World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
8254
World Ranking
9616
National Ranking
4076

Overview

Hyesoon Kim is affiliated with the Georgia Institute of Technology in the United States. Their research spans across primary and subfields within computer science, focusing notably on computer networks and communications, computer vision and pattern recognition, hardware and architecture, information systems, and artificial intelligence.

The scientist's work covers a diverse range of topics including parallel computing and optimization techniques, advanced data storage technologies, cloud computing and resource management, advanced neural network applications, distributed and parallel computing systems, advanced image and video retrieval techniques, and embedded systems design techniques.

Frequent collaborators in their research include Ramyad Hadidi, Jiashen Cao, Bahar Asgari, Joy Arulraj, and Ruobing Han.

Published research appears in various venues, with multiple works featured in arXiv (Cornell University), IEEE Computer Architecture Letters, Zenodo (CERN European Organization for Nuclear Research), Proceedings of the VLDB Endowment, and IEEE Micro.

Significant papers authored or co-authored by Hyesoon Kim include:

  • "Toward Collaborative Inferencing of Deep Neural Networks on Internet-of-Things Devices," 2020, IEEE Internet of Things Journal
  • "Traversing large graphs on GPUs with unified memory," 2020, Proceedings of the VLDB Endowment
  • "FiGO: Fine-Grained Query Optimization in Video Analytics," 2022, Proceedings of the 2022 International Conference on Management of Data
  • "GPU Database Systems Characterization and Optimization," 2023, Proceedings of the VLDB Endowment
  • "Mitigating Timing-Based NoC Side-Channel Attacks With LLC Remapping," 2023, IEEE Computer Architecture Letters

Best Publications

  • An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

    Sunpyo Hong;Hyesoon Kim

  • Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping

    Chi-Keung Luk;Sunpyo Hong;Hyesoon Kim

  • An integrated GPU power and performance model

    Sunpyo Hong;Hyesoon Kim

  • Inferring fine-grained control flow inside SGX enclaves with branch shadowing

    Sangho Lee;Ming-Wei Shih;Prasun Gera;Taesoo Kim

  • Feedback Directed Prefetching: Improving the Performance and Bandwidth-Efficiency of Hardware Prefetchers

    S. Srinath;O. Mutlu;Hyesoon Kim;Y.N. Patt

  • GraphPIM: Enabling Instruction-Level PIM Offloading in Graph Computing Frameworks

    Lifeng Nai;Ramyad Hadidi;Jaewoong Sim;Hyojong Kim

  • A performance analysis framework for identifying potential benefits in GPGPU applications

    Jaewoong Sim;Aniruddha Dasgupta;Hyesoon Kim;Richard Vuduc

  • When Prefetching Works, When It Doesn’t, and Why

    Jaekyu Lee;Hyesoon Kim;Richard Vuduc

  • GraphBIG: understanding graph computing in the context of industrial solutions

    Lifeng Nai;Yinglong Xia;Ilie G. Tanase;Hyesoon Kim

  • Many-Thread Aware Prefetching Mechanisms for GPGPU Applications

    Jae-Kyu Lee;Nagesh B. Lakshminarayana;Hyesoon Kim;Richard W. Vuduc

  • TAP: A TLP-aware cache management policy for a CPU-GPU heterogeneous architecture

    Jaekyu Lee;Hyesoon Kim

  • Transparent Hardware Management of Stacked DRAM as Part of Memory

    Jaewoong Sim;Alaa R. Alameldeen;Zeshan Chishti;Chris Wilkerson

  • SD3: A Scalable Approach to Dynamic Data-Dependence Profiling

    Minjang Kim;Hyesoon Kim;Chi-Keung Luk

  • A Mostly-Clean DRAM Cache for Effective Hit Speculation and Self-Balancing Dispatch

    Jaewoong Sim;Gabriel H. Loh;Hyesoon Kim;Mike O'Connor

  • Hierarchical Control System Synthesis for Rotorcraft-based Unmanned Aerial Vehicles

    David Hyunchul Shim;H.J. Kim;S. Sastry

  • Techniques for Efficient Processing in Runahead Execution Engines

    Onur Mutlu;Hyesoon Kim;Yale N. Patt

  • Age based scheduling for asymmetric multiprocessors

    Nagesh B. Lakshminarayana;Jaekyu Lee;Hyesoon Kim

  • Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices

    Ramyad Hadidi;Jiashen Cao;Yilun Xie;Bahar Asgari

  • Toward Collaborative Inferencing of Deep Neural Networks on Internet-of-Things Devices

    Ramyad Hadidi;Jiashen Cao;Michael S. Ryoo;Hyesoon Kim

  • 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture

    Jaewoong Sim;Alaa R. Alameldeen;Zeshan Chishti;Chris Wilkerson

  • CAIRO: A Compiler-Assisted Technique for Enabling Instruction-Level Offloading of Processing-In-Memory

    Ramyad Hadidi;Lifeng Nai;Hyojong Kim;Hyesoon Kim

  • Decentralized Nonlinear Model Predictive Control of Multiple Flying Robots in Dynamic Environments

    David Hyunchul Shim;H.J. Kim;S. Sastry

Frequent Co-Authors

Onur Mutlu
Onur Mutlu ETH Zurich
Yale N. Patt
Yale N. Patt The University of Texas at Austin
Sudhakar Yalamanchili
Sudhakar Yalamanchili Georgia Institute of Technology
Michael S. Ryoo
Michael S. Ryoo Stony Brook University
Tushar Krishna
Tushar Krishna Georgia Institute of Technology
Richard Vuduc
Richard Vuduc Georgia Institute of Technology
Sung Kyu Lim
Sung Kyu Lim Georgia Institute of Technology
Gabriel H. Loh
Gabriel H. Loh Advanced Micro Devices (United States)
Saibal Mukhopadhyay
Saibal Mukhopadhyay Georgia Institute of Technology
David Hyunchul Shim
David Hyunchul Shim Korea Advanced Institute of Science and Technology

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