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

Sep 26, 2025 Miyeon Lee has joined our group as a Master student. Welcome!
Sep 22, 2025 Seunghee Song has joined our group as an undergraduate intern. Welcome!
Sep 19, 2025 Our lab has two papers accepted for presentation at 2025 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI).
Sep 15, 2025 Eunseo Ko has joined our group as an undergraduate intern. Welcome!
Sep 01, 2025 Jihyo Han has joined our group as a Master student. Welcome!

latest publications

  1. Accepted
    MAGNETO: A Genetic Algorithm-Based Power-Aware Mapping Optimization Framework for Mobile NPUs
    Eunjin Lee, Jiho Lee, Hayoung Lim, and Jaehyeong Sim
    In 2025 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)
  2. Accepted
    DS-CAE: a Dual-Stream Cross-Attentive Autoencoder for Robust and Cluster-Aware Retrieval-Augmented Generation
    Soeun Choi, Yejin Lee, Juhee Kim, Minji Kim, and Jaehyeong Sim
    In 2025 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)
  3. Accepted
    LoRA-PIM: In-Memory Delta-Weight Injection for Multi-Adapter LLM Serving
    Soeun Choi, and Jaehyeong Sim
    In 2025 22st International SoC Design Conference (ISOCC)
  4. Accepted
    GATHER: A Gated-Attention Accelerator for Efficient LLM Inference
    Eunjin Lee, Eunseo Kim, Eunjoung Yoo, and Jaehyeong Sim
    In 2025 22st International SoC Design Conference (ISOCC)