Paper Citation Record · LEDGER

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design

As of 7 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2508.13162.

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

pith.paper-citation-record.v1
2508.13162 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:49:56.669137Z

measured 44 of 44 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

verified exact1
verified fuzzy16
unresolved27
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 810e6ad5-a79f-4964-893f-efcad373e711 · outbound

This paper cites Artificial intelligence (ai) hardware market to exceed usd 84.9 billion by 2031: Skyquest technology,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Artificial intelligence (ai) hardware market to exceed usd 84.9 billion by 2031: Skyquest technology,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.249472Z

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

Observation 2baa0c65-5e00-4b9b-8be6-10cff7f3c759 · outbound

This paper cites A survey on collaborative dnn inference for edge intelligence,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design A survey on collaborative dnn inference for edge intelligence,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.240148Z

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Observation 44a605b3-dba0-4b94-b0d1-e539f4e7b84b · outbound

This paper cites Gemmini: Enabling systematic deep-learning architec- ture evaluation via full-stack integration,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Gemmini: Enabling systematic deep-learning architec- ture evaluation via full-stack integration,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.230594Z

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Observation 415193a2-3c56-4852-b14d-323c3c8497a4 · outbound

This paper cites Verigen: A large language model for verilog code generation,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Verigen: A large language model for verilog code generation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.521235Z

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Observation 8211d1d9-4e8c-4c3e-9500-7ab9354835d8 · outbound

This paper cites Chateda: A large language model powered autonomous agent for eda,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Chateda: A large language model powered autonomous agent for eda,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.214875Z

Source-reported events for the cited work

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

Observation b25cf2fb-743e-4af0-9afd-a81a1489158a · outbound

This paper cites Chipgpt: How far are we from natural language hardware design,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Chipgpt: How far are we from natural language hardware design,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.531144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed0691f0-4c50-4e50-84f9-d6651403214d · outbound

This paper cites AutoChip: Automating HDL Generation Using LLM Feedback.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design AutoChip: Automating HDL Generation Using LLM Feedback

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.535191Z

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Unavailable: canonical work link unavailable.

Observation 7006730b-fcad-4f88-bcbf-de946724a6a6 · outbound

This paper cites Gpt4aigchip: Towards next-generation ai accelerator design automation via large language models,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Gpt4aigchip: Towards next-generation ai accelerator design automation via large language models,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.539382Z

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Unavailable: canonical work link unavailable.

Observation 00e805d7-8f4d-4890-9a07-01d21231d523 · outbound

This paper cites Sa-ds: A dataset for large language model- driven ai accelerator design generation,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Sa-ds: A dataset for large language model- driven ai accelerator design generation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.199377Z

Source-reported events for the cited work

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

Observation 75d922fa-794b-48a3-9ab9-1b0293f89a12 · outbound

This paper cites Gpt-4o: Openai’s advanced generative language model,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Gpt-4o: Openai’s advanced generative language model,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.190430Z

Source-reported events for the cited work

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

Observation d1654159-1daa-4928-a75c-490f8d2dc437 · outbound

This paper cites Claude 3.5 sonnet: Anthropic’s advanced language model,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Claude 3.5 sonnet: Anthropic’s advanced language model,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.181870Z

Source-reported events for the cited work

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

Observation c23053e0-607f-479d-ad45-c494a75a74b9 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design On the Opportunities and Risks of Foundation Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.555409Z

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Unavailable: canonical work link unavailable.

Observation 189081b5-576b-49bb-8818-901e7df32c96 · outbound

This paper cites LLM-Aided Efficient Hardware Design Automation.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design LLM-Aided Efficient Hardware Design Automation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.560376Z

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Unavailable: canonical work link unavailable.

Observation 32782312-4ab5-4a82-96d7-0490272870a1 · outbound

This paper cites The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.564912Z

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Unavailable: canonical work link unavailable.

Observation 3e9e0d33-4983-4fc3-87d1-e626cb2c7e15 · outbound

This paper cites Cybercriminals who breached nvidia issue one of the most unusual demands ever,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Cybercriminals who breached nvidia issue one of the most unusual demands ever,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.172546Z

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

Observation 078d35ab-dded-4138-88b7-88cfd236ea79 · outbound

This paper cites Fast and accurate ppa modeling with transfer learning,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Fast and accurate ppa modeling with transfer learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.163854Z

Source-reported events for the cited work

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

Observation 3aa9a20e-4b58-490d-ac8a-b6b69b037724 · outbound

This paper cites Federated machine learning: Concept and applications,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Federated machine learning: Concept and applications,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.576394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e492b9bb-8e4f-4bcf-8bd5-5998d58864b6 · outbound

This paper cites Towards Federated Learning at Scale: System Design.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Towards Federated Learning at Scale: System Design

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.580378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceb3df3d-f574-40d5-9422-7632fd43f1dc · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Communication-efficient learning of deep networks from decentralized data,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.584566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37add713-bd64-480a-ac98-ad05fa1a5df1 · outbound

This paper cites Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.588435Z

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Unavailable: canonical work link unavailable.

