Hongxiang Fan

835 total citations
43 papers, 541 citations indexed

About

Hongxiang Fan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Hongxiang Fan has authored 43 papers receiving a total of 541 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computer Vision and Pattern Recognition, 26 papers in Artificial Intelligence and 15 papers in Electrical and Electronic Engineering. Recurrent topics in Hongxiang Fan's work include Advanced Neural Network Applications (25 papers), Adversarial Robustness in Machine Learning (9 papers) and CCD and CMOS Imaging Sensors (6 papers). Hongxiang Fan is often cited by papers focused on Advanced Neural Network Applications (25 papers), Adversarial Robustness in Machine Learning (9 papers) and CCD and CMOS Imaging Sensors (6 papers). Hongxiang Fan collaborates with scholars based in United Kingdom, China and Japan. Hongxiang Fan's co-authors include Wayne Luk, Xinyu Niu, Shuanglong Liu, Zhiqiang Que, Martin Ferianc, Ho-Cheung Ng, Cheng Luo, Ce Guo, Miguel R. D. Rodrigues and Сhen Liu and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Computers and IEEE Transactions on Parallel and Distributed Systems.

In The Last Decade

Hongxiang Fan

40 papers receiving 530 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Hongxiang Fan United Kingdom 16 284 228 211 70 35 43 541
Shuanglong Liu United Kingdom 14 250 0.9× 199 0.9× 200 0.9× 57 0.8× 46 1.3× 34 486
Yiming Hu China 9 399 1.4× 292 1.3× 268 1.3× 114 1.6× 46 1.3× 24 651
Xuefei Ning China 13 184 0.6× 281 1.2× 420 2.0× 123 1.8× 62 1.8× 44 737
Junbin Wang China 6 329 1.2× 153 0.7× 277 1.3× 53 0.8× 30 0.9× 8 487
Juhyoung Lee South Korea 17 378 1.3× 188 0.8× 444 2.1× 116 1.7× 65 1.9× 44 741
Lingzhi Sui China 7 493 1.7× 208 0.9× 445 2.1× 111 1.6× 44 1.3× 11 712
Xiaolong Ma United States 15 414 1.5× 322 1.4× 225 1.1× 51 0.7× 53 1.5× 33 696
Duncan J. M. Moss Australia 8 213 0.8× 161 0.7× 270 1.3× 138 2.0× 100 2.9× 13 516
Shimpei Sato Japan 12 310 1.1× 147 0.6× 371 1.8× 94 1.3× 74 2.1× 52 588

Countries citing papers authored by Hongxiang Fan

Since Specialization
Citations

This map shows the geographic impact of Hongxiang Fan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hongxiang Fan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hongxiang Fan more than expected).

Fields of papers citing papers by Hongxiang Fan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hongxiang Fan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hongxiang Fan. The network helps show where Hongxiang Fan may publish in the future.

Co-authorship network of co-authors of Hongxiang Fan

This figure shows the co-authorship network connecting the top 25 collaborators of Hongxiang Fan. A scholar is included among the top collaborators of Hongxiang Fan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hongxiang Fan. Hongxiang Fan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Fan, Hongxiang, et al.. (2025). DESA: Dataflow Efficient Systolic Array for Acceleration of Transformers. IEEE Transactions on Computers. 74(6). 2058–2072.
3.
Chen, Hao, Wayne Luk, Rui Li, et al.. (2025). Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference. 2221–2238. 1 indexed citations
4.
Que, Zhiqiang, et al.. (2025). Trustworthy Deep Learning Acceleration with Customizable Design Flow Automation. 1–13. 1 indexed citations
5.
Que, Zhiqiang, Minghao Zhang, Hongxiang Fan, et al.. (2024). Low Latency Variational Autoencoder on FPGAs. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 14(2). 323–333. 2 indexed citations
6.
Que, Zhiqiang, Hongxiang Fan, He Li, et al.. (2024). LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics. ACM Transactions on Embedded Computing Systems. 23(2). 1–28. 15 indexed citations
7.
Que, Zhiqiang, Hiroki Nakahara, Hongxiang Fan, et al.. (2022). Remarn: A Reconfigurable Multi-threaded Multi-core Accelerator for Recurrent Neural Networks. ACM Transactions on Reconfigurable Technology and Systems. 16(1). 1–26. 4 indexed citations
8.
Fan, Hongxiang, Martin Ferianc, Zhiqiang Que, et al.. (2022). Accelerating Bayesian Neural Networks via Algorithmic and Hardware Optimizations. IEEE Transactions on Parallel and Distributed Systems. 33(12). 3387–3399. 4 indexed citations
9.
Fan, Hongxiang, Martin Ferianc, Zhiqiang Que, et al.. (2022). FPGA-Based Acceleration for Bayesian Convolutional Neural Networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41(12). 5343–5356. 26 indexed citations
10.
Liu, Shuanglong, Hongxiang Fan, & Wayne Luk. (2022). Design of Fully Spectral CNNs for Efficient FPGA-Based Acceleration. IEEE Transactions on Neural Networks and Learning Systems. 35(6). 8111–8123. 6 indexed citations
11.
Ferianc, Martin, Zhiqiang Que, Hongxiang Fan, Wayne Luk, & Miguel R. D. Rodrigues. (2021). Optimizing Bayesian Recurrent Neural Networks on an FPGA-based \n Accelerator. UCL Discovery (University College London). 5 indexed citations
12.
Li, He, Hongxiang Fan, & Jiawei Liang. (2021). Quantum Most-Significant Digit-First Addition. 22. 1–8. 3 indexed citations
13.
Fan, Hongxiang, Martin Ferianc, Miguel R. D. Rodrigues, et al.. (2021). High-Performance FPGA-based Accelerator for Bayesian Neural Networks. 1063–1068. 17 indexed citations
14.
Liu, Shuanglong, Hongxiang Fan, & Wayne Luk. (2021). Accelerating Fully Spectral CNNs with Adaptive Activation Functions on FPGA. Spiral (Imperial College London). 1530–1535. 3 indexed citations
15.
Que, Zhiqiang, Hiroki Nakahara, Eriko Nurvitadhi, et al.. (2021). Recurrent Neural Networks With Column-Wise Matrix–Vector Multiplication on FPGAs. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 30(2). 227–237. 16 indexed citations
16.
Que, Zhiqiang, et al.. (2020). Mapping Large LSTMs to FPGAs with Weight Reuse. Journal of Signal Processing Systems. 92(9). 965–979. 23 indexed citations
17.
Fan, Hongxiang, Cheng Luo, Martin Ferianc, et al.. (2019). F-E3D: FPGA-based Acceleration of an Efficient 3D Convolutional Neural Network for Human Action Recognition. 1–8. 39 indexed citations
18.
Liu, Shuanglong, et al.. (2018). Optimizing CNN-based Segmentation with Deeply Customized Convolutional and Deconvolutional Architectures on FPGA. ACM Transactions on Reconfigurable Technology and Systems. 11(3). 1–22. 56 indexed citations
19.
Fan, Hongxiang, Ho-Cheung Ng, Shuanglong Liu, et al.. (2018). Reconfigurable Acceleration of 3D-CNNs for Human Action Recognition with Block Floating-Point Representation. 287–2877. 19 indexed citations
20.
Liu, Qiang, et al.. (2016). IC security evaluation against fault injection attack based on FPGA emulation. 285–288. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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