Sanghoon Kang

1.5k total citations
48 papers, 1.1k citations indexed

About

Sanghoon Kang is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Computational Mechanics. According to data from OpenAlex, Sanghoon Kang has authored 48 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 20 papers in Electrical and Electronic Engineering and 4 papers in Computational Mechanics. Recurrent topics in Sanghoon Kang's work include Advanced Memory and Neural Computing (15 papers), Advanced Neural Network Applications (14 papers) and Ferroelectric and Negative Capacitance Devices (8 papers). Sanghoon Kang is often cited by papers focused on Advanced Memory and Neural Computing (15 papers), Advanced Neural Network Applications (14 papers) and Ferroelectric and Negative Capacitance Devices (8 papers). Sanghoon Kang collaborates with scholars based in South Korea, United States and Switzerland. Sanghoon Kang's co-authors include Hoi‐Jun Yoo, Changhyeon Kim, Sangyeob Kim, Jinmook Lee, Dongjoo Shin, Donghyeon Han, Sungpill Choi, Juhyoung Lee, Dongseok Im and Kyeongryeol Bong and has published in prestigious journals such as SHILAP Revista de lepidopterología, Psychological Science and IEEE Journal of Solid-State Circuits.

In The Last Decade

Sanghoon Kang

46 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sanghoon Kang South Korea 15 667 606 302 112 81 48 1.1k
Jinmook Lee South Korea 15 830 1.2× 711 1.2× 383 1.3× 146 1.3× 101 1.2× 25 1.2k
Hiroki Nakahara Japan 16 511 0.8× 485 0.8× 321 1.1× 202 1.8× 110 1.4× 98 971
Donghyeon Han South Korea 17 564 0.8× 464 0.8× 246 0.8× 132 1.2× 62 0.8× 70 989
Fengbin Tu China 18 820 1.2× 561 0.9× 398 1.3× 250 2.2× 127 1.6× 64 1.3k
Peng Ouyang China 19 751 1.1× 625 1.0× 404 1.3× 175 1.6× 102 1.3× 77 1.4k
Bert Moons Belgium 12 780 1.2× 438 0.7× 296 1.0× 152 1.4× 117 1.4× 18 1.1k
Liangzhen Lai United States 14 530 0.8× 415 0.7× 316 1.0× 171 1.5× 94 1.2× 31 977
Berin Martini United States 10 617 0.9× 589 1.0× 248 0.8× 148 1.3× 58 0.7× 13 948
Lingzhi Sui China 7 445 0.7× 493 0.8× 208 0.7× 111 1.0× 44 0.5× 11 712

Countries citing papers authored by Sanghoon Kang

Since Specialization
Citations

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

Fields of papers citing papers by Sanghoon Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sanghoon Kang