Observation ae620291-51c9-4fc7-a9c0-512b9701e47c · outbound

This paper cites Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Federatedscope-llm: A comprehensive package for fine-tuning large language models in federated learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.142509Z

Source-reported events for the cited work

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

Observation f1633f46-e75f-4c18-b791-431c3d32dcaa · outbound

This paper cites Federated LoRA with Sparse Communication.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Federated LoRA with Sparse Communication

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.596673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87cb526e-9d16-4257-97e7-224704e1575f · outbound

This paper cites Learned Hardware/Software Co-Design of Neural Accelerators.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Learned Hardware/Software Co-Design of Neural Accelerators

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:49:56.781160Z

Source-reported events for the cited work

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

Observation 4416e9c6-7f19-4c47-a553-1d75d4f02152 · outbound

This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.605376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a87335e-481b-459c-841b-db94903296eb · outbound

This paper cites Language Models are Few-Shot Learners.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Language Models are Few-Shot Learners

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.608873Z

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Unavailable: canonical work link unavailable.

Observation abdee3c4-3b95-4761-9d38-653af9527ad6 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.612300Z

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Observation 1713e9da-0cde-4282-af5b-5013a4ae86d4 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.615565Z

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Observation fa060d0d-77f3-41ca-88e1-9aa76edda469 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Qlora: Efficient finetuning of quantized llms,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.622444Z

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Observation d1480d08-4cbc-4591-a4d2-e24406f002e9 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design OPT: Open Pre-trained Transformer Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.625257Z

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Unavailable: canonical work link unavailable.

Observation 3c8d3ed1-e494-4a52-b3ec-436fa3aa9f6b · outbound

This paper cites Motivation for and evaluation of the first tensor processing unit,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Motivation for and evaluation of the first tensor processing unit,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.127502Z

Source-reported events for the cited work

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Observation 6bf3cb58-74ba-415a-be74-f6d91caabeff · outbound

This paper cites Aptpu: Approximate computing based tensor processing unit,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Aptpu: Approximate computing based tensor processing unit,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.117945Z

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

Observation a18ef02b-31a3-4617-8e40-abc93ab1cdd7 · outbound

This paper cites [Online].

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design [Online]

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:57.108422Z

Source-reported events for the cited work

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Observation 42a8be15-3e7f-4f18-8879-5be522c85e08 · outbound

This paper cites Least squares quantization in pcm,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Least squares quantization in pcm,

Reference 34

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Observation 18463582-e969-4e39-9ec8-9724786f2452 · outbound

This paper cites Estimating a dirichlet distribution,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Estimating a dirichlet distribution,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.640174Z

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Observation 4c363f89-b13c-491d-a897-5b4181faecb2 · outbound

This paper cites On information and sufficiency,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design On information and sufficiency,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.642977Z

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Unavailable: canonical work link unavailable.

Observation 83b74d67-fa0c-43e2-aeb6-a5677e4f5c90 · outbound

This paper cites The three sigma rule,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design The three sigma rule,

Reference 37

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Observation d0eb8d7b-9668-41ee-84cb-05f86450ea99 · outbound

This paper cites OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.649208Z

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Observation 3d3b5445-5b0f-4c15-996f-df9fc8e891be · outbound

This paper cites Lora: Low-rank adaptation of large language models,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Lora: Low-rank adaptation of large language models,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.652069Z

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Unavailable: canonical work link unavailable.

Observation 0c051ba0-70db-423a-8321-3dc27b748a65 · outbound

This paper cites Decoupled weight decay regularization,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Decoupled weight decay regularization,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.657962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 824d7445-16d9-498c-8f70-92a98065b2ea · outbound

This paper cites Stanford alpaca: An instruction-following llama model,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Stanford alpaca: An instruction-following llama model,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:56.946414Z

Source-reported events for the cited work

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Observation 48ef78b4-b8e7-4002-8d45-0254ff5923f1 · outbound

This paper cites Gpt-o1: Specialized generative pre-trained transformer for do- main applications,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Gpt-o1: Specialized generative pre-trained transformer for do- main applications,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:56.936973Z

Source-reported events for the cited work

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Observation 2cdedbc0-cdc5-4489-83d6-0c1849dc623e · outbound

This paper cites Gemini advanced: High-performance large language model,.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Gemini advanced: High-performance large language model,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:49:56.925105Z

Source-reported events for the cited work

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

Observation 742e0e60-3d42-42a2-b10e-e1ddfbcb2e43 · outbound

This paper cites Decoupled Weight Decay Regularization.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Decoupled Weight Decay Regularization

Reference 2019

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Observation 5f15fde8-0d39-4f9b-b2f0-50daca48a203 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design LoRA: Low-Rank Adaptation of Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.655047Z

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Pith citing papers

No inbound Pith citation observations are available.