Xiaoyu Sun

3.2k total citations
55 papers, 2.5k citations indexed

About

Xiaoyu Sun is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Xiaoyu Sun has authored 55 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Electrical and Electronic Engineering, 12 papers in Computer Vision and Pattern Recognition and 9 papers in Artificial Intelligence. Recurrent topics in Xiaoyu Sun's work include Advanced Memory and Neural Computing (35 papers), Ferroelectric and Negative Capacitance Devices (32 papers) and Advanced Neural Network Applications (10 papers). Xiaoyu Sun is often cited by papers focused on Advanced Memory and Neural Computing (35 papers), Ferroelectric and Negative Capacitance Devices (32 papers) and Advanced Neural Network Applications (10 papers). Xiaoyu Sun collaborates with scholars based in United States, China and Taiwan. Xiaoyu Sun's co-authors include Shimeng Yu, Xiaochen Peng, Shanshi Huang, Jae-sun Seo, Rui Liu, Shihui Yin, Meng‐Fan Chang, Yandong Luo, Xin Si and Jiajing Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Journal of Solid-State Circuits.

In The Last Decade

Xiaoyu Sun

51 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaoyu Sun United States 26 2.2k 454 317 289 279 55 2.5k
David Bol Belgium 25 1.5k 0.7× 162 0.4× 72 0.2× 206 0.7× 71 0.3× 156 1.8k
Hyunsoo Kim South Korea 15 541 0.3× 155 0.3× 55 0.2× 67 0.2× 70 0.3× 66 1.1k
Pierre‐Emmanuel Gaillardon Switzerland 33 3.1k 1.4× 367 0.8× 399 1.3× 777 2.7× 44 0.2× 206 3.7k
Ashish Ranjan United States 15 764 0.4× 200 0.4× 31 0.1× 301 1.0× 228 0.8× 23 992
Sangjin Kim South Korea 19 833 0.4× 202 0.4× 29 0.1× 79 0.3× 380 1.4× 114 1.4k
Ziyu Wang China 18 578 0.3× 216 0.5× 87 0.3× 29 0.1× 73 0.3× 99 1.1k
Yasuhiko Nakashima Japan 17 411 0.2× 290 0.6× 60 0.2× 187 0.6× 141 0.5× 182 971
Xiangyu Dong China 22 2.2k 1.0× 188 0.4× 113 0.4× 1.5k 5.1× 110 0.4× 75 3.1k
Shanshan Liu China 22 1.0k 0.5× 120 0.3× 21 0.1× 203 0.7× 46 0.2× 176 1.6k

Countries citing papers authored by Xiaoyu Sun

Since Specialization
Citations

This map shows the geographic impact of Xiaoyu Sun'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 Xiaoyu Sun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoyu Sun more than expected).

Fields of papers citing papers by Xiaoyu Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaoyu Sun. 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 Xiaoyu Sun. The network helps show where Xiaoyu Sun may publish in the future.

