Hyunjoon Kim

899 total citations
23 papers, 595 citations indexed

About

Hyunjoon Kim is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Hardware and Architecture. According to data from OpenAlex, Hyunjoon Kim has authored 23 papers receiving a total of 595 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Electrical and Electronic Engineering, 7 papers in Artificial Intelligence and 3 papers in Hardware and Architecture. Recurrent topics in Hyunjoon Kim's work include Advanced Memory and Neural Computing (18 papers), Ferroelectric and Negative Capacitance Devices (14 papers) and CCD and CMOS Imaging Sensors (6 papers). Hyunjoon Kim is often cited by papers focused on Advanced Memory and Neural Computing (18 papers), Ferroelectric and Negative Capacitance Devices (14 papers) and CCD and CMOS Imaging Sensors (6 papers). Hyunjoon Kim collaborates with scholars based in Singapore, United States and Italy. Hyunjoon Kim's co-authors include Bongjin Kim, Tony Tae-Hyoung Kim, Taegeun Yoo, Geonmo Gu, Kunsoo Park, Wook-Shin Han, Qian Chen, Chengshuo Yu, Vishal Sharma and Lu Lu and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, IEEE Transactions on Circuits and Systems I Regular Papers and IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

In The Last Decade

Hyunjoon Kim

21 papers receiving 587 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hyunjoon Kim Singapore 15 407 221 134 115 90 23 595
Hongyang Jia China 11 668 1.6× 218 1.0× 99 0.7× 157 1.4× 91 1.0× 36 826
Onur Günlü Germany 11 216 0.5× 199 0.9× 76 0.6× 72 0.6× 115 1.3× 45 436
Mahdi Nazm Bojnordi United States 12 364 0.9× 118 0.5× 85 0.6× 199 1.7× 169 1.9× 42 529
Sujan K. Gonugondla United States 13 640 1.6× 165 0.7× 103 0.8× 140 1.2× 72 0.8× 25 737
Aaron Stillmaker United States 6 219 0.5× 102 0.5× 85 0.6× 131 1.1× 143 1.6× 11 406
Kwen‐Siong Chong Singapore 14 416 1.0× 204 0.9× 100 0.7× 254 2.2× 94 1.0× 90 665
En-Yu Yang United States 8 494 1.2× 151 0.7× 131 1.0× 91 0.8× 58 0.6× 15 620
Amr T. Abdel-Hamid Egypt 11 161 0.4× 116 0.5× 113 0.8× 240 2.1× 78 0.9× 29 489
Weize Yu United States 15 206 0.5× 316 1.4× 158 1.2× 351 3.1× 68 0.8× 48 517

