T-PIM: A 2.21-to-161.08TOPS/W Processing-In-Memory Accelerator for End-to-End On-Device Training. - Jaehoon Heo; Junsoo Kim 002; Wontak Han; Sukbin Lim; Joo-Young Kim 001 (CICC, 2022)

OpenMDS: An Open-Source Shell Generation Framework for High-Performance Design on Multi-Die FPGAs. - Gyeongcheol Shin; Junsoo Kim 002; Joo-Young Kim 001 (FCCM, 2022)

DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation. - Seongmin Hong; Seungjae Moon; Junsoo Kim 002; Sungjae Lee 002; Minsub Kim; Dongsoo Lee; Joo-Young Kim 001 (HCS, 2022)

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)

OpenMDS: An Open-Source Shell Generation Framework for High-Performance Design on Xilinx Multi-Die FPGAs. - Gyeongcheol Shin; Junsoo Kim 002; Joo-Young Kim 001 (IEEE Comput. Archit. Lett., 2022)

Design of Processing-in-Memory With Triple Computational Path and Sparsity Handling for Energy-Efficient DNN Training. - Wontak Han; Jaehoon Heo; Junsoo Kim 002; Sukbin Lim; Joo-Young Kim 001 (IEEE J. Emerg. Sel. Topics Circuits Syst., 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)

HyperAccel Latency Processing Unit (LPUTM) Accelerating Hyperscale Models for Generative AI. - Seungjae Moon; Junsoo Kim 002; Jung-Hoon Kim; Junseo Cha; Gyubin Choi; Seongmin Hong; Joo-Young Kim 006 (HCS, 2023)

T-PIM: An Energy-Efficient Processing-in-Memory Accelerator for End-to-End On-Device Training. - Jaehoon Heo; Junsoo Kim 002; Sukbin Lim; Wontak Han; Joo-Young Kim 001 (IEEE J. Solid State Circuits, 2023)

LPU: A Latency-Optimized and Highly Scalable Processor for Large Language Model Inference. - Seungjae Moon; Jung-Hoon Kim; Junsoo Kim 002; Seongmin Hong; Junseo Cha; Minsu Kim 004; Sukbin Lim; Gyubin Choi; Dongjin Seo; Jongho Kim; Hunjong Lee; Hyunjun Park; et al. (CoRR, 2024)

A Latency Processing Unit: A Latency-Optimized and Highly Scalable Processor for Large Language Model Inference. - Seungjae Moon; Jung-Hoon Kim; Junsoo Kim 002; Seongmin Hong; Junseo Cha; Minsu Kim 004; Sukbin Lim; Gyubin Choi; Dongjin Seo; Jongho Kim; Hunjong Lee; Hyunjun Park; et al. (IEEE Micro, 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)

Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization. - Minsu Kim 004; Seongmin Hong; Ryeowook Ko; Soongyu Choi; Hunjong Lee; Junsoo Kim 002; Joo-Young Kim 001; Jongse Park (ISCA, 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 001 (ISPASS, 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)

Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization. - Minsu Kim 004; Seongmin Hong; Ryeowook Ko; Soongyu Choi; Hunjong Lee; Junsoo Kim 002; Joo-Young Kim 006; Jongse Park (CoRR, 2025)