Yunjae Lee

Ph.D Student in School of Electrical Engineering, KAIST, 2021.03 ~ Present

Adviser: Prof. Minsoo Rhu

Master Student in School of Electrical Engineering, KAIST, 2019.03 ~ 2021.02

Adviser: Prof. Minsoo Rhu

Bachelor's Degree in Department of Electrical and Electronic Engineering, Yonsei university, 2013.03 ~ 2019.02

Summa Cum Laude

The 27th Samsung Humantech Paper Award , 2021.02

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

Scholar of the National Academic Excellence Scholarship for Engineering, Korea Student Aid Foundation (KOSAF), 2017.03 ~ 2018.12

• Yunjae Lee*, Hyeseong Kim* and Minsoo Rhu, "PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models," The 51st International Symposium on Computer Architecture (ISCA-51), Buenos Aires, Argentina, June 2024

  • * Co-first authors

  • Acceptance Rate: 19% (83 among 423)

  • [Paper]

• Hyeseong Kim*, Yunjae Lee*, and Minsoo Rhu, "FPGA-Accelerated Data Preprocessing for Personalized Recommendation Systems," IEEE Computer Architecture Letters (CAL), Nov. 2023

  • * Co-first authors

  • [Paper]

• Yunjae Lee, Jinha Chung and Minsoo Rhu, "SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures," The 49th International Symposium on Computer Architecture (ISCA-49), New York, NY, June 2022

  • Acceptance Rate: 16% (67 among 400)

  • [Paper]

• 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]

• 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]

• 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