World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
8601
World Ranking
7182
National Ranking
63

Electronics and Electrical Engineering

D-Index
40
Citations
7768
World Ranking
4452
National Ranking
151

Overview

Sungjoo Yoo is affiliated with Seoul National University in South Korea and has made significant contributions to the field of computer science, with a focus on computer vision and artificial intelligence. Their research encompasses a variety of subfields and topics within these disciplines.

The main fields of study covered by their research include:

  • Computer Science

Subfields of study addressed in their work are:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Media Technology
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

The primary topics of their research include:

  • Advanced Vision and Imaging
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Image Processing Techniques and Applications
  • Optical measurement and interference techniques
  • Generative Adversarial Networks and Image Synthesis
  • Advanced Image Processing Techniques

Sungjoo Yoo has published extensively in various academic venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Access
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Lecture notes in computer science

Some of the recent papers authored or co-authored by Sungjoo Yoo are:

  • Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • McDRAM v2: In-Dynamic Random Access Memory Systolic Array Accelerator to Address the Large Model Problem in Deep Neural Networks on the Edge, 2020, IEEE Access
  • Augmenting Few-Shot Learning With Supervised Contrastive Learning, 2021, IEEE Access
  • MFOS: Model-Free & One-Shot Object Pose Estimation, 2024, Proceedings of the AAAI Conference on Artificial Intelligence
  • MetaMix: Meta-State Precision Searcher for Mixed-Precision Activation Quantization, 2024, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent collaborators in their research include:

  • Eunhyeok Park
  • Euntae Choi
  • Hyunyoung Jung
  • Han-Byul Kim
  • Jongmin Lee

Best Publications

  • A scalable processing-in-memory accelerator for parallel graph processing

    Junwhan Ahn;Sungpack Hong;Sungjoo Yoo;Onur Mutlu

  • Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

    Yong-Deok Kim;Eunhyeok Park;Sungjoo Yoo;Taelim Choi

  • PIM-enabled instructions: a low-overhead, locality-aware processing-in-memory architecture

    Junwhan Ahn;Sungjoo Yoo;Onur Mutlu;Kiyoung Choi

  • Machine Learning at Facebook: Understanding Inference at the Edge

    Carole-Jean Wu;David Brooks;Kevin Chen;Douglas Chen

  • Weighted-Entropy-Based Quantization for Deep Neural Networks

    Eunhyeok Park;Junwhan Ahn;Sungjoo Yoo

  • Automatic generation and targeting of application specific operating systems and embedded systems software

    L. Gauthier;Sungjoo Yoo;A.A. Jerraya

  • Component-based design approach for multicore SoCs

    W. Cescirio;A. Baghdadi;L. Gauthier;D. Lyonnard

  • Automatic generation of application-specific architectures for heterogeneous multiprocessor system-on-chip

    Damien Lyonnard;Sungjoo Yoo;Amer Baghdadi;Ahmed A. Jerraya

  • Energy-efficient neural network accelerator based on outlier-aware low-precision computation

    Eunhyeok Park;Dongyoung Kim;Sungjoo Yoo

  • Multiprocessor SoC platforms: a component-based design approach

    W.O. Cesario;D. Lyonnard;G. Nicolescu;Y. Paviot

  • Value-Aware Quantization for Training and Inference of Neural Networks

    Eunhyeok Park;Sungjoo Yoo;Peter Vajda

  • Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

    Jongsoo Park;Maxim Naumov;Protonu Basu;Summer Deng

  • Dual Motion Estimation for Frame Rate Up-Conversion

    Suk-Ju Kang;Sungjoo Yoo;Young Hwan Kim

  • Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

    Hyunsu Kim;Yunjey Choi;Junho Kim;Sungjoo Yoo

  • Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss

    Hyunsu Kim;Ho Young Jhoo;Eunhyeok Park;Sungjoo Yoo

  • Fine-Grained Semantics-Aware Representation Enhancement for Self-Supervised Monocular Depth Estimation

    Hyunyoung Jung;Eunhyeok Park;Sungjoo Yoo

  • ZeNA: Zero-Aware Neural Network Accelerator

    Dongyoung Kim;Junwhan Ahn;Sungjoo Yoo

  • DASCA: Dead Write Prediction Assisted STT-RAM Cache Architecture

    Junwhan Ahn;Sungjoo Yoo;Kiyoung Choi

  • Power management of hybrid DRAM/PRAM-based main memory

    Hyunsun Park;Sungjoo Yoo;Sunggu Lee

  • PowerViP: Soc power estimation framework at transaction level

    Ikhwan Lee;Hyunsuk Kim;Peng Yang;Sungjoo Yoo

  • Big/little deep neural network for ultra low power inference

    Eunhyeok Park;Dongyoung Kim;Soobeom Kim;Yong-Deok Kim

  • Making DRAM Stronger Against Row Hammering

    Mungyu Son;Hyunsun Park;Junwhan Ahn;Sungjoo Yoo

Frequent Co-Authors

Kiyoung Choi
Kiyoung Choi Seoul National University
Chanik Park
Chanik Park Pohang University of Science and Technology
Jung Ho Ahn
Jung Ho Ahn Seoul National University
Yangqing Jia
Yangqing Jia Alibaba Group (China)
Onur Mutlu
Onur Mutlu ETH Zurich
Tae-Lim Choi
Tae-Lim Choi Seoul National University
Kim Hazelwood
Kim Hazelwood Facebook (United States)
Norbert Wehn
Norbert Wehn Technical University of Kaiserslautern
Zhenyu Sun
Zhenyu Sun Beijing University of Chemical Technology

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