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Paper Citation Record · LEDGER

Iceberg: Enhancing HLS Modeling with Synthetic Data

As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.09948.

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

pith.paper-citation-record.v1
2507.09948 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:51:02.782237Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

43 of 43 outbound references displayed

  • verified exact0
  • verified fuzzy27
  • unresolved15
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 01b4f1f9-1ea1-4a03-983c-c984cd063b4e · outbound

This paper cites High-level synthesis for FPGAs: From prototyping to deployment,.

Iceberg: Enhancing HLS Modeling with Synthetic Data High-level synthesis for FPGAs: From prototyping to deployment,

Reference 1

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no resolver link, observed 2026-08-06T17:51:02.583424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:02.583424Z digest=sha256:026c50c9d8c888757b01e0f169154eba1a64780b9dc940d4db8006d222d41ed2

Observation 4de14457-6d3b-4e64-bea1-a1e6dfddd8fa · outbound

This paper cites FPGA HLS today: successes, challenges, and opportunities,.

Iceberg: Enhancing HLS Modeling with Synthetic Data FPGA HLS today: successes, challenges, and opportunities,

Reference 2

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no resolver link, observed 2026-08-06T17:51:02.588348Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:02.588348Z digest=sha256:800ca0376845dae9b52158bb38def7a3f76f832a1027a646e30ba4b0e91b34dc

Observation 93c00a0d-56fa-4890-a8d4-0e80bcf81c61 · outbound

This paper cites VerilogEval: Evaluating Large Language Models for Verilog Code Generation.

Iceberg: Enhancing HLS Modeling with Synthetic Data VerilogEval: Evaluating Large Language Models for Verilog Code Generation

Reference 3

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unresolved
no resolver link, observed 2026-08-06T17:51:02.593071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:02.593071Z digest=sha256:56cbd66fd4604b2bcba27934078391e73b62575a4dee4c1ea07f43b256b3a8db

Observation 08c9364c-a9d9-4575-8723-c6300045f262 · outbound

This paper cites MG-Verilog: Multi- grained dataset towards enhanced LLM-assisted Verilog generation,.

Iceberg: Enhancing HLS Modeling with Synthetic Data MG-Verilog: Multi- grained dataset towards enhanced LLM-assisted Verilog generation,

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-06T17:51:03.713051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.599484Z digest=sha256:2b6833bb844af148aac0a7b5c0350785e3712bfd3cdeb57b7cf3d6c9524ebe3c

Observation 9e362083-ce54-4acd-aa56-229f907c4f96 · outbound

This paper cites OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection.

Iceberg: Enhancing HLS Modeling with Synthetic Data OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection

Reference 5

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unresolved
no resolver link, observed 2026-08-06T17:51:02.605641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:02.605641Z digest=sha256:9b55b831071792cd03b79b06d10e3d1fcfc19945a30eae0b9f2ca16e90b9c548

Observation 4a2d0be6-837b-4f6e-a3d0-17e4a9a0419a · outbound

This paper cites ScaleHLS: A new scalable high-level synthesis framework on multi-level intermediate representation,.

Iceberg: Enhancing HLS Modeling with Synthetic Data ScaleHLS: A new scalable high-level synthesis framework on multi-level intermediate representation,

Reference 6

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raw_fallback, observed 2026-08-06T17:51:03.687063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.610835Z digest=sha256:db40b44915414fdfd400fa16b4265e0eeaa0f53e91f35a9df97b1905dd38b3f1

Observation cfae1521-8a25-4bde-b06a-de68abb40509 · outbound

This paper cites Stream-HLS: Towards automatic dataflow acceleration,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Stream-HLS: Towards automatic dataflow acceleration,

Reference 7

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raw_fallback, observed 2026-08-06T17:51:03.661155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.616098Z digest=sha256:a3f04fb5cd1d2d93920c304a17b77a49d90e1e139867ac9ccb2c5dfd4437d167

Observation f193140e-7e1f-4a86-80ac-a9817e21ff72 · outbound

This paper cites LightningSimV2: Faster and scalable simulation for high-level synthesis via graph compilation and optimization,.

Iceberg: Enhancing HLS Modeling with Synthetic Data LightningSimV2: Faster and scalable simulation for high-level synthesis via graph compilation and optimization,

Reference 8

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raw_fallback, observed 2026-08-06T17:51:03.635976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.620863Z digest=sha256:c7843bd33396d67c299813b697d903d962d6014033e1374eef3a8701b73f31df

Observation cbd1fa25-1930-48e6-a066-5b8864668824 · outbound

This paper cites A unified framework for automated code transformation and pragma insertion,.

