Cong Xu

4.2k total citations · 5 hit papers
47 papers, 3.0k citations indexed

About

Cong Xu is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Cong Xu has authored 47 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Electrical and Electronic Engineering, 12 papers in Artificial Intelligence and 7 papers in Computer Networks and Communications. Recurrent topics in Cong Xu's work include Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (13 papers) and Semiconductor materials and devices (8 papers). Cong Xu is often cited by papers focused on Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (13 papers) and Semiconductor materials and devices (8 papers). Cong Xu collaborates with scholars based in China, United States and United Kingdom. Cong Xu's co-authors include Yuan Xie, Shuangchen Li, Jishen Zhao, Tao Zhang, Yongpan Liu, Yu Wang, Ping Chi, Chunhua Wang, Hairong Lin and Norman P. Jouppi and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Cong Xu

44 papers receiving 2.9k citations

Hit Papers

PRIME 2016 2026 2019 2022 2016 2016 2016 2021 2022 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Cong Xu China 19 2.2k 724 658 558 457 47 3.0k
Majid Ahmadi Canada 22 1.3k 0.6× 628 0.9× 293 0.4× 260 0.5× 154 0.3× 282 2.2k
Cong Xu China 29 1.5k 0.7× 362 0.5× 597 0.9× 1.2k 2.2× 996 2.2× 96 3.7k
P. Hasler United States 38 4.1k 1.9× 819 1.1× 138 0.2× 225 0.4× 355 0.8× 284 4.9k
Saibal Mukhopadhyay United States 39 4.4k 2.0× 912 1.3× 730 1.1× 628 1.1× 1.6k 3.5× 352 5.7k
G.S. Moschytz Switzerland 25 1.6k 0.7× 298 0.4× 337 0.5× 599 1.1× 136 0.3× 233 2.9k
Tetsuya Asai Japan 22 1.6k 0.7× 564 0.8× 191 0.3× 236 0.4× 52 0.1× 227 2.4k
Massimo Alioto Singapore 38 3.8k 1.8× 582 0.8× 488 0.7× 308 0.6× 1.2k 2.6× 302 4.7k
B.J. Sheu United States 26 1.9k 0.9× 623 0.9× 201 0.3× 256 0.5× 239 0.5× 179 2.6k
Janusz A. Starzyk United States 25 1.3k 0.6× 590 0.8× 154 0.2× 370 0.7× 491 1.1× 132 2.3k
Zhengya Zhang United States 29 2.9k 1.3× 583 0.8× 279 0.4× 1.1k 2.0× 188 0.4× 131 3.6k

Countries citing papers authored by Cong Xu

Since Specialization
Citations

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

Fields of papers citing papers by Cong Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cong Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Cong Xu. A scholar is included among the top collaborators of Cong Xu 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 Cong Xu. Cong Xu 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.
Xu, Cong, et al.. (2025). Deep-to-broad technique to accelerate deep neural networks. Expert Systems with Applications. 274. 126529–126529.
2.
Zhu, Hongjia, et al.. (2024). A novel capsule network based on Multi-Order Descartes Extension Transformation. Neurocomputing. 610. 128526–128526.
3.
Yang, Zhijian, Cong Xu, & Guochang Li. (2024). Behavior of GFRP tube confined hollow high-strength concrete short columns under axial compression. Structures. 61. 106017–106017. 9 indexed citations
4.
Liu, Ying, et al.. (2024). Air quality historical correlation model based on time series. Scientific Reports. 14(1). 22791–22791. 2 indexed citations
5.
Pedretti, Giacomo, Sergey Serebryakov, Ron M. Roth, et al.. (2024). X-TIME: Accelerating Large Tree Ensembles Inference for Tabular Data With Analog CAMs. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 10. 116–124. 2 indexed citations
6.
Xu, Cong, et al.. (2024). A novel multi-feature fusion attention neural network for the recognition of epileptic EEG signals. Frontiers in Computational Neuroscience. 18. 1393122–1393122. 3 indexed citations
7.
Xu, Cong, et al.. (2022). Memristive competitive hopfield neural network for image segmentation application. Cognitive Neurodynamics. 17(4). 1061–1077. 16 indexed citations
8.
Wang, Chunhua, et al.. (2022). The dynamics of a memristor-based Rulkov neuron with fractional-order difference. Chinese Physics B. 31(6). 60502–60502. 57 indexed citations
9.
Lin, Hairong, Chunhua Wang, Quanli Deng, et al.. (2021). Review on chaotic dynamics of memristive neuron and neural network. Nonlinear Dynamics. 106(1). 959–973. 227 indexed citations breakdown →
10.
Wen, Wei, Cong Xu, Feng Yan, et al.. (2017). TernGrad: ternary gradients to reduce communication in distributed deep learning. arXiv (Cornell University). 30. 1508–1518. 154 indexed citations
11.
Li, Shuangchen, Liu Liu, Peng Gu, Cong Xu, & Yuan Xie. (2016). NVSim-CAM. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1–7. 28 indexed citations
12.
Xu, Cong, Dimin Niu, Zheng Yang, Shimeng Yu, & Yuan Xie. (2015). Impact of Cell Failure on Reliable Cross-Point Resistive Memory Design. ACM Transactions on Design Automation of Electronic Systems. 20(4). 1–21. 14 indexed citations
13.
Yang, Zheng, Cong Xu, & Yuan Xie. (2015). Modeling framework for cross-point resistive memory design emphasizing reliability and variability issues. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 112–117. 8 indexed citations
14.
Xu, Cong, Pai-Yu Chen, Dimin Niu, et al.. (2014). Architecting 3D vertical resistive memory for next-generation storage systems. International Conference on Computer Aided Design. 55–62. 18 indexed citations
15.
Chen, Pai-Yu, Cong Xu, Yuan Xie, & Shimeng Yu. (2014). 3D RRAM design and benchmark with 3d NAND FLASH. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 100. 1–4. 2 indexed citations
16.
Xu, Cong, Dimin Niu, Shimeng Yu, & Yuan Xie. (2014). Modeling and design analysis of 3D vertical resistive memory — A low cost cross-point architecture. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 825–830. 22 indexed citations
17.
Niu, Dimin, Cong Xu, Naveen Muralimanohar, Norman P. Jouppi, & Yuan Xie. (2013). Design of cross-point metal-oxide ReRAM emphasizing reliability and cost. International Conference on Computer Aided Design. 17–23. 24 indexed citations
18.
Niu, Dimin, Cong Xu, Naveen Muralimanohar, Norman P. Jouppi, & Yuan Xie. (2013). Design of cross-point metal-oxide ReRAM emphasizing reliability and cost. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 41 indexed citations
19.
Xu, Cong, et al.. (2011). Device-architecture co-optimization of STT-RAM based memory for low power embedded systems. International Conference on Computer Aided Design. 463–470. 26 indexed citations
20.
Xu, Cong. (2009). Application of data mining methods to traditional prediction mathematical models. 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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