Sangyeob Kim

1.8k total citations
88 papers, 1.2k citations indexed

About

Sangyeob Kim is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Sangyeob Kim has authored 88 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Electrical and Electronic Engineering, 33 papers in Computer Vision and Pattern Recognition and 27 papers in Computational Mechanics. Recurrent topics in Sangyeob Kim's work include Advanced Memory and Neural Computing (31 papers), Ferroelectric and Negative Capacitance Devices (22 papers) and Fluid Dynamics Simulations and Interactions (20 papers). Sangyeob Kim is often cited by papers focused on Advanced Memory and Neural Computing (31 papers), Ferroelectric and Negative Capacitance Devices (22 papers) and Fluid Dynamics Simulations and Interactions (20 papers). Sangyeob Kim collaborates with scholars based in South Korea, United States and Switzerland. Sangyeob Kim's co-authors include Hoi‐Jun Yoo, Sanghoon Kang, Jinmook Lee, Changhyeon Kim, Dongjoo Shin, Juhyoung Lee, Sangjin Kim, Yonghwan Kim, Donghyeon Han and Soyeon Kim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Construction and Building Materials and Marine Pollution Bulletin.

In The Last Decade

Sangyeob Kim

81 papers receiving 1.2k citations

Peers

Sangyeob Kim

EEE

CVPR

Donghyeon Han South Korea

Fengbo Ren United States

Guillermo Botella Spain

Yiannis Andreopoulos United Kingdom

Guanghui He China

Haoyu Yang Hong Kong

Peng Ouyang China

Fengbin Tu China

Dongjoo Shin South Korea

Indrajit Chakrabarti India

Donghyeon Han South Korea

Sangyeob Kim

564 ×0.8

EEE

464 ×1.0

CVPR

246 ×0.9

53 ×0.3

132 ×1.0

Citations per year, relative to Sangyeob Kim Sangyeob Kim (= 1×) peers Donghyeon Han

Countries citing papers authored by Sangyeob Kim

Since Specialization

Citations

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

Fields of papers citing papers by Sangyeob Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sangyeob Kim

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

All Works

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20 of 20 papers shown

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.

Kim, Sangjin, et al.. (2025). Dyamond: Compact and Efficient 1T1C DRAM IMC Accelerator With Bit Column Addition for Memory-Intensive AI. IEEE Journal of Solid-State Circuits. 60(4). 1299–1310. 1 indexed citations

Kim, Sangyeob, et al.. (2024). Two-Step Spike Encoding Scheme and Architecture for Highly Sparse Spiking-Neural-Network. 1–5. 1 indexed citations

Cho, Hyun Gug, et al.. (2024). Effect of Refractive Power and Diameter on Weight and Thickness in Spectacle Lenses for Myopia. Journal of Korean Ophthalmic Optics Society. 29(1). 9–17. 2 indexed citations

Im, Dongseok, et al.. (2024). ED-MPIM: An Energy-Efficient Event-Driven Smart Vision SoC With High-Linearity and Reconfigurable MRAM PIM. IEEE Journal of Solid-State Circuits. 60(6). 2226–2238. 1 indexed citations

Li, Zhiyong, Donghyeon Han, Dongseok Im, et al.. (2024). NeuGPU: An Energy-Efficient Neural Graphics Processing Unit for Instant Modeling and Real-Time Rendering on Mobile Devices. IEEE Journal of Solid-State Circuits. 60(1). 99–111. 1 indexed citations

Kim, Sangyeob, et al.. (2024). The Effects of a Gate Bias Condition on 1.2 kV SiC MOSFETs during Irradiating Gamma-Radiation. Micromachines. 15(4). 496–496.

Kim, Sangjin, et al.. (2024). LOG-CIM: An Energy-Efficient Logarithmic Quantization Computing-In-Memory Processor With Exponential Parallel Data Mapping and Zero-Aware 6T Dual-WL Cell. IEEE Journal of Solid-State Circuits. 59(10). 3330–3341. 2 indexed citations

Kim, Sangjin, Zhiyong Li, Juhyoung Lee, et al.. (2023). 16.5 DynaPlasia: An eDRAM In-Memory-Computing-Based Reconfigurable Spatial Accelerator with Triple-Mode Cell for Dynamic Resource Switching. 256–258. 34 indexed citations

10.

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

11.

Kim, Sangyeob, et al.. (2023). 1.2 kV SiC MOSFETs with tapered buffer oxide for the suppression of the electric field crowding effect. Japanese Journal of Applied Physics. 62(11). 114001–114001. 1 indexed citations

12.

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

13.

Lee, Juhyoung, Ji-Hoon Kim, Sangyeob Kim, et al.. (2021). A 13.7 TFLOPS/W Floating-point DNN Processor using Heterogeneous Computing Architecture with Exponent-Computing-in-Memory. 1–2. 35 indexed citations

14.

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

15.

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

16.

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

17.

Kim, Sangyeob, et al.. (2018). Prediction of Extreme Sloshing Pressure Using Different Statistical Models. 4(4). 185–194. 3 indexed citations

18.

Kim, Sangyeob, et al.. (2017). Experimental Study of Slosh-Induced Loads on LNG Fuel Tank of Container Ship. The 27th International Ocean and Polar Engineering Conference. 3 indexed citations

19.

Kim, Sangyeob, et al.. (2013). Experimental Studies on Sloshing in a STX Independence Type-B Tank. The Twenty-third International Offshore and Polar Engineering Conference. 6 indexed citations

20.

Kim, Yonghwan, et al.. (2013). Model-Scale Sloshing Tests for an Anti-Sloshing Blanket System. International Journal of Offshore and Polar Engineering. 23(4). 254–262. 10 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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