32e9fc3d-b232-4dc8-8676-b59e7c9337e2 · outbound

This paper cites Accessed: 2025-07-15.

On the Sustainability of AI Inferences in the Edge Accessed: 2025-07-15

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.554120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91b872f4-364a-4a43-bcbe-2d3942bfe12b · outbound

This paper cites Accessed: 2025-07-15.

On the Sustainability of AI Inferences in the Edge Accessed: 2025-07-15

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.540429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 52a6e4a9-4e18-4605-9565-b094d725a518 · outbound

This paper cites Accessed: 2025-07-15.

On the Sustainability of AI Inferences in the Edge Accessed: 2025-07-15

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.526977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cccd1dab-f630-4581-90bc-72eaddd9671e · outbound

This paper cites Accessed: 2025-07-15.

On the Sustainability of AI Inferences in the Edge Accessed: 2025-07-15

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.513775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a1ba51c1-c15e-471b-a59c-5ed46ed943de · outbound

This paper cites The carbon footprint of machine learning training will plateau, then shrink,.

On the Sustainability of AI Inferences in the Edge The carbon footprint of machine learning training will plateau, then shrink,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.500733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6a4b1c7-de5c-4f4c-b89e-6ec211499c2d · outbound

This paper cites Deep learning: Edge-cloud data analytics for iot,.

On the Sustainability of AI Inferences in the Edge Deep learning: Edge-cloud data analytics for iot,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.487304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e420c032-96f7-4fb2-bc7d-45d76354e350 · outbound

This paper cites DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge.

On the Sustainability of AI Inferences in the Edge DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:07:59.097378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 09e339dd-26dd-4613-8d3b-302929859ad7 · outbound

This paper cites Understanding the Performance and Power of LLM Inferencing on Edge Accelerators.

On the Sustainability of AI Inferences in the Edge Understanding the Performance and Power of LLM Inferencing on Edge Accelerators

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T11:07:58.959186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc110ec3-90fa-41fc-b2a9-a0f60f8c1736 · outbound

This paper cites Raspberry Pi,.

On the Sustainability of AI Inferences in the Edge Raspberry Pi,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.474763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 032fb7e4-2dbd-440e-b735-48462c1f7211 · outbound

This paper cites Enhanced visual intelligence at the network edge,.

On the Sustainability of AI Inferences in the Edge Enhanced visual intelligence at the network edge,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.462243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6645db12-9549-42a8-a454-cf276f6be692 · outbound

This paper cites Intel Neural Compute Stick 2,.

On the Sustainability of AI Inferences in the Edge Intel Neural Compute Stick 2,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.449218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6c453345-0a40-4801-9142-715d13b7e1b1 · outbound

This paper cites USB Accelerator,.

On the Sustainability of AI Inferences in the Edge USB Accelerator,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.436640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ae8ef584-5739-41d4-b12d-70e8801ff9f9 · outbound

This paper cites NVIDIA Jetson Nano,.

On the Sustainability of AI Inferences in the Edge NVIDIA Jetson Nano,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.423800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 18390b4c-0de5-4ad7-9848-4c41ac509a10 · outbound

This paper cites Accessed: 2025-07-15.

On the Sustainability of AI Inferences in the Edge Accessed: 2025-07-15

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.411271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 83839d9e-588c-4ecc-a924-bfdf1c1163d4 · outbound

This paper cites Accessed: 2025-07-15.

On the Sustainability of AI Inferences in the Edge Accessed: 2025-07-15

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.398949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63df5543-c78d-48a3-91e5-ac39d1057fa9 · outbound

This paper cites NVIDIA TensorRT,.

On the Sustainability of AI Inferences in the Edge NVIDIA TensorRT,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.384914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5700ab49-2eda-496a-a05c-e3e49bd2b620 · outbound

This paper cites Openvino ir format,.

On the Sustainability of AI Inferences in the Edge Openvino ir format,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.371625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3a04a4b-9b31-4e87-9489-746d85891e0a · outbound

This paper cites an unresolved cited work.

On the Sustainability of AI Inferences in the Edge Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:07:59.358517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 78820cc5-a439-4a62-bd6a-08fb4bc35392 · outbound

This paper cites imagenet,.

On the Sustainability of AI Inferences in the Edge imagenet,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.345060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8aea0f90-5018-4908-a27b-4c62f3dfab72 · outbound

This paper cites Glue: A multi-task benchmark and analysis platform for natural lan- guage understanding,.

On the Sustainability of AI Inferences in the Edge Glue: A multi-task benchmark and analysis platform for natural lan- guage understanding,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.331271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 430b4bd6-66af-4472-b376-04ab6e809f5a · outbound

This paper cites Openassistant conversations – democra- tizing large language model alignment,.

On the Sustainability of AI Inferences in the Edge Openassistant conversations – democra- tizing large language model alignment,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.315973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bb4b3135-03c5-4f7f-a884-c9351d2a1a4d · outbound

This paper cites Ac- cessed: 2025-07-17.

On the Sustainability of AI Inferences in the Edge Ac- cessed: 2025-07-17

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.302432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a37cd55c-6ff6-4720-9002-232d4d212b9a · outbound

This paper cites Save and load models | tensorflow core,.

On the Sustainability of AI Inferences in the Edge Save and load models | tensorflow core,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.289171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f6f73137-c65b-4b6d-b015-6146752248e4 · outbound

This paper cites TinyBERT: Distilling BERT for natural language understanding,.

On the Sustainability of AI Inferences in the Edge TinyBERT: Distilling BERT for natural language understanding,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.274930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4f202a6d-9599-4133-a20f-a33599d9b73f · outbound

This paper cites Llm-inference- bench: Inference benchmarking of large language models on ai acceler- ators,.

On the Sustainability of AI Inferences in the Edge Llm-inference- bench: Inference benchmarking of large language models on ai acceler- ators,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:07:59.261663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 9363acab-2707-422f-81ba-0f3d8ac15f72 · inbound

Characterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows cites this paper.

Characterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows On the Sustainability of AI Inferences in the Edge

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-15T12:30:00.233660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.