Youngeun Kwon

I earned my Ph.D. in electrical engineering at KAIST under the guidance of Professor Minsoo Rhu. My primary research interests are computer systems/architecture for deep learning and emerging applications.

2018.09 ~ 2024.02

Ph.D. Degree in School of Electrical Engineering, KAIST

• Thesis: Hardware and Software Systems for Accelerating Large-Scale Deep Learning Recommendation Models

• Vertically Integrated Architecture Research Group (VIA)

• Adviser: Prof. Minsoo Rhu

2013.03 ~ 2017.08

Bachelor's Degree in Department of Computer Science and Engineering, POSTECH

• Graduation Research: Unsupervised Object Detection

2021.05

Honorable Mention in IEEE Micro Top Picks 2020

2021.02

The 27th Samsung Humantech Paper Award

• Gold Prize (1st place in the Computer Science and Engineering track)

2020.05

Honorable Mention in IEEE Micro Top Picks 2019

2020 Fall

KAIST Breakthroughs, Fall 2020 Vol. 14

2019.03 ~ 2024.02

Global Ph.D. Fellowship, National Research Foundation of Korea (NRF)

• • Research Topic: Memory-centric Architecture for Deep Learning Acceleration

  • Acceptance Rate: 15.2% (216 among 1,423)

2017.07

Best presentation award (1st place) in the graduation research project at POSTECH CSE department

• • Research Topic: Unsupervised Object Detection

2013.03 ~ 2016.12

Scholar of the National Academic Excellence Scholarship for Science and Engineering, Korea Student Aid Foundation (KOSAF)

2021-Spring

Teaching Assistant, EE595: Special Topics in Electrical and Computer Engineering <Parallel Computer Architecture>  @KAIST 

2021-Fall, 2020-Fall, 2020-Spring

Teaching Assistant, EE312: Introduction to Computer Architecture  @KAIST 

2022-Spring, 2019-Fall

Teaching Assistant, EE209: Programming Structure for Electrical Engineering  @KAIST 

2018-Spring

Teaching Assistant, CSED311: Computer Architecture @POSTECH 

• Juntaek Lim, Youngeun Kwon, Ranggi Hwang, Kiwan Maeng, Edward Suh, and Minsoo Rhu, "LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models," The 29th ACM International Conference on Architectural Support for  Programming Languages and Operating Systems (ASPLOS-29), San Diego, CA, April 2024

  • [Paper]

• Youngeun Kwon and Minsoo Rhu, "Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards," The 49th IEEE/ACM International Symposium on Computer Architecture (ISCA-49), New York, NY, June 2022

  • Acceptance Rate: 16% (67 among 400)

  • [Paper][Slide][Talk]

• Yunjae Lee, Youngeun Kwon, and Minsoo Rhu, "Understanding the Implication of Non-Volatile Memory for Large-Scale Graph Neural Network Training", IEEE Computer Architecture Letters (CAL), Jul. 2021

  • [Paper]

• Youngeun Kwon, Yunjae Lee, and Minsoo Rhu, "Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training," The 27th IEEE International Symposium on High-Performance Computer Architecture (HPCA-27), Seoul, South Korea, Feb. 2021 

  • Acceptance Rate: 24% (63 among 258)

  • [Paper][Talk]

• Ranggi Hwang, Taehun Kim, Youngeun Kwon, and Minsoo Rhu, "Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations," The 47th International Symposium on Computer Architecture (ISCA-47), Valencia, Spain, June 2020

  • Acceptance Rate: 18% (77 among 421)

  • [Paper]

• Bongjoon Hyun, Youngeun Kwon, Yujeong Choi, John Kim, and Minsoo Rhu, "NeuMMU: Architectural Support for Efficient Address Translations in NPUs," The 25th ACM International Conference on Computer Architectural Support for Programming Languages and Operating Systems (ASPLOS-25), Lausanne, Switzerland, Mar. 2020

  • Selected for IEEE Micro Top Picks Honorable Mention ("IEEE Micro - Top Picks From the 2020 Computer Architecture")

  • Acceptance Rate: 18% (86 among 476)

  • [Paper]

• Youngeun Kwon, Yunjae Lee, and Minsoo Rhu, "TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning," The 52nd IEEE/ACM International Symposium on Microarchitecture (MICRO-52), Columbus, OH, Oct. 2019

  • Selected for IEEE Micro Top Picks Honorable Mention ("IEEE Micro - The 2019 Top Picks in Computer Architecture")

  • Acceptance Rate: 22% (79 among 344)

  • [Paper]

• Youngeun Kwon and Minsoo Rhu, "A Disaggregated Memory System for Deep Learning," IEEE Micro, Special Issue on Machine Learning Acceleration, Sep/Oct., 2019

  • [Paper]

• Youngeun Kwon and Minsoo Rhu, "Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep Learning", The 51st IEEE/ACM International Symposium on Microarchitecture (MICRO-51), Fukuoka, Japan, Oct. 2018

  • Acceptance Rate: 21% (74 among 351)

  • [Paper][Lightning talk]

• Youngeun Kwon and Minsoo Rhu, "A Case for Memory-Centric HPC System Architecture for Training Deep Neural Networks", in IEEE Computer Architecture Letters (CAL), vol. 17, no. 2, pp. 134-138, July-Dec. 2018. 

  • [Paper]

• Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Youngeun Kwon, and Stephen W. Keckler, "Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks," The 24th IEEE International Symposium on High-Performance Computer Architecture (HPCA-24), Vienna, Austria, Feb. 2018 

  • Acceptance Rate: 20% (54 among 260)

  • [Paper] [Talk]

• Neural network acceleration system and operating method thereof

• Inventor: Minsoo Rhu, Youngeun Kwon, and Yunjae Lee

• Applications: KR (2019), US (2020), CN (2020)

• Registered: KR (2022)

• Current Assignee: KAIST

• High performance computing system for deep learning

• Inventor: Minsoo Rhu, and Youngeun Kwon

• Applications: KR (2018), US (2019)

• Registered: KR (2022)

• Current Assignee: Samsung Electronics

Extended Review Committee

• High-Performance Computer Architecture (HPCA): 2024

Journal Reviewer

• Computer Architecture Letters (CAL): 2023

• Applied Artificial Intelligence: 2023

• ETRI Journal: 2020

Discussion Chair

• PeRSonAI tutorial at ISCA: 2020