Co-authorship network of co-authors of Xiaoyu Sun

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoyu Sun. A scholar is included among the top collaborators of Xiaoyu Sun 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 Xiaoyu Sun. Xiaoyu Sun 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.
Sun, Xiaoyu, et al.. (2025). FSE-Mamba: A novel Frequency-Spatial Entanglement Mamba model for retinal vessel segmentation. Computers in Biology and Medicine. 196(Pt B). 110776–110776.
2.
Sun, Xiaoyu, Weidong Cao, Brian Crafton, et al.. (2023). Efficient Processing of MLPerf Mobile Workloads Using Digital Compute-In-Memory Macros. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 43(4). 1191–1205. 10 indexed citations
3.
Li, Wantong, Xiaoyu Sun, Shanshi Huang, Hongwu Jiang, & Shimeng Yu. (2022). A 40-nm MLC-RRAM Compute-in-Memory Macro With Sparsity Control, On-Chip Write-Verify, and Temperature-Independent ADC References. IEEE Journal of Solid-State Circuits. 57(9). 2868–2877. 41 indexed citations
4.
Lin, Bohan, Yachuan Pang, Bin Gao, et al.. (2021). A Highly Reliable RRAM Physically Unclonable Function Utilizing Post-Process Randomness Source. IEEE Journal of Solid-State Circuits. 56(5). 1641–1650. 46 indexed citations
5.
Quan, Lijun, et al.. (2021). Quantifying Intensities of Transcription Factor-DNA Binding by Learning From an Ensemble of Protein Binding Microarrays. IEEE Journal of Biomedical and Health Informatics. 25(7). 2811–2819. 5 indexed citations
6.
Sun, Xiaoyu, et al.. (2021). VVC Fast ME Algorithm Based on Spatial Texture Features and Time Correlation. 371–377. 3 indexed citations
7.
Wang, Panni, Zheng Wang, Xiaoyu Sun, et al.. (2020). Investigating Ferroelectric Minor Loop Dynamics and History Effect—Part I: Device Characterization. IEEE Transactions on Electron Devices. 67(9). 3592–3597. 23 indexed citations
8.
Yin, Shihui, Yulhwa Kim, Xiaoyu Sun, et al.. (2020). 2-Bit-Per-Cell RRAM-Based In-Memory Computing for Area-/Energy-Efficient Deep Learning. IEEE Solid-State Circuits Letters. 3. 194–197. 50 indexed citations
9.
Wang, Panni, Zheng Wang, Xiaoyu Sun, et al.. (2020). Investigating Ferroelectric Minor Loop Dynamics and History Effect—Part II: Physical Modeling and Impact on Neural Network Training. IEEE Transactions on Electron Devices. 67(9). 3598–3604. 18 indexed citations
10.
Zhu, Ming, et al.. (2020). BLAC: A Named Entity Recognition Model Incorporating Part-of-Speech Attention in Irregular Short Text. abs 1611 0. 56–61. 1 indexed citations
11.
Yin, Shihui, Xiaoyu Sun, Shimeng Yu, & Jae-sun Seo. (2020). High-Throughput In-Memory Computing for Binary Deep Neural Networks With Monolithically Integrated RRAM and 90-nm CMOS. IEEE Transactions on Electron Devices. 67(10). 4185–4192. 123 indexed citations
12.
Si, Xin, Jiajing Chen, Yung-Ning Tu, et al.. (2019). A Twin-8T SRAM Computation-in-Memory Unit-Macro for Multibit CNN-Based AI Edge Processors. IEEE Journal of Solid-State Circuits. 55(1). 189–202. 159 indexed citations
13.
14.
Si, Xin, Meng‐Fan Chang, Win-San Khwa, et al.. (2019). A Dual-Split 6T SRAM-Based Computing-in-Memory Unit-Macro With Fully Parallel Product-Sum Operation for Binarized DNN Edge Processors. IEEE Transactions on Circuits and Systems I Regular Papers. 66(11). 4172–4185. 115 indexed citations
15.
Si, Xin, Jiajing Chen, Yung-Ning Tu, et al.. (2019). 24.5 A Twin-8T SRAM Computation-In-Memory Macro for Multiple-Bit CNN-Based Machine Learning. 396–398. 203 indexed citations
16.
Sun, Xiaoyu, Rui Liu, Xiaochen Peng, & Shimeng Yu. (2018). Computing-in-Memory with SRAM and RRAM for Binary Neural Networks. 1–4. 26 indexed citations
17.
Kim, Seyoung, Xiaoyu Sun, P. M. Solomon, et al.. (2018). Capacitor-based Cross-point Array for Analog Neural Network with Record Symmetry and Linearity. 25–26. 33 indexed citations
18.
Sun, Xiaoyu, Panni Wang, Kai Ni, Suman Datta, & Shimeng Yu. (2018). Exploiting Hybrid Precision for Training and Inference: A 2T-1FeFET Based Analog Synaptic Weight Cell. 3.1.1–3.1.4. 83 indexed citations
19.
20.
Chu, Yanhui, Zhenyu Wu, Jiayi Ji, et al.. (2017). Effects of school breaks on influenza-like illness incidence in a temperate Chinese region: an ecological study from 2008 to 2015. BMJ Open. 7(3). e013159–e013159. 13 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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