관심 주제를 입력하면 그 주제의 논문을 낸 연구실을 관련도 순으로 보여드려요. 순위가 아니라 근거 논문과 함께 보는 출발점이에요.
2026 논문이 아직 반영되지 않은 연구실은 결과에서 불리할 수 있어요.
ZipFlow: a Compiler-based Framework to Unleash Compressed Data Movement for Modern GPUs
Gwangoo Yeo, Zhiyang Shen, Wei Cui, Matteo Interlandi, Rathijit Sen, Bailu Ding, Qi Chen, and Minsoo Rhu
KAIST유민수
Trinity: Three-Dimensional Tensor Program Optimization via Tile-level Equality Saturation
Jaehyeong Park, Youngchan Kim, Haechan An, Gieun Jeong, Jeehoon Kang, Dongsu Han
KAIST한동수
NoiseTilt: Noise-Tilted Reverse Kernels for Diffusion Reward Alignment
Jisung Hwang, Yunhong Min, Jaihoon Kim, I-Chao Shen, Minhyuk Sung
KAIST성민혁
Learning Coordinate-based Convolutional Kernels for Continuous SE(3) Equivariant and Efficient Point Cloud Analysis
Jaein Kim, Hee Bin Yoo, Dong-Sig Han, Byoung-Tak Zhang
SNU장병탁
DictLLM: Post-training Compression of Large Language Model with Dictionary Kernels
J H Park, S Y Chun, M G Seok
SNU전세영
TaLK: Text-Attributed Graph Dataset Distillation via Coupling Language Model with Graph-Aware Kernel
Yeongho Kim, Yeonje Choi, Kijung Shin
KAIST신기정
ATiM: Autotuning Tensor Programs for Processing-in-DRAM
Yongwon Shin*, Dookyung Kang*, Hyojin Sung (*: co-first authors)
SNU성효진
PIM-CARE: A Compiler-Assisted Dynamic Resource Allocation Framework for Real-world DRAM PIM
Inyong Hwang, Donghyeon Kim, Seokwon Kang, Taehyeong Park, Taehoon Kim, Jiwon Seo, Hanjun Kim, Youngsok Kim, Yongjun Park
Yonsei김영석
Brain-inspired Lp-Convolution benefits large kernels and aligns better with visual cortex
Jea Kwon, SungJun Lim, Kyungwoo Song, C. Justin Lee
Yonsei송경우
HYPERF: End-to-End Autotuning Framework for High-Performance Computing
Juseong Park*, Yongwon Shin*, Junghyun Lee, Junseo Lee, Juyeon Kim, Oh-Kyoung Kwon, Hyojin Sung (*: co-first authors)
SNU성효진
COMPASS: A Compiler Framework for Resource-Constrained Crossbar-Array Based In-Memory Deep Learning Accelerators
Jihoon Park*, Jeongin Choe*, Dohyun Kim, Jae-Joon Kim
SNU김재준
An LLM Compiler for Parallel Function Calling
Sehoon Kim, Suhong Moon, Ryan Tabrizi, Nicholas Lee, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
KAIST김세훈
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies
Haanvid Lee, Tri Wahyu Guntara, Jongmin Lee, Yung-Kyun Noh, Kee-Eung Kim
Yonsei이종민
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim, Kai Tzu-iunn Ong, Beong-woo Kwak, Seonghwan Kim, Taeyoon Kwon, Jiwan Chung, Youngjae Yu, Jinyoung Yeo
SNU유영재
Learning to Approximate Adaptive Kernel Convolution on Graphs
Jaeyoon Sim, Sooyeon Jeon, Injun Choi, Guorong Wu, Won Hwa Kim
POSTECH김원화, 류일우
Occamy: Memory-efficient GPU Compiler for DNN Inference
Jaeho Lee, Shinnung Jeong, Seungbin Song, Kunwoo Kim, Heelim Choi, Youngsok Kim, Hanjun Kim
Yonsei김영석
Variational Weighting for Kernel Density Ratios
Sangwoong Yoon, Frank C. Park, Gunsu YUN, Iljung Kim, and Yung-Kyun Noh
SNU박종우
BoKASAN: Binary-only Kernel Address Sanitizer for Effective Kernel Fuzzing
Mingi Cho, Dohyeon An, Hoyong Jin, Taekyoung Kwon
Yonsei권태경
Scale-invariant Bayesian Linearized Neural Networks with Connectivity Tangent Kernel
Sung-Yub Kim, Si Hwan Kim, Kyungsu Kim, Eunho Yang
SNU김경수
Convergence analysis of ODE models for accelerated first-order methods via positive semidefinite kernels
Jungbin Kim, and Insoon Yang
SNU양인순
Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
Sung-Yub Kim, Sihwan Park, Kyungsu Kim, Eunho Yang
KAIST양은호
PIMFlow: Compiler and Runtime Support for CNN Models on Processing-in-Memory DRAM with Mixed-Parallel Execution Model
Yongwon Shin*, Juseong Park*, Sungjun Cho, Hyojin Sung (*: co-first authors)
SNU성효진
Multi-band CNN with Band-dependent Kernels and Amalgamated Cross Entropy Loss for Motor Imagery Classification
Jinhyo Shin, Wonzoo Chung
KU정원주
Deep embedding kernel mixture networks for conditional anomaly detection in high-dimensional data
H. Kim and H. Kim
KAIST김희영