Wei-Hsing Huang

1.4k total citations
10 papers, 540 citations indexed

About

Wei-Hsing Huang is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Wei-Hsing Huang has authored 10 papers receiving a total of 540 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Electrical and Electronic Engineering, 2 papers in Cellular and Molecular Neuroscience and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Wei-Hsing Huang's work include Advanced Memory and Neural Computing (8 papers), Ferroelectric and Negative Capacitance Devices (7 papers) and Neuroscience and Neural Engineering (2 papers). Wei-Hsing Huang is often cited by papers focused on Advanced Memory and Neural Computing (8 papers), Ferroelectric and Negative Capacitance Devices (7 papers) and Neuroscience and Neural Engineering (2 papers). Wei-Hsing Huang collaborates with scholars based in Taiwan, United States and China. Wei-Hsing Huang's co-authors include Meng‐Fan Chang, Kea‐Tiong Tang, Ren-Shuo Liu, Chih-Cheng Hsieh, Xin Si, Jiajing Chen, Yen-Cheng Chiu, Wei-Chen Wei, Yung-Ning Tu and Shimeng Yu and has published in prestigious journals such as Science, IEEE Journal of Solid-State Circuits and IEEE Transactions on Electron Devices.

In The Last Decade

Wei-Hsing Huang

8 papers receiving 532 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wei-Hsing Huang Taiwan	7	501	93	85	66	45	10	540
Yung-Ning Tu Taiwan	9	495 1.0×	81 0.9×	88 1.0×	72 1.1×	30 0.7×	11	525
Mustafa Ali United States	11	417 0.8×	90 1.0×	103 1.2×	58 0.9×	41 0.9×	17	473
Burak Erbagci United States	7	423 0.8×	87 0.9×	153 1.8×	47 0.7×	49 1.1×	12	467
Yun-Chen Lo Taiwan	7	311 0.6×	78 0.8×	37 0.4×	47 0.7×	47 1.0×	18	356
Chin-I Su Taiwan	8	437 0.9×	73 0.8×	44 0.5×	47 0.7×	83 1.8×	10	476
Sheng-Po Huang Taiwan	7	579 1.2×	100 1.1×	64 0.8×	69 1.0×	87 1.9×	10	631
Tai-Hao Wen Taiwan	8	312 0.6×	74 0.8×	41 0.5×	30 0.5×	48 1.1×	12	343
Leibin Ni China	8	299 0.6×	89 1.0×	51 0.6×	44 0.7×	46 1.0×	34	360
Sih-Han Li Taiwan	11	409 0.8×	70 0.8×	75 0.9×	26 0.4×	21 0.5×	23	440
Hui-Yao Kao Taiwan	7	515 1.0×	88 0.9×	52 0.6×	52 0.8×	83 1.8×	7	552

Countries citing papers authored by Wei-Hsing Huang

Since Specialization

Citations

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

Fields of papers citing papers by Wei-Hsing Huang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wei-Hsing 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 Wei-Hsing Huang. The network helps show where Wei-Hsing Huang may publish in the future.

Co-authorship network of co-authors of Wei-Hsing Huang

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Huang, Wei-Hsing, et al.. (2025). Hardware Acceleration of Kolmogorov-Arnold Network (KAN) for Lightweight Edge Inference. 693–699. 2 indexed citations

2.

Wen, Tai-Hao, Je-Min Hung, Wei-Hsing Huang, et al.. (2024). Fusion of memristor and digital compute-in-memory processing for energy-efficient edge computing. Science. 384(6693). 325–332. 47 indexed citations

3.

Huang, Wei-Hsing, et al.. (2024). Opposing Mean Error Compensation for Accuracy Enhancement in Analog Compute-in-Memory With Resistive Switching Devices. IEEE Transactions on Electron Devices. 72(2). 934–938.

4.

Wen, Tai-Hao, Hung-Hsi Hsu, Win-San Khwa, et al.. (2024). 34.8 A 22nm 16Mb Floating-Point ReRAM Compute-in-Memory Macro with 31.2TFLOPS/W for AI Edge Devices. 580–582. 20 indexed citations

5.

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

6.

Jiang, Hongwu, Shanshi Huang, Xiaochen Peng, et al.. (2020). A Two-way SRAM Array based Accelerator for Deep Neural Network On-chip Training. 28 indexed citations

7.

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

8.

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

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.

Wang, Chunyao, et al.. (2015). The Interaction between Contacting Barrier Materials for Containment of Radioactive Wastes. Advances in computer science research.

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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