Iceberg: Enhancing HLS Modeling with Synthetic Data A unified framework for automated code transformation and pragma insertion,

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-06T17:51:03.615606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.625366Z digest=sha256:216aaf5babff109fde1c1fb4ee8f1bf014ec285a4697ea61b309602791db4add

Observation c6f21b88-b543-4a52-b025-70fc7c4efd36 · outbound

This paper cites Allo: A programming model for composable accelerator design,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Allo: A programming model for composable accelerator design,

Reference 10

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raw_fallback, observed 2026-08-06T17:51:03.590569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.629696Z digest=sha256:cc4161714c778ff719303b68668922eda420ca367025e0e416f948e49a76cf11

Observation 7e0bb155-0ac8-432d-be67-f691948c02ed · outbound

This paper cites An iteratively-refined dataset for high-level synthesis functional verification through LLM- aided bug injection,.

Iceberg: Enhancing HLS Modeling with Synthetic Data An iteratively-refined dataset for high-level synthesis functional verification through LLM- aided bug injection,

Reference 11

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raw_fallback, observed 2026-08-06T17:51:03.570205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.634490Z digest=sha256:53146c23d57c82f1653c806f7fba776bb59a2f67f5e91fa9bb3deceacf0feb22

Observation 494c5728-386f-4ea6-8c5c-3c03f03fa70f · outbound

This paper cites HLSPilot: LLM-based High-Level Synthesis.

Iceberg: Enhancing HLS Modeling with Synthetic Data HLSPilot: LLM-based High-Level Synthesis

Reference 12

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no resolver link, observed 2026-08-06T17:51:02.639173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:02.639173Z digest=sha256:574009ec5711248feaee8b917615806b9b8d1e7de8e33f56e42d612f0ef7c61c

Observation 66e8518f-81b8-4fea-9e0e-c07a8a7d4c90 · outbound

This paper cites C2HLSC: Can LLMs Bridge the Software-to-Hardware Design Gap?.

Iceberg: Enhancing HLS Modeling with Synthetic Data C2HLSC: Can LLMs Bridge the Software-to-Hardware Design Gap?

Reference 13

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no resolver link, observed 2026-08-06T17:51:02.643621Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:02.643621Z digest=sha256:7c474abb0ca96c4ee684cf659f479f020c12e763ae5f2a2f5fc60ddb52e0f605

Observation 503d18de-de4e-4007-9e26-c93c60d48591 · outbound

This paper cites Optimizing high-level synthesis designs with retrieval-augmented large language models,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Optimizing high-level synthesis designs with retrieval-augmented large language models,

Reference 14

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raw_fallback, observed 2026-08-06T17:51:03.546954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.648597Z digest=sha256:7abf09b7e6c603661e39af2894a6866e854117074ef5881adf9c08faa6c52f2c

Observation e0d1b7c3-1d5c-4b4d-9822-e50178bdc961 · outbound

This paper cites Automated C/C++ Program Repair for High-Level synthesis via Large Language Models,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Automated C/C++ Program Repair for High-Level synthesis via Large Language Models,

Reference 15

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raw_fallback, observed 2026-08-06T17:51:03.524144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.653341Z digest=sha256:4c5af87caa4eee15de8dd2f1535b57a58e752fe1bcfbfe1616c5d60168f43ac9

Observation 7d64dabc-932b-44fe-a655-182b851258bf · outbound

This paper cites LLM-aided compilation for tensor accelerators,.

Iceberg: Enhancing HLS Modeling with Synthetic Data LLM-aided compilation for tensor accelerators,

Reference 16

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raw_fallback, observed 2026-08-06T17:51:03.507721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.658857Z digest=sha256:09fc2b39ecf7b383cb313ef46abd9afef2b9098b5fa59c339e95681a1e188e3b

Observation 8d782d12-3626-47d0-bd04-a04fb3ec5672 · outbound

This paper cites GPT4AIGchip: Towards next-generation AI accelerator design automation via large language models,.

Iceberg: Enhancing HLS Modeling with Synthetic Data GPT4AIGchip: Towards next-generation AI accelerator design automation via large language models,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.664173Z digest=sha256:2904b0731f4d3fa06f444ab48fe2ccb2ffbaa8fbba9c57a91d694f1a38168ad8

Observation 2714c54d-a0f9-408f-bcce-469a30f81ff0 · outbound

This paper cites IRONMAN-PRO: Multiobjective design space exploration in HLS via reinforcement learning and graph neural network-based modeling,.

