---
title: "DAC (A*): papers and universities"
description: "DAC (Design Automation Conf) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,135 of its papers from 2024–2026 (370 in 2024, 458 in 2025, 307 in 2026). By the countries of the authors' institutions, the most papers come from China (609), the United States (353) and Hong Kong (128)."
url: https://scholar4ai.com/proceedings/dac
markdown_url: https://scholar4ai.com/proceedings/dac.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "DAC (Design Automation Conf) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,135 of its papers from 2024–2026 (370 in 2024, 458 in 2025, 307 in 2026). (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# DAC: Design Automation Conf — papers and institutions, 2024–2026

Papers of DAC, Design Automation Conf (CORE rank A*; field: Computer science (general)), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02.

All conferences: https://scholar4ai.com/proceedings.md

## Key findings

- DAC (Design Automation Conf) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,135 of its papers from 2024–2026 (370 in 2024, 458 in 2025, 307 in 2026).
- By the countries of the authors' institutions, the most papers come from China (609), the United States (353) and Hong Kong (128).
- The institutions with the most papers are Peking University (78), Tsinghua University (72) and Chinese University of Hong Kong (71).
- Its most-cited paper of these years is “RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Model” (2024), with 105 citations.

## Papers per year

*DAC papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 370 | 369 |
| 2025 | 458 | 453 |
| 2026 | 307 | 307 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4210231368](https://openalex.org/S4210231368) | The conference's proceedings |

## Who publishes there

*Institutions with the most DAC papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 78 |
| 2 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 72 |
| 3 | Chinese University of Hong Kong | 71 |
| 4 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 71 |
| 5 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 68 |
| 6 | [Southeast University](https://scholar4ai.com/institutions/southeast-university.md) | 58 |
| 7 | [Fudan University](https://scholar4ai.com/institutions/fudan-university.md) | 54 |
| 8 | Institute of Computing Technology | 46 |
| 9 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 40 |
| 10 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 34 |

*Countries with the most DAC papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 609 |
| 2 | United States | 353 |
| 3 | Hong Kong | 128 |
| 4 | South Korea | 78 |
| 5 | Germany | 60 |
| 6 | United Kingdom | 52 |
| 7 | Taiwan | 49 |
| 8 | Singapore | 29 |
| 9 | Switzerland | 20 |
| 10 | United Arab Emirates | 19 |

*Keywords with the most DAC papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | energy efficiency | 152 |
| 2 | in-memory computing | 84 |
| 3 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 74 |
| 4 | hardware acceleration | 42 |
| 5 | hardware-software co-design | 39 |
| 6 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 37 |
| 7 | design space exploration | 35 |
| 8 | hardware security | 33 |
| 9 | hardware accelerators | 29 |
| 10 | hardware verification | 26 |

## Most-cited papers

*Most-cited DAC papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 105 | [RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Model](https://doi.org/10.1145/3649329.3657353) | 2024-06-23 | Yun-Da Tsai, Mingjie Liu, Haoxing Mark Ren |
| 2 | 60 | [EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Unified Compression and Adaptive Layer Voting](https://doi.org/10.1145/3649329.3658473) | 2024-06-23 | Zhongzhi Yu, Zheng Wang, Yuhan Li et al. |
| 3 | 60 | [Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework](https://doi.org/10.1145/3649329.3657356) | 2024-06-23 | Kaiyan Chang, Kun Wang, Nan Yang et al. |
| 4 | 34 | [Artisan: Automated Operational Amplifier Design via Domain-specific Large Language Model](https://doi.org/10.1145/3649329.3655903) | 2024-06-23 | Zihao Chen, Jiangli Huang, Yiting Liu et al. |
| 5 | 33 | [MAGE: A Multi-Agent Engine for Automated RTL Code Generation](https://doi.org/10.1109/dac63849.2025.11133191) | 2025-06-22 | Yujie Zhao, Hejia Zhang, Hui Huang et al. |
| 6 | 31 | [ChatCPU: An Agile CPU Design and Verification Platform with LLM](https://doi.org/10.1145/3649329.3658493) | 2024-06-23 | Xi Wang, Gwok-Waa Wan, Sam-Zaak Wong et al. |
| 7 | 29 | [S2RAM PUF: An Ultra-low Power Subthreshold SRAM PUF with Zero Bit Error Rate](https://doi.org/10.1145/3649329.3658246) | 2024-06-23 | Li Ni, Jiliang Zhang |
| 8 | 28 | [AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems](https://doi.org/10.1145/3649329.3655917) | 2024-06-23 | Chentao Jia, Ming Hu, Zekai Chen et al. |
| 9 | 27 | [Invited: New Solutions on LLM Acceleration, Optimization, and Application](https://doi.org/10.1145/3649329.3663517) | 2024-06-23 | Yingbing Huang, Jiaxin Wan, Hanchen Ye et al. |
| 10 | 27 | [APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models](https://doi.org/10.1145/3649329.3658498) | 2024-06-23 | Ziyi Guan, Hantao Huang, Yupeng Su et al. |

## Cite this page

Scholar4AI. (2026). DAC: Design Automation Conf — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/dac

```bibtex
@misc{scholar4ai-proceedings-dac,
  author = {{Scholar4AI}},
  title = {DAC: Design Automation Conf — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/dac}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).