AI Computing Platform Laboratory

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

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As AI models continue to grow in size, they require vast amounts of energy, making sustainable AI unfeasible if current trends persist. Consequently, the importance of robust computing HW/SW infrastructure underpinning AI will become critical.

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

  • Neural Processing Unit (NPU), domain-specific hardware, FPGA
  • quantization, pruning, and knowledge distillation
  • Hardware-aware neural architecture search (HW-Aware NAS) and neural architecture accelerator search (NAAS)
  • Processing-in-memory (PIM)
  • Efficient LLM serving including KV Caching and other optimizations
  • On-Device AI

현재 연구실 구성원 모집 정보 및 연구실 합류 희망자 안내

news

Apr 02, 2025 Prof. Sim was selected as a Teaching Excellence Award for the 2024-2 semester. link.
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.

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
  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