---
title: "FPGA (A): papers and universities"
description: "FPGA (ACM Int'l Symposium on Field Programmable Gate Arrays) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 141 of its papers from 2024–2026 (43 in 2024, 44 in 2025, 54 in 2026). By the countries of the authors' institutions, the most papers come from the United States (76), China (31) and the United Kingdom (12)."
url: https://scholar4ai.com/proceedings/fpga
markdown_url: https://scholar4ai.com/proceedings/fpga.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "FPGA (ACM Int'l Symposium on Field Programmable Gate Arrays) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 141 of its papers from 2024–2026 (43 in 2024, 44 in 2025, 54 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)
---

# FPGA: ACM Int'l Symposium on Field Programmable Gate Arrays — papers and institutions, 2024–2026

Papers of FPGA, ACM Int'l Symposium on Field Programmable Gate Arrays (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

- FPGA (ACM Int'l Symposium on Field Programmable Gate Arrays) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 141 of its papers from 2024–2026 (43 in 2024, 44 in 2025, 54 in 2026).
- By the countries of the authors' institutions, the most papers come from the United States (76), China (31) and the United Kingdom (12).
- The institutions with the most papers are Arizona State University (9), University of California, Los Angeles (8) and University of Florida (7).
- Its most-cited paper of these years is “FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs” (2024), with 139 citations.

## Papers per year

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

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 43 | 41 |
| 2025 | 44 | 42 |
| 2026 | 54 | 52 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S7407087960](https://openalex.org/S7407087960) | The conference's proceedings |

## Who publishes there

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

| # | Institution | Papers |
|---:|---|---:|
| 1 | Arizona State University | 9 |
| 2 | University of California, Los Angeles | 8 |
| 3 | University of Florida | 7 |
| 4 | ETH Zurich | 7 |
| 5 | Imperial College London | 7 |
| 6 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 6 |
| 7 | École Polytechnique Fédérale de Lausanne | 6 |
| 8 | University of Southern California | 5 |
| 9 | [University of Science and Technology of China](https://scholar4ai.com/institutions/university-of-science-and-technology-of-china.md) | 5 |
| 10 | Intel (United States) | 5 |

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

| # | Country | Papers |
|---:|---|---:|
| 1 | United States | 76 |
| 2 | China | 31 |
| 3 | United Kingdom | 12 |
| 4 | Switzerland | 11 |
| 5 | Canada | 8 |
| 6 | Hong Kong | 6 |
| 7 | Germany | 5 |
| 8 | India | 3 |
| 9 | Taiwan | 3 |
| 10 | Australia | 2 |

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

| # | Keyword | Papers |
|---:|---|---:|
| 1 | FPGA | 32 |
| 2 | FPGA accelerator | 23 |
| 3 | high-level synthesis | 19 |
| 4 | hardware acceleration | 15 |
| 5 | FPGA acceleration | 13 |
| 6 | energy efficiency | 13 |
| 7 | design space exploration | 7 |
| 8 | hardware description languages | 6 |
| 9 | memory bandwidth | 6 |
| 10 | FPGA implementation | 5 |

## Most-cited papers

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

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 139 | [FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs](https://doi.org/10.1145/3626202.3637562) | 2024-04-01 | Shulin Zeng, Jun Liu, Guohao Dai et al. |
| 2 | 35 | [SSR: Spatial Sequential Hybrid Architecture for Latency Throughput Tradeoff in Transformer Acceleration](https://doi.org/10.1145/3626202.3637569) | 2024-04-01 | Jinming Zhuang, Zhuoping Yang, Shixin Ji et al. |
| 3 | 21 | [TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees](https://doi.org/10.1145/3706628.3708877) | 2025-02-26 | Alireza Khataei, Kia Bazargan |
| 4 | 20 | [Hardcaml MSM: A High-Performance Split CPU-FPGA Multi-Scalar Multiplication Engine](https://doi.org/10.1145/3626202.3637577) | 2024-04-01 | Andy Ray, Benjamin Devlin, Fu Yong Quah et al. |
| 5 | 19 | [HiSpMV: Hybrid Row Distribution and Vector Buffering for Imbalanced SpMV Acceleration on FPGAs](https://doi.org/10.1145/3626202.3637557) | 2024-04-01 | Manoj Bheemasandra Rajashekar, Xingyu Tian, Zhenman Fang |
| 6 | 16 | [Greater than the Sum of its LUTs: Scaling Up LUT-based Neural Networks with AmigoLUT](https://doi.org/10.1145/3706628.3708874) | 2025-02-26 | Olivia Weng, Marta Andronic, Danial Zuberi et al. |
| 7 | 16 | [FlightVGM: Efficient Video Generation Model Inference with Online Sparsification and Hybrid Precision on FPGAs](https://doi.org/10.1145/3706628.3708864) | 2025-02-26 | Jun Liu, Shulin Zeng, Li Ding et al. |
| 8 | 16 | [Stream-HLS: Towards Automatic Dataflow Acceleration](https://doi.org/10.1145/3706628.3708878) | 2025-02-26 | Suhail Basalama, Jason Cong |
| 9 | 16 | [A Composable Dynamic Sparse Dataflow Architecture for Efficient Event-based Vision Processing on FPGA](https://doi.org/10.1145/3626202.3637558) | 2024-04-01 | Yizhao Gao, Baoheng Zhang, Yuhao Ding et al. |
| 10 | 15 | [SuperNIC: An FPGA-Based, Cloud-Oriented SmartNIC](https://doi.org/10.1145/3626202.3637564) | 2024-04-01 | Will Lin, Yizhou Shan, Ryan Kosta et al. |

## Cite this page

Scholar4AI. (2026). FPGA: ACM Int'l Symposium on Field Programmable Gate Arrays — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/fpga

```bibtex
@misc{scholar4ai-proceedings-fpga,
  author = {{Scholar4AI}},
  title = {FPGA: ACM Int'l Symposium on Field Programmable Gate Arrays — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/fpga}},
  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/).