Mingu Kang

2.2k citations

72 papers · 1.3k · h-index 19

Impact in

Hardware and Architecture top 5%
- Parallel Computing and Optimization Techniques
Electrical and Electronic Engineering top 5%
- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- Semiconductor materials and devices
- Low-power high-performance VLSI design
- CCD and CMOS Imaging Sensors

Papers in

Electrical and Electronic Engineering 38
- Advanced Memory and Neural Computing 26
- Ferroelectric and Negative Capacitance Devices 18
- Low-power high-performance VLSI design 11
- Semiconductor materials and devices 6
Artificial Intelligence 17
- Neural Networks and Reservoir Computing 5

Co-authors: Naresh R. Shanbhag (19 shared papers)Sujan K. Gonugondla (12 shared papers)Ameya D. Patil (3 shared papers)Min-Sun Keel (3 shared papers)Sungmin Lim (3 shared papers)Sean Eilert (2 shared papers)Youn Tae Kim (8 shared papers)Dong‐Geun Yoo (2 shared papers)
Journals: IEEE Journal of Solid-State Circuits (6 papers)IEEE Journal on Emerging and Selected Topics in Circuits and Systems (2 papers)Electronics (2 papers)IEEE Access (2 papers)Journal of Clinical Oncology (2 papers)
Partner nations: United States South Korea Japan

In The Last Decade

Mingu Kang

59 papers receiving 1.2k citations

Peers

Countries citing papers authored by Mingu Kang

Since Specialization

Citations

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

Fields of papers citing papers by Mingu Kang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Mingu Kang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mingu Kang Line = papers co-authored together Mingu Kang links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	A Multi-Functional In-Memory Inference Processor Using a Standard 6T SRAM Array IEEE Journal of Solid-State Circuits ·Mingu Kang, Sujan K. Gonugondla, Ameya D. Patil, Naresh R. Shanbhag	2018	160
2	A 42pJ/decision 3.12TOPS/W robust in-memory machine learning classifier with on-chip training Sujan K. Gonugondla, Mingu Kang, Naresh R. Shanbhag	2018	158
3	An energy-efficient VLSI architecture for pattern recognition via deep embedding of computation in SRAM Mingu Kang, Min-Sun Keel, Naresh R. Shanbhag, Sean Eilert, Ken Curewitz	2014	128
4	A Variation-Tolerant In-Memory Machine Learning Classifier via On-Chip Training IEEE Journal of Solid-State Circuits ·Sujan K. Gonugondla, Mingu Kang, Naresh R. Shanbhag	2018	88
5	Benchmarking Self-Supervised Learning on Diverse Pathology Datasets Mingu Kang, Heon Song, Seonwook Park, Dong‐Geun Yoo, Sérgio Pereira	2023	74
6	A 19.4-nJ/Decision, 364-K Decisions/s, In-Memory Random Forest Multi-Class Inference Accelerator IEEE Journal of Solid-State Circuits ·Mingu Kang, Sujan K. Gonugondla, Sungmin Lim, Naresh R. Shanbhag	2018	45
7	An In-Memory VLSI Architecture for Convolutional Neural Networks IEEE Journal on Emerging and Selected Topics in Circuits and Systems ·Mingu Kang, Sungmin Lim, Sujan K. Gonugondla, Naresh R. Shanbhag	2018	42
8	Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals Journal of Healthcare Engineering ·Mingu Kang, Siho Shin, Jaehyo Jung, Youn Tae Kim	2021	41
9	PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine-Learning Algorithms Prakalp Srivastava, Mingu Kang, Sujan K. Gonugondla, Sungmin Lim, Jungwook Choi, Vikram Adve, Nam Sung Kim, Naresh R. Shanbhag	2018	40
10	An MRAM-Based Deep In-Memory Architecture for Deep Neural Networks Ameya D. Patil, Haocheng Hua, Sujan K. Gonugondla, Mingu Kang, Naresh R. Shanbhag	2019	39
11	Deep In-Memory Architectures in SRAM: An Analog Approach to Approximate Computing Proceedings of the IEEE ·Mingu Kang, Sujan K. Gonugondla, Naresh R. Shanbhag	2020	32
12	Accelerating attention through gradient-based learned runtime pruning Zheng Li, Soroush Ghodrati, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Mingu Kang	2022	31
13	FinFET SRAM Optimization With Fin Thickness and Surface Orientation IEEE Transactions on Electron Devices ·Mingu Kang, S. C. Song, Seonghoon Woo, H. K. Park, Mohamed H. Abu-Rahma, Lixin Ge, B. M. Han, Jiannong Wang, Geoffrey Yeap, Seong‐Ook Jung	2010	29
14	Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation Amir Yazdanbakhsh, Ashkan Moradifirouzabadi, Zheng Li, Mingu Kang	2022	27
15	A 19.4 nJ/decision 364K decisions/s in-memory random forest classifier in 6T SRAM array Mingu Kang, Sujan K. Gonugondla, Naresh R. Shanbhag	2017	25
16	Energy-efficient and high throughput sparse distributed memory architecture Mingu Kang, Eric P. Kim, Min-Sun Keel, Naresh R. Shanbhag	2015	23
17	An energy-efficient memory-based high-throughput VLSI architecture for convolutional networks Mingu Kang, Sujan K. Gonugondla, Min-Sun Keel, Naresh R. Shanbhag	2015	21
18	Ultrathin All‐Solid‐State MoS ₂ ‐Based Electrolyte Gated Synaptic Transistor with Tunable Organic–Inorganic Hybrid Film Advanced Science ·Jungyeop Oh, Seohak Park, (unknown), Sung-Kyu Kim, Hyeonji Lee, Changhyeon Lee, Woonggi Hong, Jun‐Hwe Cha, Mingu Kang, Jun Hyup Jin, Sung Gap Im, Min Ju Kim, Sung‐Yool Choi	2024	19
19	In-Memory Computing Architectures for Sparse Distributed Memory IEEE Transactions on Biomedical Circuits and Systems ·Mingu Kang, Naresh R. Shanbhag	2016	18
20	Real-time pump scheduling for water transmission systems: Case study KSCE Journal of Civil Engineering ·Donghwi Jung, Doosun Kang, Mingu Kang, Byung-Seop Kim	2015	17

About Mingu Kang

Mingu Kang is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Hardware and Architecture, Computer Vision and Pattern Recognition and Biomedical Engineering, having authored 72 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (26 papers), Ferroelectric and Negative Capacitance Devices (18 papers), Low-power high-performance VLSI design (11 papers), Advanced Neural Network Applications (7 papers), Parallel Computing and Optimization Techniques (7 papers), Semiconductor materials and devices (6 papers), Non-Invasive Vital Sign Monitoring (5 papers) and Neural Networks and Reservoir Computing (5 papers). The work is most often cited by research in Hardware and Architecture (217 citations), Electrical and Electronic Engineering (912 citations), Artificial Intelligence (297 citations), Computer Vision and Pattern Recognition (162 citations) and Computational Mathematics (4 citations). Mingu Kang has collaborated with scholars based in United States, South Korea and Japan. Frequent co-authors include Naresh R. Shanbhag, Sujan K. Gonugondla, Ameya D. Patil, Min-Sun Keel, Sungmin Lim, Sean Eilert, Youn Tae Kim, Dong‐Geun Yoo, Sérgio Pereira and Seonwook Park. Their work appears in journals such as IEEE Journal of Solid-State Circuits, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Electronics, IEEE Access and Journal of Clinical Oncology.

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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