Youngeun Kwon

497 total citations
8 papers, 309 citations indexed

About

Youngeun Kwon is a scholar working on Hardware and Architecture, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Youngeun Kwon has authored 8 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Hardware and Architecture, 5 papers in Electrical and Electronic Engineering and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Youngeun Kwon's work include Parallel Computing and Optimization Techniques (5 papers), Ferroelectric and Negative Capacitance Devices (4 papers) and Advanced Neural Network Applications (3 papers). Youngeun Kwon is often cited by papers focused on Parallel Computing and Optimization Techniques (5 papers), Ferroelectric and Negative Capacitance Devices (4 papers) and Advanced Neural Network Applications (3 papers). Youngeun Kwon collaborates with scholars based in South Korea and Canada. Youngeun Kwon's co-authors include Minsoo Rhu, Y. Lee and John Kim and has published in prestigious journals such as IEEE Micro and IEEE Computer Architecture Letters.

In The Last Decade

Youngeun Kwon

8 papers receiving 296 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Youngeun Kwon South Korea	8	132	119	115	99	96	8	309
Mohammad Alian United States	12	238 1.8×	76 0.6×	122 1.1×	83 0.8×	175 1.8×	29	373
Xinfeng Xie United States	9	123 0.9×	121 1.0×	112 1.0×	40 0.4×	158 1.6×	15	306
Pengcheng Yao China	11	86 0.7×	110 0.9×	74 0.6×	57 0.6×	97 1.0×	25	273
Hyojong Kim United States	10	235 1.8×	86 0.7×	157 1.4×	83 0.8×	273 2.8×	15	408
Joonho Song South Korea	7	88 0.7×	65 0.5×	179 1.6×	21 0.2×	106 1.1×	16	311
Hyunsung Shin South Korea	6	143 1.1×	42 0.4×	188 1.6×	33 0.3×	178 1.9×	6	332
Bairen Yi Hong Kong	6	228 1.7×	159 1.3×	63 0.5×	179 1.8×	68 0.7×	6	397
Ivan Fernandez Spain	9	131 1.0×	69 0.6×	127 1.1×	33 0.3×	147 1.5×	21	308
Mark Gottscho United States	10	143 1.1×	65 0.5×	208 1.8×	39 0.4×	188 2.0×	19	365

Countries citing papers authored by Youngeun Kwon

Since Specialization

Citations

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

Fields of papers citing papers by Youngeun Kwon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Youngeun Kwon

This figure shows the co-authorship network connecting the top 25 collaborators of Youngeun Kwon. A scholar is included among the top collaborators of Youngeun Kwon 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 Youngeun Kwon. Youngeun Kwon 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:

8 of 8 papers shown

1.

Kwon, Youngeun & Minsoo Rhu. (2022). Training personalized recommendation systems from (GPU) scratch. 860–873. 15 indexed citations

2.

Kwon, Youngeun, et al.. (2021). Understanding the Implication of Non-Volatile Memory for Large-Scale Graph Neural Network Training. IEEE Computer Architecture Letters. 20(2). 118–121. 7 indexed citations

3.

Kwon, Youngeun, et al.. (2020). NeuMMU. 1109–1124. 18 indexed citations

4.

Kwon, Youngeun, et al.. (2020). Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations. 968–981. 67 indexed citations

5.

Kwon, Youngeun, Y. Lee, & Minsoo Rhu. (2019). TensorDIMM. 740–753. 146 indexed citations

6.

Kwon, Youngeun & Minsoo Rhu. (2019). A Disaggregated Memory System for Deep Learning. IEEE Micro. 39(5). 82–90. 12 indexed citations

7.

Kwon, Youngeun & Minsoo Rhu. (2018). Beyond the Memory Wall: A Case for Memory-Centric HPC System for Deep Learning. 148–161. 31 indexed citations

8.

Kwon, Youngeun & Minsoo Rhu. (2018). A Case for Memory-Centric HPC System Architecture for Training Deep Neural Networks. IEEE Computer Architecture Letters. 17(2). 134–138. 13 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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