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Paper Citation Record · LEDGER

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2606.11117.

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

pith.paper-citation-record.v1
2606.11117 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T11:19:17.690153Z

measured 22 of 22 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 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

22 of 22 outbound references displayed

  • verified exact6
  • verified fuzzy0
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 364b966f-e2e8-4411-ac9f-3e17f7b0cd8a · outbound

This paper cites Machine Learning for FPGA Electronic Design Automation,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA Machine Learning for FPGA Electronic Design Automation,

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:447c48a6f42227b4bbbc4a8e58ceb991c0f2a00be7bc2142234c1b158f6fcc5b

Observation ab70365a-c5f7-492f-aef9-65e144a6ca46 · outbound

This paper cites Large Language Models for Software Engineering: Survey and Open Problems,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA Large Language Models for Software Engineering: Survey and Open Problems,

Reference 2

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Observation 3cf7c97c-d53c-4da3-9f3d-695898051597 · outbound

This paper cites LLM-AID: Leveraging Large Language Models for Rapid Domain-Specific Accelerator Development,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA LLM-AID: Leveraging Large Language Models for Rapid Domain-Specific Accelerator Development,

Reference 3

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source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:5aba6ae124d34a371d51a32d406ced9554783146eb06e6e90789b38860c0e626

Observation d96f70c0-73ac-4acc-855e-6ae6923a90de · outbound

This paper cites GPT4AIGChip: Towards Next-Generation AI Accelerator Design Au- tomation via Large Language Models,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA GPT4AIGChip: Towards Next-Generation AI Accelerator Design Au- tomation via Large Language Models,

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:31183b08c96e3779ff72a792e691fe62e0c6830c31c20ccfa19881461a26c218

Observation de822c71-7200-4466-a2e8-3bcf5bb2f700 · outbound

This paper cites DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration,

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:42c8d796666d876742d970a805d4f9347e9e914bc21d6a0e1434ed591f1dc620

Observation 4dbad033-5764-4bcf-a284-035e75e56d0d · outbound

This paper cites SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Acceler- ators for Edge Inference,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Acceler- ators for Edge Inference,

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:6e4184ec3d79f8d017a550d6e51b462afc515c81ac22070cb6a46ab8bf4be5ad

Observation ecd90366-7c16-47fe-b611-55e4430ca7b2 · outbound

This paper cites SECDA- TFLite: A toolkit for efficient development of FPGA-based DNN accelerators for edge inference,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA SECDA- TFLite: A toolkit for efficient development of FPGA-based DNN accelerators for edge inference,

Reference 7

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source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:a28fb93e597a3a44698936a2f1e01462c9292225ced16ddbecd329bb1735db73

Observation 5fe6a0a6-868d-454e-ba2c-66055475dd6f · outbound

This paper cites Designing Efficient LLM Accelerators for Edge Devices.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA Designing Efficient LLM Accelerators for Edge Devices

Reference 8

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verified exact
arxiv_id, observed 2026-07-03T07:57:45.332969Z

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.

source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:208f7158105540d70e1bdf688c37e8926e2f48231bba4d74f9472e52ca129c82

Observation b01b0152-0881-4c2f-b7a4-49ab3ea09257 · outbound

This paper cites Large Language Models for Software Engineering: A Systematic Literature Review,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA Large Language Models for Software Engineering: A Systematic Literature Review,

Reference 9

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source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:a792c90d5fbaaa20e81baf86987a71c221dd1f9a7d96a83780b6777e8e89f566

Observation 891952e2-4dea-4b48-a986-5e45d0cf39a4 · outbound

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

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA LoRA: Low-Rank Adaptation of Large Language Models,

Reference 10

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Observation a523ba3a-c8d5-421c-a535-c6b047ca22cd · outbound

This paper cites FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review

Reference 11

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arxiv_id, observed 2026-07-03T07:57:45.324084Z

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.

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Observation 761ec280-4236-4c48-9964-dd1721814e70 · outbound

This paper cites iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs

Reference 12

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arxiv_id, observed 2026-07-03T07:57:45.333548Z

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.

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Observation d89c7cf0-6c67-4210-8dc7-0eda66b15374 · outbound

This paper cites Are LLMs Any Good for High- Level Synthesis?.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA Are LLMs Any Good for High- Level Synthesis?

Reference 13

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Observation d91d75ae-d629-434f-aa5a-8d347cbb20be · outbound

This paper cites A Survey on Neural Network Hardware Accelerators,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA A Survey on Neural Network Hardware Accelerators,

Reference 14

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Observation 0361cecd-f618-4e26-9e38-64b97c686515 · outbound

This paper cites Ollama: Run large language models locally,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA Ollama: Run large language models locally,

Reference 15

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Observation 8b4f67bb-d4da-496c-987b-bea79692949d · outbound

This paper cites A Survey on Design Space Exploration Approaches for Approximate Computing Systems,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA A Survey on Design Space Exploration Approaches for Approximate Computing Systems,

Reference 16

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source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:5b8e422cee1d53daf064ae686550fe894cbac152913f7d3d82048998b14383f6

Observation 02bb793d-c21e-401d-ba7a-6fdabe9701e6 · outbound

This paper cites Distributionally Robust Receive Combining.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA Distributionally Robust Receive Combining

Reference 17

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verified exact
arxiv_id, observed 2026-07-03T07:57:45.327490Z

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.

source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:522cbf5bb8c93e23a08b2d44c90f9a07e48d0641d39eccf942f8073cfabd98cc

Observation 2b376fd2-ed8b-4010-a8d2-2e623a83e693 · outbound

This paper cites SA-DS: A Dataset for Large Language Model-Driven AI Accelerator Design Generation,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA SA-DS: A Dataset for Large Language Model-Driven AI Accelerator Design Generation,

Reference 18

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Observation d6d7e34a-7853-47bf-b262-044d0b52f906 · outbound

This paper cites Vivado high-level synthesis,.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA Vivado high-level synthesis,

Reference 19

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Observation 43597819-c5d6-4e65-9cf2-ebfd2476aa4b · outbound

This paper cites Hardware acceleration for neural networks: A comprehensive survey.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA Hardware acceleration for neural networks: A comprehensive survey

Reference 20

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arxiv_id, observed 2026-07-03T07:57:45.327129Z

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.

source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:7813a3fee8c21b2510fd139a860c8f143c571410a96ea377f02c4fd044767c14

Observation a06d3d68-b826-446b-afea-a68a91cdcb76 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA TinyLlama: An Open-Source Small Language Model

Reference 21

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local_arxiv, observed 2026-07-03T07:57:45.336439Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:964ec19e343ccf211bf704e560e3933506df1f7da09ca1bee679bc89bb789df6

Observation e079b14d-3c3b-4cc9-b177-085d6e2640ef · outbound

This paper cites arXiv:2603.05904 [cs.AR]https://arxiv.org/abs/2603.

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA arXiv:2603.05904 [cs.AR]https://arxiv.org/abs/2603

Reference 22

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arxiv_id, observed 2026-07-03T07:57:45.335656Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-27T11:19:17.690153Z digest=sha256:c528f0ffb7390f02b0aa8026f031ce029db00b52a6d00c457f817eff69321386

Pith citing papers

No inbound Pith citation observations are available.