An 8.9-71.3 TOPS/W Deep Learning Accelerator for Arbitrarily Quantized Neural Networks. - Seunghyun Moon; Kyeong-Jun Lee; Han-Gyeol Mun; Byungjun Kim; Jae-Yoon Sim (IEEE Trans. Circuits Syst. II Express Briefs, 2022)

Joint Optimization of Cache Management and Graph Reordering for GCN Acceleration. - Kyeong-Jun Lee; ByungJun Kim; Han-Gyeol Mun; Seunghyun Moon; Jae-Yoon Sim (ISLPED, 2023)

A 127.8TOPS/W Arbitrarily Quantized 1-to-8b Scalable-Precision Accelerator for General-Purpose Deep Learning with Reduction of Storage, Logic and Latency Waste. - Seunghyun Moon; Han-Gyeol Mun; Hyunwoo Son; Jae-Yoon Sim (ISSCC, 2023)

A 28 nm 66.8 TOPS/W Sparsity-Aware Dynamic-Precision Deep-Learning Processor. - Han-Gyeol Mun; Hyunwoo Son; Seunghyun Moon; Jaehyun Park; ByungJun Kim; Jae-Yoon Sim (VLSI Technology and Circuits, 2023)

Bottleneck-Stationary Compact Model Accelerator With Reduced Requirement on Memory Bandwidth for Edge Applications. - Han-Gyeol Mun; Seunghyun Moon; Byungjun Kim; Kyeong-Jun Lee; Jae-Yoon Sim (IEEE Trans. Circuits Syst. I Regul. Pap., 2023)

Multipurpose Deep-Learning Accelerator for Arbitrary Quantization With Reduction of Storage, Logic, and Latency Waste. - Seunghyun Moon; Han-Gyeol Mun; Hyunwoo Son; Jae-Yoon Sim (IEEE J. Solid State Circuits, 2024)