---
title: "ICCAD (A): papers and universities"
description: "ICCAD (IEEE/ACM International Conference on Computer-Aided Design) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 239 of its papers from 2024–2026 (239 in 2024; 2025 and 2026 not in OpenAlex yet). By the countries of the authors' institutions, the most papers come from the United States (110), China (95) and Hong Kong (28)."
url: https://scholar4ai.com/proceedings/iccad
markdown_url: https://scholar4ai.com/proceedings/iccad.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ICCAD (IEEE/ACM International Conference on Computer-Aided Design) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 239 of its papers from 2024–2026 (239 in 2024; 2025 and 2026 not in OpenAlex yet). (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)
---

# ICCAD: IEEE/ACM International Conference on Computer-Aided Design — papers and institutions, 2024–2026

Papers of ICCAD, IEEE/ACM International Conference on Computer-Aided Design (CORE rank A; field: Computer science (general), Distributed computing and systems software), 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

- ICCAD (IEEE/ACM International Conference on Computer-Aided Design) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 239 of its papers from 2024–2026 (239 in 2024; 2025 and 2026 not in OpenAlex yet).
- By the countries of the authors' institutions, the most papers come from the United States (110), China (95) and Hong Kong (28).
- The institutions with the most papers are Peking University (20), Chinese University of Hong Kong (19) and Fudan University (11).
- Its most-cited paper of these years is “ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models” (2024), with 57 citations.

## Papers per year

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

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 239 | 238 |
| 2025 | – | – |
| 2026 | – | – |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| – | Papers of the volumes titled “IEEE/ACM International Conference on Computer-Aided Design” (ACM), found by DOI through Crossref |

## Who publishes there

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

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 20 |
| 2 | Chinese University of Hong Kong | 19 |
| 3 | [Fudan University](https://scholar4ai.com/institutions/fudan-university.md) | 11 |
| 4 | University of California San Diego | 10 |
| 5 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 10 |
| 6 | University of Notre Dame | 9 |
| 7 | [Southeast University](https://scholar4ai.com/institutions/southeast-university.md) | 9 |
| 8 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 8 |
| 9 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 8 |
| 10 | Arizona State University | 8 |

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

| # | Country | Papers |
|---:|---|---:|
| 1 | United States | 110 |
| 2 | China | 95 |
| 3 | Hong Kong | 28 |
| 4 | Germany | 16 |
| 5 | Taiwan | 16 |
| 6 | South Korea | 12 |
| 7 | United Kingdom | 6 |
| 8 | Switzerland | 4 |
| 9 | Greece | 4 |
| 10 | United Arab Emirates | 2 |

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

| # | Keyword | Papers |
|---:|---|---:|
| 1 | energy efficiency | 24 |
| 2 | in-memory computing | 24 |
| 3 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 19 |
| 4 | edge computing | 10 |
| 5 | hardware acceleration | 9 |
| 6 | non-volatile memory | 9 |
| 7 | resistive random access memory | 9 |
| 8 | design space exploration | 8 |
| 9 | electronic design automation | 8 |
| 10 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 8 |

## Most-cited papers

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

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 57 | [ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models](https://doi.org/10.1145/3676536.3676816) | 2024-10-27 | Yuxuan Yin, Yu Wang, Boxun Xu et al. |
| 2 | 41 | [MEIC: Re-thinking RTL Debug Automation using LLMs](https://doi.org/10.1145/3676536.3676801) | 2024-10-27 | Ke Xu, Jialin Sun, Yuchen Hu et al. |
| 3 | 36 | [OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection](https://doi.org/10.1145/3676536.3676830) | 2024-10-27 | Fan Cui, Chenyang Yin, Kexing Zhou et al. |
| 4 | 31 | [OpenLLM-RTL: Open Dataset and Benchmark for LLM-Aided Design RTL Generation](https://doi.org/10.1145/3676536.3697118) | 2024-10-27 | Shang Liu, Yao Lu, Wenji Fang et al. |
| 5 | 24 | [HLSPilot: LLM-based High-Level Synthesis](https://doi.org/10.1145/3676536.3676781) | 2024-10-27 | Chenwei Xiong, Cheng Liu, Huawei Li et al. |
| 6 | 23 | [RTLRewriter: Methodologies for Large Models aided RTL Code Optimization](https://doi.org/10.1145/3676536.3676775) | 2024-10-27 | Xufeng Yao, Yiwen Wang, Xing Li et al. |
| 7 | 23 | [DeepGate3: Towards Scalable Circuit Representation Learning](https://doi.org/10.1145/3676536.3676791) | 2024-10-27 | Zhengyuan Shi, Ziyang Zheng, Sadaf Khan et al. |
| 8 | 23 | [AnalogGym: An Open and Practical Testing Suite for Analog Circuit Synthesis](https://doi.org/10.1145/3676536.3697117) | 2024-10-27 | Jintao Li, Haochang Zhi, Ruiyu Lyu et al. |
| 9 | 23 | [MARCA: Mamba Accelerator with Reconfigurable Architecture](https://doi.org/10.1145/3676536.3676798) | 2024-10-27 | Jinhao Li, Shan Huang, Jiaming Xu et al. |
| 10 | 23 | [AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference](https://doi.org/10.1145/3676536.3676741) | 2024-10-27 | Shuzhang Zhong, Ling Liang, Yuan Wang et al. |

## Cite this page

Scholar4AI. (2026). ICCAD: IEEE/ACM International Conference on Computer-Aided Design — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/iccad

```bibtex
@misc{scholar4ai-proceedings-iccad,
  author = {{Scholar4AI}},
  title = {ICCAD: IEEE/ACM International Conference on Computer-Aided Design — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/iccad}},
  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/).