Iceberg: Enhancing HLS Modeling with Synthetic Data IRONMAN-PRO: Multiobjective design space exploration in HLS via reinforcement learning and graph neural network-based modeling,

Reference 18

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raw_fallback, observed 2026-08-06T17:51:03.470195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.668685Z digest=sha256:7f73afd87e65d7b0d2720963a41e1552eabae39afa6cac150dd098c44db05398

Observation 7a6e6d9d-de1b-4f49-a3b0-5fe3ecc1506d · outbound

This paper cites Robust GNN-based representation learning for HLS,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Robust GNN-based representation learning for HLS,

Reference 19

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raw_fallback, observed 2026-08-06T17:51:03.452302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.673322Z digest=sha256:9d26951d172e68991c42a5807205da65ee9e1b11f65ad51a8c03334769da319c

Observation c3b2ea0d-308c-43d5-b9e4-6594b8adac4a · outbound

This paper cites Balor: HLS source code evaluator based on custom graphs and hierarchical GNNs,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Balor: HLS source code evaluator based on custom graphs and hierarchical GNNs,

Reference 20

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raw_fallback, observed 2026-08-06T17:51:03.432580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.677762Z digest=sha256:38ff8b00b8db6c95d9b905d8898920c0a590f49ca838eb7a7d6251a537404f52

Observation 8116b7bc-68c8-40ed-ba98-dd217c0c4185 · outbound

This paper cites Hierarchical source-to-post-route QoR prediction in high-level synthesis with GNNs,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Hierarchical source-to-post-route QoR prediction in high-level synthesis with GNNs,

Reference 21

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raw_fallback, observed 2026-08-06T17:51:03.413655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.681858Z digest=sha256:6ea105fb30c23cce7fc2373bf19f3946fbf222300364683ee8c059827b37fc1a

Observation 811be845-6397-4368-bc7a-26585222d02b · outbound

This paper cites Learning to compare hardware designs for high- level synthesis,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Learning to compare hardware designs for high- level synthesis,

Reference 22

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raw_fallback, observed 2026-08-06T17:51:03.388590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.685959Z digest=sha256:8dbfa6b0bc482456270e1daae354458423a40ce628d3a805e8fa9fb31f0d1185

Observation 4ed3b6c0-6ce0-4b43-b09a-2e36807cb892 · outbound

This paper cites PowerGear: Early-stage power estimation in FPGA HLS via heterogeneous edge- centric GNNs,.

Iceberg: Enhancing HLS Modeling with Synthetic Data PowerGear: Early-stage power estimation in FPGA HLS via heterogeneous edge- centric GNNs,

Reference 23

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raw_fallback, observed 2026-08-06T17:51:03.368706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.689944Z digest=sha256:0d5917e23d034d241b48000ef6156bee9cfc7816e10b38605877d6ac81d973d2

Observation 63bcccce-dad5-4d11-b9f9-0f6e840a469f · outbound

This paper cites Cross-modality program representation learning for electronic design automation with high-level synthesis,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Cross-modality program representation learning for electronic design automation with high-level synthesis,

Reference 24

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raw_fallback, observed 2026-08-06T17:51:03.348019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.694075Z digest=sha256:f327c64ce6d4ccb5d8dbfaf9f62f8e6c8324437ee14b99751b04a09e0d0e86ad

Observation 946fe6d2-9095-47b3-b313-ba66e7a71772 · outbound

This paper cites Hierarchical mixture of experts: Generalizable learning for high-level synthesis,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Hierarchical mixture of experts: Generalizable learning for high-level synthesis,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:51:03.325961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.698025Z digest=sha256:fd529c8e8aa306b80c1a88d27a4bd30fad1c05cbe579f52a4e632f7938290306

Observation 9add720f-0cce-4e5d-be6f-de8c33292431 · outbound

This paper cites Efficient task transfer for HLS DSE,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Efficient task transfer for HLS DSE,

Reference 26

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raw_fallback, observed 2026-08-06T17:51:03.308752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.702596Z digest=sha256:1d28417ae68ca0cc995a18e97dd1c581a85fbab7d9d1519d4c2963f5f459244c

Observation 908cabff-2e34-418a-b348-d3a02048949f · outbound

This paper cites Towards a comprehensive benchmark for high-level synthesis targeted to FPGAs,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Towards a comprehensive benchmark for high-level synthesis targeted to FPGAs,

Reference 27

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unresolved
no resolver link, observed 2026-08-06T17:51:02.707197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:02.707197Z digest=sha256:6a0859f47991502dbc8b89f5614a4568c0d87f80dfe79f85f12d1db11437e6eb

Observation acae3d42-44c0-487d-8dd3-fdef55bc9608 · outbound

This paper cites HLSFactory: A framework empowering high-level synthesis datasets for machine learning and beyond,.

