Song Chen

274 papers receiving 2.9k citations

Peers

Song Chen
Comparison fields: 5 of 142
  • Hardware and Architecture 349
  • Electrical and Electronic Engineering 1.4k
  • Computer Networks and Communications 448
  • Polymers and Plastics 239
  • Computer Vision and Pattern Recognition 337
Replace Pengjun Wang with:
Pengjun Wang China
Adesh Kumar India
Xiaofan Jiang United States
Bo Yuan China
Yongxin Liu China
Chen Dong China
Yun Ye China
Liang Liu China
Eng Gee Lim China
Hui Guo China
Song Chen relative to Pengjun Wang China Pengjun Wang's profile →
Citations per field
00.5×4.4×
Pengjun Wang · 1×
Citations per year

Countries citing papers authored by Song Chen

Since Specialization
Citations

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

Fields of papers citing papers by Song Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Song Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Song Chen Line = papers co-authored together Song Chen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 308 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015265
2 2019185
3 2012104
4 201485
5 201979
6 201879
7 201967
8 202064
9 200857
10 201750
11 202145
12 202244
13 202042
14 201942
15 202240
16 201539
17 202039
18 202437
19 201635
20 202134

About Song Chen

Song Chen is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications, Hardware and Architecture, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 308 papers that have together received 3.0k indexed citations. Recurring topics across this work include VLSI and FPGA Design Techniques (72 papers), Interconnection Networks and Systems (54 papers), Embedded Systems Design Techniques (36 papers), Advanced Memory and Neural Computing (28 papers), Low-power high-performance VLSI design (27 papers), 3D IC and TSV technologies (24 papers), Parallel Computing and Optimization Techniques (23 papers) and VLSI and Analog Circuit Testing (23 papers). The work is most often cited by research in Hardware and Architecture (349 citations), Electrical and Electronic Engineering (1.4k citations), Computer Networks and Communications (448 citations), Polymers and Plastics (239 citations) and Computer Vision and Pattern Recognition (337 citations). Song Chen has collaborated with scholars based in China, Japan and United States. Frequent co-authors include Kefeng Cai, Takeshi Yoshimura, Shirley Shen, Qi Xu, Sheqin Dong, Richard Donelson, Bei Yu, Yong Du, Hongxia Wang and Tong Lin. Their work appears in journals such as IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, ACM Transactions on Design Automation of Electronic Systems, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, IEEE Transactions on Circuits & Systems II Express Briefs and IEEE Transactions on Circuits and Systems for Video Technology.

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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