Xiaochen Peng
- Electrical and Electronic Engineering top 1%
- Artificial Intelligence top 2%
- Cellular and Molecular Neuroscience top 5%
- Computer Vision and Pattern Recognition top 5%
- Hardware and Architecture top 5%
- Topics
- Advanced Memory and Neural Computing (53 papers)Ferroelectric and Negative Capacitance Devices (49 papers)Advanced Neural Network Applications (17 papers)
- Cited by
- Electrical and Electronic EngineeringHardware and ArchitectureCellular and Molecular Neuroscience
- Journals
- IEEE Transactions on Electron DevicesIEEE Transactions on ComputersIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- Partner nations
- United StatesTaiwanSouth Korea
In The Last Decade
Xiaochen Peng
57 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 57
- Electrical and Electronic Engineering 2.7k
- Artificial Intelligence 631
- Cellular and Molecular Neuroscience 519
- Computer Vision and Pattern Recognition 338
- Hardware and Architecture 233
Countries citing papers authored by Xiaochen Peng
This map shows the geographic impact of Xiaochen Peng'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 Xiaochen Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaochen Peng more than expected).
Fields of papers citing papers by Xiaochen Peng
This network shows the impact of papers produced by Xiaochen Peng. 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 Xiaochen Peng. The network helps show where Xiaochen Peng may publish in the future.
Co-authorship network of co-authors of Xiaochen Peng
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaochen Peng. A scholar is included among the top collaborators of Xiaochen Peng 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 Xiaochen Peng. Xiaochen Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 8 | |
| 4 | 27 | |
| 5 | 14 | |
| 6 | Compute-in-Memory Chips for Deep Learning: Recent Trends and Prospectsbreakdown → | 224 |
| 7 | 38 | |
| 8 | 5 | |
| 9 | 23 | |
| 10 | 21 | |
| 11 | 10 | |
| 12 | 23 | |
| 13 | 185 | |
| 14 | 56 | |
| 15 | 18 | |
| 16 | 26 | |
| 17 | 170 | |
| 18 | 3 | |
| 19 | 20 | |
| 20 | 318 |
About Xiaochen Peng
Xiaochen Peng is a scholar working on Electrical and Electronic Engineering, Hardware and Architecture and Computer Vision and Pattern Recognition, having authored 59 papers that have together received 2.9k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (53 papers), Ferroelectric and Negative Capacitance Devices (49 papers) and Advanced Neural Network Applications (17 papers). The work is most often cited by research in Electrical and Electronic Engineering (2.7k citations), Hardware and Architecture (233 citations) and Cellular and Molecular Neuroscience (519 citations). Xiaochen Peng has collaborated with scholars based in United States, Taiwan and South Korea. Frequent co-authors include Shimeng Yu, Pai-Yu Chen, Shanshi Huang, Xiaoyu Sun, Hongwu Jiang, Yandong Luo, Anni Lu, Rui Liu, Jae-sun Seo and Rui Liu. Their work appears in journals such as IEEE Transactions on Electron Devices, IEEE Transactions on Computers and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
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.