GPUdmm: A high-performance and memory-oblivious GPU architecture using dynamic memory management. - Youngsok Kim; Jaewon Lee; Jae-Eon Jo; Jangwoo Kim (HPCA, 2014)

Stealing Webpages Rendered on Your Browser by Exploiting GPU Vulnerabilities. - Sangho Lee 001; Youngsok Kim; Jangwoo Kim; Jong Kim 001 (IEEE Symposium on Security and Privacy, 2014)

ScaleGPU: GPU Architecture for Memory-Unaware GPU Programming. - Youngsok Kim; Jaewon Lee; Donggyu Kim; Jangwoo Kim (IEEE Comput. Archit. Lett., 2014)

DCS: a fast and scalable device-centric server architecture. - Jaehyung Ahn; Dongup Kwon; Youngsok Kim; Mohammadamin Ajdari; Jaewon Lee; Jangwoo Kim (MICRO, 2015)

CloudSwap: A Cloud-Assisted Swap Mechanism for Mobile Devices. - Dongju Chae; Joonsung Kim; Youngsok Kim; Jangwoo Kim; Kyung-Ah Chang; Sang-Bum Suh; Hyogun Lee (CCGrid, 2016)

Efficient footprint caching for Tagless DRAM Caches. - Hakbeom Jang; Yongjun Lee; Jongwon Kim; Youngsok Kim; Jangwoo Kim; Jinkyu Jeong; Jae W. Lee (HPCA, 2016)

GPUpd: a fast and scalable multi-GPU architecture using cooperative projection and distribution. - Youngsok Kim; Jae-Eon Jo; Hanhwi Jang; Minsoo Rhu; Hanjun Kim 001; Jangwoo Kim (MICRO, 2017)

Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks. - Amirali Boroumand; Saugata Ghose; Youngsok Kim; Rachata Ausavarungnirun; Eric Shiu; Rahul Thakur; Daehyun Kim 003; Aki Kuusela; Allan Knies; Parthasarathy Ranganathan; Onur Mutlu (ASPLOS, 2018)

DCS-ctrl: A Fast and Flexible Device-Control Mechanism for Device-Centric Server Architecture. - Dongup Kwon; Jaehyung Ahn; Dongju Chae; Mohammadamin Ajdari; Jaewon Lee; Suheon Bae; Youngsok Kim; Jangwoo Kim (ISCA, 2018)

Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations. - Dayeol Lee; Gwangmu Lee; Dongup Kwon; Sunghwa Lee; Youngsok Kim; Jangwoo Kim (ISCA, 2018)

μLayer: Low Latency On-Device Inference Using Cooperative Single-Layer Acceleration and Processor-Friendly Quantization. - Youngsok Kim; Joonsung Kim; Dongju Chae; Daehyun Kim 003; Jangwoo Kim (EuroSys, 2019)

FlexLearn: Fast and Highly Efficient Brain Simulations Using Flexible On-Chip Learning. - Eunjin Baek; Hunjun Lee; Youngsok Kim; Jangwoo Kim (MICRO, 2019)

Real-Time Object Detection System with Multi-Path Neural Networks. - Seonyeong Heo; Sungjun Cho; Youngsok Kim; Hanjun Kim 001 (RTAS, 2020)

Thread-Aware Area-Efficient High-Level Synthesis Compiler for Embedded Devices. - Changsu Kim 004; Shinnung Jeong; Sungjun Cho; Yongwoo Lee 001; William Song; Youngsok Kim; Hanjun Kim 001 (CGO, 2021)

DANCE: Differentiable Accelerator/Network Co-Exploration. - Kanghyun Choi; Deokki Hong; Hojae Yoon; Joonsang Yu; Youngsok Kim; Jinho Lee (DAC, 2021)

Dataflow Mirroring: Architectural Support for Highly Efficient Fine-Grained Spatial Multitasking on Systolic-Array NPUs. - Jounghoo Lee; Jinwoo Choi 003; Jaeyeon Kim; Jinho Lee; Youngsok Kim (DAC, 2021)

Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples. - Kanghyun Choi; Deokki Hong; Noseong Park; Youngsok Kim; Jinho Lee (NeurIPS, 2021)

