Jongse Park

2.3k total citations · 2 hit papers
43 papers, 1.5k citations indexed

About

Jongse Park is a scholar working on Hardware and Architecture, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jongse Park has authored 43 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Hardware and Architecture, 20 papers in Electrical and Electronic Engineering and 14 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jongse Park's work include Parallel Computing and Optimization Techniques (19 papers), Advanced Memory and Neural Computing (10 papers) and Low-power high-performance VLSI design (10 papers). Jongse Park is often cited by papers focused on Parallel Computing and Optimization Techniques (19 papers), Advanced Memory and Neural Computing (10 papers) and Low-power high-performance VLSI design (10 papers). Jongse Park collaborates with scholars based in United States, South Korea and United Kingdom. Jongse Park's co-authors include Hadi Esmaeilzadeh, Hardik Sharma, Joon Kyung Kim, Divya Mahajan, Amir Yazdanbakhsh, Emmanuel Amaro, Bradley Thwaites, Naveen Suda, Vikas Chandra and Liangzhen Lai and has published in prestigious journals such as IEEE Transactions on Computers, Proceedings of the VLDB Endowment and ACM SIGPLAN Notices.

In The Last Decade

Jongse Park

41 papers receiving 1.5k citations

Hit Papers

Bit Fusion: Bit-Level Dynamically Composable Architecture... 2016 2026 2019 2022 2018 2016 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network
    2018 344 citations Hardik Sharma, Jongse Park et al. profile →
  2. From high-level deep neural models to FPGAs
    2016 273 citations Hardik Sharma, Jongse Park et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jongse Park United States 18 835 581 539 516 351 43 1.5k
Eriko Nurvitadhi United States 20 695 0.8× 677 1.2× 698 1.3× 492 1.0× 486 1.4× 75 1.7k
Ninghui Sun China 15 711 0.9× 804 1.4× 665 1.2× 485 0.9× 528 1.5× 63 1.7k
Jaewoong Sim United States 18 631 0.8× 532 0.9× 802 1.5× 378 0.7× 655 1.9× 33 1.6k
Hyoukjun Kwon United States 16 829 1.0× 703 1.2× 576 1.1× 414 0.8× 339 1.0× 38 1.6k
Yakun Sophia Shao United States 19 1.1k 1.3× 587 1.0× 1.2k 2.2× 401 0.8× 705 2.0× 50 2.1k
Anurag Mukkara United States 8 680 0.8× 844 1.5× 533 1.0× 512 1.0× 301 0.9× 9 1.4k
Xiaobing Feng China 16 727 0.9× 778 1.3× 562 1.0× 510 1.0× 478 1.4× 100 1.7k
Ganesh Venkatesh United States 13 664 0.8× 422 0.7× 834 1.5× 290 0.6× 645 1.8× 24 1.5k
Ardavan Pedram United States 14 750 0.9× 919 1.6× 623 1.2× 636 1.2× 418 1.2× 28 1.8k
Amir Yazdanbakhsh United States 18 725 0.9× 323 0.6× 587 1.1× 314 0.6× 304 0.9× 51 1.2k

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-authorship network of co-authors of Jongse Park

This figure shows the co-authorship network connecting the top 25 collaborators of Jongse Park. A scholar is included among the top collaborators of Jongse Park 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 Jongse Park. Jongse Park 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.
Kim, D., et al.. (2025). MixDiT: Accelerating Image Diffusion Transformer Inference With Mixed-Precision MX Quantization. IEEE Computer Architecture Letters. 24(1). 141–144.
2.
Kim, Joo-Young, et al.. (2025). Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization. ArXiv.org. 482–497. 1 indexed citations
3.
4.
Kim, Jung‐Hoon, Junsoo Kim, Seongmin Hong, et al.. (2024). A Latency Processing Unit: A Latency-Optimized and Highly Scalable Processor for Large Language Model Inference. IEEE Micro. 44(6). 17–33. 4 indexed citations
5.
Park, Jongse, et al.. (2024). ONNXim: A Fast, Cycle-Level Multi-Core NPU Simulator. IEEE Computer Architecture Letters. 23(2). 219–222. 6 indexed citations
6.
Xu, Hanyang, et al.. (2024). Tandem Processor: Grappling with Emerging Operators in Neural Networks. 1165–1182. 10 indexed citations
7.
Oh, Changhun, et al.. (2024). DACAPO: Accelerating Continuous Learning in Autonomous Systems for Video Analytics. 1246–1261. 5 indexed citations
8.
Park, Jongse, et al.. (2024). Cerberus: Triple Mode Acceleration of Sparse Matrix and Vector Multiplication. ACM Transactions on Architecture and Code Optimization. 21(2). 1–24. 1 indexed citations
9.
Kim, Yeonjae, et al.. (2024). Interference-Aware DNN Serving on Heterogeneous Processors in Edge Systems. 199–206. 1 indexed citations
10.
Kim, Minsu, et al.. (2024). LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale. 15–29. 3 indexed citations
11.
Kim, Taehoon, et al.. (2023). Hardware-hardened Sandbox Enclaves for Trusted Serverless Computing. ACM Transactions on Architecture and Code Optimization. 21(1). 1–25. 5 indexed citations
12.
Koo, Jahyun, et al.. (2023). FlexBlock: A Flexible DNN Training Accelerator With Multi-Mode Block Floating Point Support. IEEE Transactions on Computers. 72(9). 2522–2535. 15 indexed citations
13.
Kim, Joon Kyung, Shuting Wang, Babak Mahmoudi, et al.. (2022). Yin-Yang: Programming Abstractions for Cross-Domain Multi-Acceleration. IEEE Micro. 42(5). 89–98. 2 indexed citations
14.
Sharma, Hardik, Jongse Park, Naveen Suda, et al.. (2018). Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network. 764–775. 344 indexed citations breakdown →
15.
Park, Jongse, et al.. (2017). Scale-out acceleration for machine learning. 367–381. 28 indexed citations
16.
Park, Jongse, Emmanuel Amaro, Divya Mahajan, Bradley Thwaites, & Hadi Esmaeilzadeh. (2016). AxGames. ACM SIGPLAN Notices. 51(4). 623–636. 2 indexed citations
17.
Yazdanbakhsh, Amir, Divya Mahajan, Bradley Thwaites, et al.. (2015). Axilog: language support for approximate hardware design. Design, Automation, and Test in Europe. 812–817. 32 indexed citations
18.
Yazdanbakhsh, Amir, Jongse Park, Hardik Sharma, Pejman Lotfi-Kamran, & Hadi Esmaeilzadeh. (2015). Neural acceleration for GPU throughput processors. 482–493. 75 indexed citations
19.
Mahajan, Divya, et al.. (2015). Axilog: Abstractions for Approximate Hardware Design and Reuse. IEEE Micro. 1–1. 2 indexed citations
20.
Mahajan, Divya, Amir Yazdanbakhsh, Jongse Park, et al.. (2015). Axilog: Abstractions for Approximate Hardware Design and Reuse. IEEE Micro. 35(5). 16–30. 11 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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2026