Shanshi Huang
- Hardware and Architecture top 5%
- Physical Unclonable Functions (PUFs) and Hardware Security 3
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- Advanced Memory and Neural Computing 23
- Ferroelectric and Negative Capacitance Devices 20
- Semiconductor materials and devices 5
- CCD and CMOS Imaging Sensors 4
- Artificial Intelligence top 5%
- Machine Learning and ELM 3
- Neural Networks and Reservoir Computing 2
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- Advanced Neural Network Applications 4
- Co-authors
- Shimeng YuXiaochen PengHongwu JiangXiaoyu SunAnni LuYandong LuoWantong LiFrancky Catthoor
- Cited by
- Hardware and ArchitectureElectrical and Electronic EngineeringCellular and Molecular Neuroscience
- Partner nations
- United StatesTaiwanHong Kong
In The Last Decade
Shanshi Huang
26 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 47
- Hardware and Architecture 118
- Electrical and Electronic Engineering 968
- Cellular and Molecular Neuroscience 160
- Artificial Intelligence 230
- Computer Vision and Pattern Recognition 126
Countries citing papers authored by Shanshi Huang
This map shows the geographic impact of Shanshi Huang'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 Shanshi Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shanshi Huang more than expected).
Fields of papers citing papers by Shanshi Huang
This network shows the impact of papers produced by Shanshi Huang. 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 Shanshi Huang. The network helps show where Shanshi Huang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Shanshi Huang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 3 | |
| 4 | 2022 | 35 | |
| 5 | 2022 | 41 | |
| 6 | 2022 | 15 | |
| 7 | 2022 | 2 | |
| 8 | 2022 | 4 | |
| 9 | 2021 | 14 | |
| 10 | 2021 | 37 | |
| 11 | Compute-in-Memory Chips for Deep Learning: Recent Trends and Prospectsbreakdown → | 2021 | 224 |
| 12 | 2021 | 31 | |
| 13 | 2021 | 7 | |
| 14 | 2021 | 15 | |
| 15 | 2020 | 10 | |
| 16 | 2020 | 23 | |
| 17 | 2020 | 185 | |
| 18 | 2020 | 56 | |
| 19 | 2020 | 35 | |
| 20 | 2019 | 12 |
About Shanshi Huang
Shanshi Huang is a scholar working on Hardware and Architecture, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 29 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (23 papers), Ferroelectric and Negative Capacitance Devices (20 papers), Semiconductor materials and devices (5 papers), CCD and CMOS Imaging Sensors (4 papers), Advanced Neural Network Applications (4 papers), Machine Learning and ELM (3 papers), Physical Unclonable Functions (PUFs) and Hardware Security (3 papers) and Neural Networks and Reservoir Computing (2 papers). The work is most often cited by research in Hardware and Architecture (118 citations), Electrical and Electronic Engineering (968 citations) and Cellular and Molecular Neuroscience (160 citations). Shanshi Huang has collaborated with scholars based in United States, Taiwan and Hong Kong. Frequent co-authors include Shimeng Yu, Xiaochen Peng, Hongwu Jiang, Xiaoyu Sun, Anni Lu, Yandong Luo, Wantong Li, Francky Catthoor, Stefan Cosemans and Chun-Ming Lin.
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.