Making a Better Use of Caches for GCN Accelerators with Feature Slicing and Automatic Tile Morphing. - Mingi Yoo; Jaeyong Song; Jounghoo Lee; Namhyung Kim; Youngsok Kim; Jinho Lee (IEEE Comput. Archit. Lett., 2021)

Decoupling Schedule, Topology Layout, and Algorithm to Easily Enlarge the Tuning Space of GPU Graph Processing. - Shinnung Jeong; Yongwoo Lee 001; Jaeho Lee 005; Heelim Choi; Seungbin Song; Jinho Lee; Youngsok Kim; Hanjun Kim 001 (PACT, 2022)

Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators. - Mingi Yoo; Jaeyong Song; Hyeyoon Lee; Jounghoo Lee; Namhyung Kim; Youngsok Kim; Jinho Lee (PACT, 2022)

It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher. - Kanghyun Choi; Hyeyoon Lee; Deokki Hong; Joonsang Yu; Noseong Park; Youngsok Kim; Jinho Lee (CVPR, 2022)

Enabling hard constraints in differentiable neural network and accelerator co-exploration. - Deokki Hong; Kanghyun Choi; Hyeyoon Lee; Joonsang Yu; Noseong Park; Youngsok Kim; Jinho Lee (DAC, 2022)

SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs. - Seongyeon Park; Hajin Kim; Tanveer Ahmad; Nauman Ahmed; Zaid Al-Ars; H. Peter Hofstee; Youngsok Kim; Jinho Lee (IPDPS, 2022)

GCoM: a detailed GPU core model for accurate analytical modeling of modern GPUs. - Jounghoo Lee; Yeonan Ha; Suhyun Lee 002; Jinyoung Woo; Jinho Lee; Hanhwi Jang; Youngsok Kim (ISCA, 2022)

GuardiaNN: Fast and Secure On-Device Inference in TrustZone Using Embedded SRAM and Cryptographic Hardware. - Jinwoo Choi 003; Jaeyeon Kim; Chaemin Lim; Suhyun Lee 002; Jinho Lee; Dokyung Song; Youngsok Kim (Middleware, 2022)

Virtual PIM: Resource-Aware Dynamic DPU Allocation and Workload Scheduling Framework for Multi-DPU PIM Architecture. - Donghyeon Kim 001; Taehoon Kim; Inyong Hwang; Taehyeong Park 001; Hanjun Kim 001; Youngsok Kim; Yongjun Park 001 (PACT, 2023)

Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression. - Jaeyong Song; Jinkyu Yim; Jaewon Jung 001; Hongsun Jang; Hyung-Jin Kim; Youngsok Kim; Jinho Lee (ASPLOS (2), 2023)

Occamy: Memory-efficient GPU Compiler for DNN Inference. - Jaeho Lee 005; Shinnung Jeong; Seungbin Song; Kunwoo Kim; Heelim Choi; Youngsok Kim; Hanjun Kim 001 (DAC, 2023)

Pipe-BD: Pipelined Parallel Blockwise Distillation. - Hongsun Jang; Jaewon Jung 001; Jaeyong Song; Joonsang Yu; Youngsok Kim; Jinho Lee (DATE, 2023)

SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators. - Mingi Yoo; Jaeyong Song; Jounghoo Lee; Namhyung Kim; Youngsok Kim; Jinho Lee (HPCA, 2023)

McCore: A Holistic Management of High-Performance Heterogeneous Multicores. - Jaewon Kwon; Yongju Lee 003; Hongju Kal; Minjae Kim; Youngsok Kim; Won Woo Ro (MICRO, 2023)

GraNNDis: Efficient Unified Distributed Training Framework for Deep GNNs on Large Clusters. - Jaeyong Song; Hongsun Jang; Jaewon Jung 001; Youngsok Kim; Jinho Lee (CoRR, 2023)

Design and Analysis of a Processing-in-DIMM Join Algorithm: A Case Study with UPMEM DIMMs. - Chaemin Lim; Suhyun Lee 002; Jinwoo Choi 003; Jounghoo Lee; Seongyeon Park; Hanjun Kim 001; Jinho Lee; Youngsok Kim (Proc. ACM Manag. Data, 2023)