Iceberg: Enhancing HLS Modeling with Synthetic Data HLSFactory: A framework empowering high-level synthesis datasets for machine learning and beyond,

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-06T17:51:03.270386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.712066Z digest=sha256:52b10add7575f05b57239b5a986ee5b9679cc4ab5d9653ec2cfe669427c91460

Observation bf12543f-67cb-4f1b-b62d-3dff71170dba · outbound

This paper cites Source-to-source optimization for HLS,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Source-to-source optimization for HLS,

Reference 29

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raw_fallback, observed 2026-08-06T17:51:03.250897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.717689Z digest=sha256:ef6036ca0dba969756a51eb6f729986c24ce1138b5506ff6025f412a205de44f

Observation ff48bfb6-c430-4e97-a4aa-05f5c23aced1 · outbound

This paper cites HIDA: A hierarchical dataflow compiler for high-level synthesis,.

Iceberg: Enhancing HLS Modeling with Synthetic Data HIDA: A hierarchical dataflow compiler for high-level synthesis,

Reference 30

Resolution
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raw_fallback, observed 2026-08-06T17:51:03.230441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.722895Z digest=sha256:03c0c60ea93aaa2c9c4fee96dba3c4134b511bcf6e420ce24f022cc086b141a1

Observation 05d5e332-e717-4c9d-ac12-c152586f86ef · outbound

This paper cites AutoDSE: Enabling software programmers to design efficient FPGA accelerators,.

Iceberg: Enhancing HLS Modeling with Synthetic Data AutoDSE: Enabling software programmers to design efficient FPGA accelerators,

Reference 31

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unresolved
no resolver link, observed 2026-08-06T17:51:02.727921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:02.727921Z digest=sha256:922f844e68f575e0b52761036201981359325c396d77ad9a4013b7363cfb5ad1

Observation e34ec934-57f2-460e-97bf-e66ed4348e4f · outbound

This paper cites Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling.

Iceberg: Enhancing HLS Modeling with Synthetic Data Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling

Reference 32

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unresolved
no resolver link, observed 2026-08-06T17:51:02.732477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:02.732477Z digest=sha256:2ab71f8aec86d4979ebedbefd1c3027663cae806696ebe7689410c031933d674

Observation 454f5aea-ffd4-4a34-b62b-7f1f493e4adb · outbound

This paper cites Expt: Synthetic pretraining for few-shot experimental design,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Expt: Synthetic pretraining for few-shot experimental design,

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-06T17:51:03.213950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T17:51:02.737149Z digest=sha256:cf7e23584d4cd544972814e980d0bbb47ec89c810840cc97c9a510129f2e416e

Observation 5a04d45c-b864-4f55-bc9c-814ed5d48f5d · outbound

This paper cites TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

Iceberg: Enhancing HLS Modeling with Synthetic Data TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 34

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unresolved
no resolver link, observed 2026-08-06T17:51:02.741272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b4091c4c-46f2-495c-adb4-49179e6b92f4 · outbound

This paper cites Rosetta: A realistic high- level synthesis benchmark suite for software programmable FPGAs,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Rosetta: A realistic high- level synthesis benchmark suite for software programmable FPGAs,

Reference 35

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verified fuzzy
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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 49ac912c-c103-47a2-9878-66ed69beaceb · outbound

This paper cites Attention is all you need,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Attention is all you need,

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7c608c9d-92d0-4ec0-87b0-34a4c84728fc · outbound

This paper cites Vitis HLS 2023.2,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Vitis HLS 2023.2,

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation a09cbc5f-1927-43b1-a88f-a78d9939722b · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Iceberg: Enhancing HLS Modeling with Synthetic Data Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 38

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Source-reported events for the cited work

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Observation c732ae1a-1c20-4823-86ca-6d2461936ca7 · outbound

This paper cites LICO: Large language models for in-context molecular optimization,.

Iceberg: Enhancing HLS Modeling with Synthetic Data LICO: Large language models for in-context molecular optimization,

Reference 39

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Source-reported events for the cited work

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Observation df58c989-ecd7-4452-a618-3ea924e83a85 · outbound

This paper cites Neural Processes.

Iceberg: Enhancing HLS Modeling with Synthetic Data Neural Processes

Reference 40

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1d9bf036-57e4-48e1-a15c-ac7419faa52f · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 704c1a9f-dfc7-47d6-88bb-9377e25a6e86 · outbound

This paper cites Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,.

Iceberg: Enhancing HLS Modeling with Synthetic Data Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,

Reference 42

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 211fddfd-1511-402a-a78f-69410c3c4724 · outbound

This paper cites Mixture of In-Context Prompters for Tabular PFNs.

Iceberg: Enhancing HLS Modeling with Synthetic Data Mixture of In-Context Prompters for Tabular PFNs

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.