Soyeon Kim

608 total citations
49 papers, 383 citations indexed

About

Soyeon Kim is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Soyeon Kim has authored 49 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Electrical and Electronic Engineering, 16 papers in Computer Vision and Pattern Recognition and 11 papers in Artificial Intelligence. Recurrent topics in Soyeon Kim's work include Advanced Memory and Neural Computing (14 papers), Ferroelectric and Negative Capacitance Devices (10 papers) and Neural Networks and Reservoir Computing (7 papers). Soyeon Kim is often cited by papers focused on Advanced Memory and Neural Computing (14 papers), Ferroelectric and Negative Capacitance Devices (10 papers) and Neural Networks and Reservoir Computing (7 papers). Soyeon Kim collaborates with scholars based in South Korea, Switzerland and United States. Soyeon Kim's co-authors include Hoi‐Jun Yoo, Sangyeob Kim, Donghyeon Han, Sangjin Kim, Sanghoon Kang, Juhyoung Lee, Dongseok Im, Minsung Kim, Jeong-Ryeol Kim and Kwantae Kim and has published in prestigious journals such as Chemical Engineering Journal, Energy and Molecules.

In The Last Decade

Soyeon Kim

43 papers receiving 367 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Soyeon Kim South Korea 13 208 110 98 31 28 49 383
Huang Wei China 11 197 0.9× 72 0.7× 59 0.6× 18 0.6× 36 1.3× 64 613
Chixiao Chen China 13 393 1.9× 99 0.9× 93 0.9× 11 0.4× 79 2.8× 96 557
Pradeep Kumar India 11 263 1.3× 46 0.4× 29 0.3× 18 0.6× 14 0.5× 89 429
Daniel Bankman United States 10 361 1.7× 102 0.9× 121 1.2× 7 0.2× 33 1.2× 13 437
Xiaobin Wang China 11 82 0.4× 52 0.5× 73 0.7× 28 0.9× 6 0.2× 23 243
Luiz Carlos Gouveia United Kingdom 8 167 0.8× 57 0.5× 32 0.3× 8 0.3× 17 0.6× 18 315
Yuxiang Fu China 11 243 1.2× 45 0.4× 92 0.9× 8 0.3× 55 2.0× 69 377
Muya Chang United States 14 392 1.9× 60 0.5× 128 1.3× 20 0.6× 97 3.5× 35 502
Shubham Jain United States 15 528 2.5× 95 0.9× 134 1.4× 93 3.0× 82 2.9× 33 721
Tobias Gemmeke Germany 12 338 1.6× 41 0.4× 100 1.0× 13 0.4× 88 3.1× 70 517

Countries citing papers authored by Soyeon Kim

Since Specialization
Citations

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

Fields of papers citing papers by Soyeon Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Soyeon Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Soyeon Kim. A scholar is included among the top collaborators of Soyeon Kim 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 Soyeon Kim. Soyeon Kim 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.
Kim, Sangyeob, et al.. (2025). C-Transformer: An Energy-Efficient Homogeneous DNN-Transformer/SNN-Transformer Processor for Large Language Models. IEEE Journal of Solid-State Circuits. 60(10). 3802–3815.
2.
Kim, Sangyeob, et al.. (2024). Two-Step Spike Encoding Scheme and Architecture for Highly Sparse Spiking-Neural-Network. 1–5. 1 indexed citations
3.
Kim, Soyeon, Minseuk Kim, Gi‐Yeop Kim, et al.. (2024). Site selectivity of single dopant in high-nickel cathodes for lithium-ion batteries. Chemical Engineering Journal. 482. 148869–148869. 13 indexed citations
4.
Kim, Sangyeob, Soyeon Kim, Sangjin Kim, et al.. (2023). COOL-NPU: Complementary Online Learning Neural Processing Unit. IEEE Micro. 44(1). 28–37. 1 indexed citations
5.
Kim, Sangyeob, et al.. (2023). Neuro-CIM: ADC-Less Neuromorphic Computing-in-Memory Processor With Operation Gating/Stopping and Digital–Analog Networks. IEEE Journal of Solid-State Circuits. 58(10). 2931–2945. 17 indexed citations
6.
Kim, Sangyeob, Soyeon Kim, Sangjin Kim, et al.. (2023). C-DNN: An Energy-Efficient Complementary Deep-Neural-Network Processor With Heterogeneous CNN/SNN Core Architecture. IEEE Journal of Solid-State Circuits. 59(1). 157–172. 12 indexed citations
7.
Kim, Sangyeob, et al.. (2023). SNPU: An Energy-Efficient Spike Domain Deep-Neural-Network Processor With Two-Step Spike Encoding and Shift-and-Accumulation Unit. IEEE Journal of Solid-State Circuits. 58(10). 2812–2825. 10 indexed citations
8.
Kim, Sangyeob, Soyeon Kim, Sangjin Kim, et al.. (2023). COOL-NPU: Complementary Online Learning Neural Processing Unit with CNN-SNN Heterogeneous Core and Event-driven Backpropagation. 54 4. 1–3. 2 indexed citations
9.
Kim, Ho Soo & Soyeon Kim. (2023). Analysis of Seismic Performance Characteristics for School Buildings on the Bracing Configuration of Steel Frame System Reinforcement. Journal of the Korean Association for Spatial Structures. 23(4). 59–69. 1 indexed citations
10.
11.
Kim, Soyeon, et al.. (2022). Vision Transformer Equipped With Neural Resizer On Facial Expression Recognition Task. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2614–2618. 6 indexed citations
12.
Lee, Yongho, Soyeon Kim, & Hyunchol Shin. (2022). A 24 GHz CMOS Direct-Conversion RF Receiver with I/Q Mismatch Calibration for Radar Sensor Applications. Sensors. 22(21). 8246–8246. 4 indexed citations
13.
Kim, Sangyeob, et al.. (2022). Neuro-CIM: A 310.4 TOPS/W Neuromorphic Computing-in-Memory Processor with Low WL/BL activity and Digital-Analog Mixed-mode Neuron Firing. 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits). 16 indexed citations
14.
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
15.
Kim, Soyeon & Jeong-Ryeol Kim. (2021). Affective Effects of English Digital Textbook Lessons Using AI Chatbots. Korean Association For Learner-Centered Curriculum And Instruction. 21(10). 37–49. 22 indexed citations
16.
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
17.
Kim, Soyeon, et al.. (2020). Heat transfer performance of water-based electrospray cooling. International Communications in Heat and Mass Transfer. 118. 104861–104861. 30 indexed citations
18.
Kim, Soyeon & Sanghyun Park. (2011). Multi-class Classification of Database Workloads using PCA-SVM Classifier. 38(1). 1–8. 1 indexed citations
19.
Kim, Soyeon & Hyesook Lim. (2009). Tuple Pruning Using Bloom Filters for Packet Classification. ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications. 1358–1359. 1 indexed citations
20.
Brookes, Matthew J., et al.. (2005). A new quantitative analysis of significant timing differences between externally cued and self-initiated motor tasks in an fMRI study. Solid State Nuclear Magnetic Resonance. 28(2-4). 258–265. 3 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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