A3: Accelerating Attention Mechanisms in Neural Networks with Approximation. - Tae Jun Ham; Sungjun Jung; Seonghak Kim; Young H. Oh; Yeonhong Park; Yoonho Song; Jung-Hun Park; Sanghee Lee 002; Kyoung Park; Jae W. Lee; Deog-Kyoon Jeong (HPCA, 2020)

Unlocking Wordline-level Parallelism for Fast Inference on RRAM-based DNN Accelerator. - Yeonhong Park; Seung Yul Lee; Hoon Shin; Jun Heo 001; Tae Jun Ham; Jae W. Lee (ICCAD, 2020)

Graphene: Strong yet Lightweight Row Hammer Protection. - Yeonhong Park; Woosuk Kwon; Eojin Lee; Tae Jun Ham; Jung Ho Ahn; Jae W. Lee (MICRO, 2020)

Layerweaver: Maximizing Resource Utilization of Neural Processing Units via Layer-Wise Scheduling. - Young H. Oh; Seonghak Kim; Yunho Jin; Sam Son; Jonghyun Bae; Jongsung Lee 001; Yeonhong Park; Dong Uk Kim; Tae Jun Ham; Jae W. Lee (HPCA, 2021)

BOSS: Bandwidth-Optimized Search Accelerator for Storage-Class Memory. - Jun Heo 001; Seung Yul Lee; Sunhong Min; Yeonhong Park; Sungjun Jung; Tae Jun Ham; Jae W. Lee (ISCA, 2021)

Effective zero compression on ReRAM-based sparse DNN accelerators. - Hoon Shin; Rihae Park; Seung Yul Lee; Yeonhong Park; Hyunseung Lee 001; Jae W. Lee (DAC, 2022)

Mithril: Cooperative Row Hammer Protection on Commodity DRAM Leveraging Managed Refresh. - Michael Jaemin Kim; Jaehyun Park 006; Yeonhong Park; Wanju Doh; Namhoon Kim; Tae Jun Ham; Jae W. Lee; Jung Ho Ahn (HPCA, 2022)

Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching. - Yeonhong Park; Sunhong Min; Jae W. Lee (Proc. VLDB Endow., 2022)

Liquid: Mix-and-Match Multiple Image Formats to Balance DNN Training Pipeline. - Woohyeon Baek; Jonghyun Bae; Donghyun Lee 005; Hyunwoong Bae; Yeonhong Park; Jae W. Lee (APSys, 2023)

ReLUifying Smooth Functions: Low-Cost Knowledge Distillation to Obtain High-Performance ReLU Networks. - Soosung Kim 001; Yeonhong Park; Hyunseung Lee 001; Sungchan Yi; Jae W. Lee (ACCV (8), 2024)

Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs. - Yeonhong Park; Jake Hyun; SangLyul Cho; Bonggeun Sim; Jae W. Lee (ICML, 2024)

Pushing the Envelope of Low-Bit LLM via Dynamic Error Compensation. - Yeonhong Park; Jake Hyun; Hojoon Kim; Jae W. Lee (CoRR, 2024)

FACIL: Flexible DRAM Address Mapping for SoC-PIM Cooperative On-device LLM Inference. - Seong Hoon Seo; Junghoon Kim; Donghyun Lee 005; Seonah Yoo; Seokwon Moon; Yeonhong Park; Jae W. Lee (HPCA, 2025)

DecDEC: A Systems Approach to Advancing Low-Bit LLM Quantization. - Yeonhong Park; Jake Hyun; Hojoon Kim; Jae W. Lee (OSDI, 2025)

GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance. - Jinuk Kim; Marwa El Halabi; Wonpyo Park; Clemens JS Schaefer; Deokjae Lee; Yeonhong Park; Jae W. Lee; Hyun Oh Song (CoRR, 2025)

NestedFP: High-Performance, Memory-Efficient Dual-Precision Floating Point Support for LLMs. - Haeun Lee; Omin Kwon; Yeonhong Park; Jae W. Lee (CoRR, 2025)