Countries citing papers authored by Hyunjoon Kim

Since Specialization
Citations

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

Fields of papers citing papers by Hyunjoon Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hyunjoon Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Hyunjoon Kim. A scholar is included among the top collaborators of Hyunjoon 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 Hyunjoon Kim. Hyunjoon 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.
Mele, Filippo, et al.. (2024). The SparkPix-S ASIC for the sparsified readout of 1 MHz frame-rate X-ray cameras at LCLS-II: pixel design and simulation results. Journal of Instrumentation. 19(1). C01010–C01010. 3 indexed citations
2.
Kim, Hyunjoon, Bojan Marković, Guosheng Liu, et al.. (2024). A Single-Ended SAR ADC with Low-Energy Differential Switching for X-Ray Imagers. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–4.
3.
4.
Kim, Hyunjoon, et al.. (2023). A 1-16b Reconfigurable 80Kb 7T SRAM-Based Digital Near-Memory Computing Macro for Processing Neural Networks. IEEE Transactions on Circuits and Systems I Regular Papers. 70(4). 1580–1590. 21 indexed citations
5.
Kim, Hyunjoon, et al.. (2023). BP-SCIM: A Reconfigurable 8T SRAM Macro for Bit-Parallel Searching and Computing In-Memory. IEEE Transactions on Circuits and Systems I Regular Papers. 70(5). 2016–2027. 17 indexed citations
6.
Sharma, Vishal, et al.. (2022). A Reconfigurable 16Kb AND8T SRAM Macro With Improved Linearity for Multibit Compute-In Memory of Artificial Intelligence Edge Devices. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 12(2). 522–535. 19 indexed citations
7.
Kim, Hyunjoon, et al.. (2022). SRAM-Based In-Memory Computing Macro Featuring Voltage-Mode Accumulator and Row-by-Row ADC for Processing Neural Networks. IEEE Transactions on Circuits and Systems I Regular Papers. 69(6). 2412–2422. 25 indexed citations
8.
Sharma, Vishal, Hyunjoon Kim, & Tony Tae-Hyoung Kim. (2022). A 64 Kb Reconfigurable Full-Precision Digital ReRAM-Based Compute-In-Memory for Artificial Intelligence Applications. IEEE Transactions on Circuits and Systems I Regular Papers. 69(8). 3284–3296. 14 indexed citations
9.
Kim, Hyunjoon, et al.. (2022). A Scalable CMOS Ising Computer Featuring Sparse and Reconfigurable Spin Interconnects for Solving Combinatorial Optimization Problems. IEEE Journal of Solid-State Circuits. 57(3). 858–868. 23 indexed citations
10.
Kim, Hyunjoon, et al.. (2022). CIM-Spin: A Scalable CMOS Annealing Processor With Digital In-Memory Spin Operators and Register Spins for Combinatorial Optimization Problems. IEEE Journal of Solid-State Circuits. 57(7). 2263–2273. 20 indexed citations
11.
Kim, Hyunjoon, et al.. (2022). A Reconfigurable 8T SRAM Macro for Bit-Parallel Searching and Computing In-Memory. 2022 IEEE International Symposium on Circuits and Systems (ISCAS). 2556–2560. 2 indexed citations
12.
Kim, Hyunjoon, Taegeun Yoo, Tony Tae-Hyoung Kim, & Bongjin Kim. (2021). Colonnade: A Reconfigurable SRAM-Based Digital Bit-Serial Compute-In-Memory Macro for Processing Neural Networks. IEEE Journal of Solid-State Circuits. 56(7). 2221–2233. 116 indexed citations
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15.
Yu, Chengshuo, et al.. (2020). A Logic-Compatible eDRAM Compute-In-Memory With Embedded ADCs for Processing Neural Networks. IEEE Transactions on Circuits and Systems I Regular Papers. 68(2). 667–679. 59 indexed citations
16.
Kim, Hyunjoon, Qian Chen, Taegeun Yoo, Tony Tae-Hyoung Kim, & Bongjin Kim. (2019). A 1-16b Precision Reconfigurable Digital In-Memory Computing Macro Featuring Column-MAC Architecture and Bit-Serial Computation. 345–348. 45 indexed citations
17.
Hsu, Tzu-Hsiang, Yen-Kai Chen, Wei-Chen Wei, et al.. (2019). A 0.5V Real-Time Computational CMOS Image Sensor with Programmable Kernel for Always-On Feature Extraction. 33–34. 17 indexed citations
18.
Kim, Hyunjoon, Qian Chen, & Bongjin Kim. (2019). A 16K SRAM-Based Mixed-Signal In-Memory Computing Macro Featuring Voltage-Mode Accumulator and Row-by-Row ADC. 35–36. 33 indexed citations
19.
Yoo, Taegeun, Hyunjoon Kim, Qian Chen, Tony Tae-Hyoung Kim, & Bongjin Kim. (2019). A Logic Compatible 4T Dual Embedded DRAM Array for In-Memory Computation of Deep Neural Networks. 1–6. 19 indexed citations
20.
Kim, Hyunjoon, et al.. (2006). Virtual image compositor of View Point Tracking System for Outdoor Augmented Reality. ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications. 593–596. 1 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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