Enabling Fine-Grained Spatial Multitasking on Systolic-Array NPUs Using Dataflow Mirroring. - Jinwoo Choi 003; Yeonan Ha; Jounghoo Lee; Sangsu Lee; Jinho Lee; Hanhwi Jang; Youngsok Kim (IEEE Trans. Computers, 2023)

GraNNDis: Fast Distributed Graph Neural Network Training Framework for Multi-Server Clusters. - Jaeyong Song; Hongsun Jang; Hunseong Lim; Jaewon Jung 001; Youngsok Kim; Jinho Lee (PACT, 2024)

MPC-Wrapper: Fully Harnessing the Potential of Samsung Aquabolt-XL HBM2-PIM on FPGAs. - Jinwoo Choi 003; Yeonan Ha; Hanna Cha; Seil Lee; Sungchul Lee; Jounghoo Lee; Shinhaeng Kang; Bongjun Kim; Hanwoong Jung; Hanjun Kim 001; Youngsok Kim (FCCM, 2024)

Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real System. - Hongsun Jang; Jaeyong Song; Jaewon Jung 001; Jaeyoung Park; Youngsok Kim; Jinho Lee (HPCA, 2024)

CR2: Community-aware Compressed Regular Representation for Graph Processing on a GPU. - Shinnung Jeong; Sungjun Cho; Yongwoo Lee 001; Hyunjun Park; Seonyeong Heo; Gwangsun Kim; Youngsok Kim; Hanjun Kim 001 (ICPP, 2024)

PID-Comm: A Fast and Flexible Collective Communication Framework for Commodity Processing-in-DIMM Devices. - Si Ung Noh; Junguk Hong; Chaemin Lim; Seongyeon Park; Jeehyun Kim; Hanjun Kim 001; Youngsok Kim; Jinho Lee (ISCA, 2024)

Orchestrating Multiple Mixed Precision Models on a Shared Precision-Scalable NPU. - Kiung Jung; Seok Namkoong; Hongjun Um; Hyejun Kim; Youngsok Kim; Yongjun Park 001 (LCTES, 2024)

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping. - Seongyeon Park; Junguk Hong; Jaeyong Song; Hajin Kim; Youngsok Kim; Jinho Lee (PPoPP, 2024)

Gem5-AVX: Extension of the Gem5 Simulator to Support AVX Instruction Sets. - Seungmin Lee; Youngsok Kim; Dukyun Nam; Jong Kim 001 (IEEE Access, 2024)

GCStack: A GPU Cycle Accounting Mechanism for Providing Accurate Insight Into GPU Performance. - Hanna Cha; Sungchul Lee; Yeonan Ha; Hanhwi Jang; Joonsung Kim; Youngsok Kim (IEEE Comput. Archit. Lett., 2024)

AIS-SNU/GraNNDis_Artifact: Artifact Evaluation Submission. - Jaeyong Song; Hongsun Jang; Hunseong Lim; Jaewon Jung 001; Youngsok Kim; Jinho Lee (Zenodo, 2024)

SPID-Join: A Skew-resistant Processing-in-DIMM Join Algorithm Exploiting the Bank- and Rank-level Parallelisms of DIMMs. - Suhyun Lee 002; Chaemin Lim; Jinwoo Choi 003; Heelim Choi; Chan Lee; Yongjun Park 001; Kwanghyun Park 001; Hanjun Kim 001; Youngsok Kim (Proc. ACM Manag. Data, 2024)

GCStack+GCScaler: Fast and Accurate GPU Performance Analyses Using Fine-Grained Stall Cycle Accounting and Interval Analysis. - Hanna Cha; Sungchul Lee; Jounghoo Lee; Yeonan Ha; Joonsung Kim; Youngsok Kim (ISCA, 2025)

IntervalSim++: Enhanced Interval Simulation for Unbalanced Processor Designs. - Haseung Bong; Nahyeon Kang; Youngsok Kim; Joonsung Kim; Hanhwi Jang (IEEE Comput. Archit. Lett., 2025)