Federated Onboard-Ground Station Computing With Weakly Supervised Cascading Pyramid Attention Network for Satellite Image Analysis. - Taewoo Kim 003; Minsu Jeon; Changha Lee; Junsoo Kim 002; Geonwoo Ko; Joo-Young Kim 001; Chan-Hyun Youn (IEEE Access, 2022)

A 26.55TOPS/W Explainable AI Processor with Dynamic Workload Allocation and Heat Map Compression/Pruning. - Junsoo Kim 002; Geonwoo Ko; Ji-Hoon Kim 004; Changha Lee; Taewoo Kim 003; Chan-Hyun Youn; Joo-Young Kim 001 (CICC, 2023)

MuLe: Multi-Grained Graph Learning for Multi-Behavior Recommendation. - Seunghan Lee; Geonwoo Ko; Hyun-Je Song; Jinhong Jung (CIKM, 2024)

Picasso: An Area/Energy-Efficient End-to-End Diffusion Accelerator with Hyper-Precision Data Type. - Sungyeob Yoo; Geonwoo Ko; Seri Ham; Seeyeon Kim; Yi Chen; Joo-Young Kim 001 (HCS, 2024)

Learning disentangled representations in signed directed graphs without social assumptions. - Geonwoo Ko; Jinhong Jung (Inf. Sci., 2024)

EPU: An Energy-Efficient Explainable AI Accelerator With Sparsity-Free Computation and Heat Map Compression/Pruning. - Junsoo Kim 002; Seunghee Han; Geonwoo Ko; Ji-Hoon Kim 004; Changha Lee; Taewoo Kim 003; Chan-Hyun Youn; Joo-Young Kim 001 (IEEE J. Solid State Circuits, 2024)

Personalized Ranking on Cascading Behavior Graphs for Accurate Multi-Behavior Recommendation. - Geonwoo Ko; Minseo Jeon; Jinhong Jung (CoRR, 2025)

ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput. - Junsoo Kim 002; Hunjong Lee; Geonwoo Ko; Gyubin Choi; Seri Ham; Seongmin Hong; Joo-Young Kim 006 (CoRR, 2025)