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

All Works

20 of 20 papers shown
1.
Im, Dongseok, Gwangtae Park, Zhiyong Li, et al.. (2023). A Mobile 3-D Object Recognition Processor With Deep-Learning-Based Monocular Depth Estimation. IEEE Micro. 43(3). 74–82. 2 indexed citations
2.
Kang, Sanghoon, et al.. (2023). Acute Stress Enhances Memory and Preference for Smoking-Related Associations in Smokers. Nicotine & Tobacco Research. 26(3). 333–341. 1 indexed citations
3.
Im, Dongseok, Gwangtae Park, Zhiyong Li, et al.. (2022). DSPU: An Efficient Deep Learning-Based Dense RGB-D Data Acquisition With Sensor Fusion and 3-D Perception SoC. IEEE Journal of Solid-State Circuits. 58(1). 177–188. 3 indexed citations
4.
Kang, Sanghoon, et al.. (2022). Information search by smartphone and tourists’ spatial behavior. International Journal of Tourism and Hospitality Research. 36(4). 71–81. 1 indexed citations
5.
Im, Jooyeon Jamie, Seunghee Na, Sanghoon Kang, et al.. (2022). A Randomized, Double-Blind, Sham-Controlled Trial of Transcranial Direct Current Stimulation for the Treatment of Persistent Postural-Perceptual Dizziness (PPPD). Frontiers in Neurology. 13. 868976–868976. 10 indexed citations
6.
Kim, Sangyeob, et al.. (2022). TSUNAMI: Triple Sparsity-Aware Ultra Energy-Efficient Neural Network Training Accelerator With Multi-Modal Iterative Pruning. IEEE Transactions on Circuits and Systems I Regular Papers. 69(4). 1494–1506. 14 indexed citations
7.
Han, Donghyeon, Sanghoon Kang, Sangyeob Kim, Juhyoung Lee, & Hoi‐Jun Yoo. (2022). Energy-Efficient DNN Training Processors on Micro-AI Systems. SHILAP Revista de lepidopterología. 2. 259–275. 5 indexed citations
8.
Im, Dongseok, Gwangtae Park, Zhiyong Li, et al.. (2022). DSPU: A 281.6mW Real-Time Depth Signal Processing Unit for Deep Learning-Based Dense RGB-D Data Acquisition with Depth Fusion and 3D Bounding Box Extraction in Mobile Platforms. 2022 IEEE International Solid- State Circuits Conference (ISSCC). 510–512. 11 indexed citations
9.
Kang, Sanghoon, Gwangtae Park, Sangjin Kim, et al.. (2021). An Overview of Sparsity Exploitation in CNNs for On-Device Intelligence With Software-Hardware Cross-Layer Optimizations. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 11(4). 634–648. 15 indexed citations
10.
Kim, Sangyeob, Juhyoung Lee, Sanghoon Kang, Jinsu Lee, & Hoi‐Jun Yoo. (2020). A Power-Efficient CNN Accelerator With Similar Feature Skipping for Face Recognition in Mobile Devices. IEEE Transactions on Circuits and Systems I Regular Papers. 67(4). 1181–1193. 23 indexed citations
11.
Lee, Jinsu, Sanghoon Kang, Jinmook Lee, et al.. (2020). The Hardware and Algorithm Co-Design for Energy-Efficient DNN Processor on Edge/Mobile Devices. IEEE Transactions on Circuits and Systems I Regular Papers. 67(10). 3458–3470. 36 indexed citations
12.
Kang, Sanghoon, Donghyeon Han, Juhyoung Lee, et al.. (2020). 7.4 GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation. 140–142. 59 indexed citations
13.
Im, Dongseok, Sanghoon Kang, Donghyeon Han, Sungpill Choi, & Hoi‐Jun Yoo. (2020). A 4.45 ms Low-Latency 3D Point-Cloud-Based Neural Network Processor for Hand Pose Estimation in Immersive Wearable Devices. 1–2. 11 indexed citations
14.
Kim, Sangyeob, Juhyoung Lee, Sanghoon Kang, Jinmook Lee, & Hoi‐Jun Yoo. (2020). A 146.52 TOPS/W Deep-Neural-Network Learning Processor with Stochastic Coarse-Fine Pruning and Adaptive Input/Output/Weight Skipping. 1–2. 24 indexed citations
15.
Kang, Sanghoon, Hanhoon Park, & Jong-Il Park. (2019). CNN-Based Ternary Classification for Image Steganalysis. Electronics. 8(11). 1225–1225. 6 indexed citations
16.
Lee, Juhyoung, Dongjoo Shin, Jinsu Lee, et al.. (2019). A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices. 29 indexed citations
17.
Lee, Jinmook, Changhyeon Kim, Sanghoon Kang, et al.. (2018). UNPU: A 50.6TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision. 218–220. 232 indexed citations
18.
Kang, Sanghoon, et al.. (2018). Mobile Augmented Reality Application for Early Childhood Language Education. Journal of Broadcast Engineering. 23(6). 914–924. 1 indexed citations
19.
Lee, Jinmook, Changhyeon Kim, Sanghoon Kang, et al.. (2018). UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision. IEEE Journal of Solid-State Circuits. 54(1). 173–185. 236 indexed citations
20.
Bong, Kyeongryeol, Sungpill Choi, Changhyeon Kim, et al.. (2017). 14.6 A 0.62mW ultra-low-power convolutional-neural-network face-recognition processor and a CIS integrated with always-on haar-like face detector. 248–249. 106 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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