AI Computing Platform Laboratory

Department of Computer Science and Engineering, Artificial Intelligence Convergence, Ewha Womans University

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The AI Computing Platform Laboratory (ACPL) has been a lab of excellence for efficient AI hardware & software research since its founding in 2021.

Our main research goal is to accelerate AI model in a faster and energy-efficient way through HW/SW co-design. Specifically, our research interests include:

  • Designing Neural Processing Unit (NPU) and domain-specific hardware
  • Making AI model efficient, such as quantization, pruning, and knowledge distillation
  • Hardware-aware neural architecture search (HW-Aware NAS) and neural architecture accelerator search (NAAS)
  • Processing-in-memory (PIM)

현재 연구실 구성원 모집 정보 및 연구실 합류 희망자 안내

news

Mar 04, 2025 Our lab has one paper accepted for presentation in IEEE Conference on Artificial Intelligence (CAI) 2025.
Feb 24, 2025 HaYoung, Subean, and Kyungmi have graduated. Congratulations!
Dec 24, 2024 Our lab has one paper accepted for publication in IEEE Access.
Dec 08, 2024 Our lab has one paper accepted for presentation in IEEE BigComp 2025.
Nov 21, 2024 Our lab has one paper accepted for publication in IEEE Access.

latest publications

  1. Accepted
    ViT-Slim: Genetic Alorighm-based NAS Framework for Efficient Vision Transformer Design
    Eunjoung Yoo, and Jaehyeong Sim
    In 2025 IEEE International Conference on Artificial Intelligence (CAI)
  2. Enhancing Gender Prediction Model Performance through Automatic Individual Entity Extraction and Class Balance
    Chaeyun Kim, Eunseo Kim, Yeonhee Kim, Jaehyeong Sim, and Jonkil Kim
    In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp)
  3. SCIE
    PRISM-Med: Parameter-efficient Robust Interdomain Specialty Model for Medical Language Tasks
    Jieui Kang, Hyungon Ryu, and Jaehyeong Sim
    IEEE Access, vol.13, pp.4957-4965, 2025
    Collaborative research conducted with NVIDIA
    DOI
  4. SCIE
    SpDRAM: Efficient In-DRAM Acceleration of Sparse Matrix-Vector Multiplication
    Jieui Kang, Soeun Choi, Eunjin Lee, and Jaehyeong Sim
    IEEE Access, vol.12, pp.176009-176021, 2024
    DOI