Jongse Park

42 papers receiving 1.7k citations

Jongse Park's Hit Papers

Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network 2018 · 395 citations
3950+3+6Years since publication100200300

Peers

Jongse Park
Comparison fields: 5 of 61
  • Hardware and Architecture 614
  • Computational Mathematics 22
  • Computer Vision and Pattern Recognition 657
  • Artificial Intelligence 589
  • Computer Networks and Communications 395
Replace Eriko Nurvitadhi with:
Eriko Nurvitadhi United States
Amir Yazdanbakhsh United States
Ninghui Sun China
Cody Hao Yu United States
Swagath Venkataramani United States
Peter A. Milder United States
Kalin Ovtcharov Canada
Shengen Yan China
Xiaobing Feng China
Brandon Reagen United States
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Citations per field
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Citations per year

Countries citing papers authored by Jongse Park

Since Specialization
Citations

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

Fields of papers citing papers by Jongse Park

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jongse Park, 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 Jongse Park Line = papers co-authored together Jongse Park links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network
Hit paper breakdown →
2018395
2
From high-level deep neural models to FPGAs
Hit paper breakdown →
2016309
3 2016127
4 2014122
5 201587
6 201869
7 201460
8 201558
9 202457
10 201251
11 201537
12 202136
13 201536
14 201633
15 201632
16 201431
17 201729
18 202126
19 202225
20 202322

About Jongse Park

Jongse Park is a scholar working on Hardware and Architecture, Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering, having authored 44 papers that have together received 1.7k indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (18 papers), Advanced Memory and Neural Computing (10 papers), Low-power high-performance VLSI design (9 papers), Advanced Neural Network Applications (7 papers), Ferroelectric and Negative Capacitance Devices (6 papers), Advanced Data Storage Technologies (5 papers), Embedded Systems Design Techniques (5 papers) and Security and Verification in Computing (5 papers). The work is most often cited by research in Hardware and Architecture (614 citations), Computational Mathematics (22 citations), Computer Vision and Pattern Recognition (657 citations), Artificial Intelligence (589 citations) and Computer Networks and Communications (395 citations). Jongse Park has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Hadi Esmaeilzadeh, Hardik Sharma, Joon Kyung Kim, Divya Mahajan, Amir Yazdanbakhsh, Emmanuel Amaro, Bradley Thwaites, Naveen Suda, Liangzhen Lai and Vikas Chandra. Their work appears in journals such as IEEE Micro, IEEE Computer Architecture Letters, Proceedings of the VLDB Endowment, ACM SIGPLAN Notices and IEEE Transactions on Computers.

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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