Yung-Ning Tu

1.3k total citations
11 papers, 525 citations indexed

About

Yung-Ning Tu is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Yung-Ning Tu has authored 11 papers receiving a total of 525 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Electrical and Electronic Engineering, 3 papers in Computer Vision and Pattern Recognition and 1 paper in Computer Networks and Communications. Recurrent topics in Yung-Ning Tu's work include Advanced Memory and Neural Computing (9 papers), Ferroelectric and Negative Capacitance Devices (7 papers) and Advanced Neural Network Applications (3 papers). Yung-Ning Tu is often cited by papers focused on Advanced Memory and Neural Computing (9 papers), Ferroelectric and Negative Capacitance Devices (7 papers) and Advanced Neural Network Applications (3 papers). Yung-Ning Tu collaborates with scholars based in Taiwan, China and United States. Yung-Ning Tu's co-authors include Meng‐Fan Chang, Xin Si, Chih-Cheng Hsieh, Wei-Chen Wei, Yen-Cheng Chiu, Kea‐Tiong Tang, Ren-Shuo Liu, Jiajing Chen, Wei-Hsing Huang and Qiang Li and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, IET Generation Transmission & Distribution and IET Power Electronics.

In The Last Decade

Yung-Ning Tu

10 papers receiving 516 citations

Peers

Yung-Ning Tu

EEE

HA

AI

CVPR

CNC

Wei-Hsing Huang Taiwan

Burak Erbagci United States

Mustafa Ali United States

Sih-Han Li Taiwan

Yun-Chen Lo Taiwan

Chin-I Su Taiwan

Edouard Giacomin United States

Sheng-Po Huang Taiwan

Tai-Hao Wen Taiwan

Wei-Hsing Huang Taiwan

Yung-Ning Tu

501 ×1.0

EEE

85 ×1.0

HA

93 ×1.1

AI

66 ×0.9

CVPR

27 ×0.8

CNC

Citations per year, relative to Yung-Ning Tu Yung-Ning Tu (= 1×) peers Wei-Hsing Huang

Countries citing papers authored by Yung-Ning Tu

Since Specialization

Citations

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

Fields of papers citing papers by Yung-Ning Tu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yung-Ning Tu. 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 Yung-Ning Tu. The network helps show where Yung-Ning Tu may publish in the future.

Co-authorship network of co-authors of Yung-Ning Tu

This figure shows the co-authorship network connecting the top 25 collaborators of Yung-Ning Tu. A scholar is included among the top collaborators of Yung-Ning Tu 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 Yung-Ning Tu. Yung-Ning Tu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Li, Xiaojun, et al.. (2024). A novel non‐contact voltage measurement based on distribution lines. IET Generation Transmission & Distribution. 19(1).

2.

Yue, Jinshan, Yongpan Liu, Wenyu Sun, et al.. (2020). STICKER-T: An Energy-Efficient Neural Network Processor Using Block-Circulant Algorithm and Unified Frequency-Domain Acceleration. IEEE Journal of Solid-State Circuits. 56(6). 1936–1948. 8 indexed citations

3.

Chiu, Yen-Cheng, Zhixiao Zhang, Jiajing Chen, et al.. (2020). A 4-Kb 1-to-8-bit Configurable 6T SRAM-Based Computation-in-Memory Unit-Macro for CNN-Based AI Edge Processors. IEEE Journal of Solid-State Circuits. 55(10). 2790–2801. 65 indexed citations

4.

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

5.

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

6.

Srinivasa, Srivatsa, Wei-Hao Chen, Yung-Ning Tu, et al.. (2019). Monolithic-3D Integration Augmented Design Techniques for Computing in SRAMs. 1–5. 12 indexed citations

7.

Yue, Jinshan, Wenyu Sun, Zhe Yuan, et al.. (2019). 7.5 A 65nm 0.39-to-140.3TOPS/W 1-to-12b Unified Neural Network Processor Using Block-Circulant-Enabled Transpose-Domain Acceleration with 8.1 × Higher TOPS/mm²and 6T HBST-TRAM-Based 2D Data-Reuse Architecture. 138–140. 33 indexed citations

8.

Srinivasa, Srivatsa, Yung-Ning Tu, Xin Si, et al.. (2019). Monolithic 3D⁺ -IC based Reconfigurable Compute-in-Memory SRAM Macro. T32–T33. 10 indexed citations

9.

Zhang, Zhixiao, Jiajing Chen, Xin Si, et al.. (2019). A 55nm 1-to-8 bit Configurable 6T SRAM based Computing-in-Memory Unit-Macro for CNN-based AI Edge Processors. 217–218. 16 indexed citations

10.

Hsueh, Fu-Kuo, Wei-Hao Chen, Kai‐Shin Li, et al.. (2018). Ultra-Low Power 3D NC-FinFET-based Monolithic 3D⁺ -IC with Computing-in-Memory for Intelligent IoT Devices. 15.1.1–15.1.4. 8 indexed citations

11.

Tu, Yung-Ning, Tai‐Lang Jong, & C.M. Liaw. (2011). Development of a class-D audio amplifier with switch-mode rectifier front-end and its waveform control. IET Power Electronics. 4(9). 1002–1014. 11 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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