Minsoo Rhu

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 61

Top Topics

Personalized Recommendation

Motion Field Estimation

Top Venues

Publications

Yujeong Choi, Jiin Kim, Minsoo Rhu
ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Models. ISCA (2024)
Juntaek Lim, Youngeun Kwon, Ranggi Hwang, Kiwan Maeng, G. Edward Suh, Minsoo Rhu
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models. ASPLOS (2) (2024)
Yujeong Choi, Jiin Kim, Minsoo Rhu
ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Models. CoRR (2024)
Yunjae Lee, Hyeseong Kim, Minsoo Rhu
PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models. CoRR (2024)
Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Yang Li, Liangzhen Lai, Ilias Leontiadis, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, G. Edward Suh
GPU-based Private Information Retrieval for On-Device Machine Learning Inference. ASPLOS (1) (2024)
Hyeseong Kim, Yunjae Lee, Minsoo Rhu
FPGA-Accelerated Data Preprocessing for Personalized Recommendation Systems. IEEE Comput. Archit. Lett. 23 (1) (2024)
Yunjae Lee, Hyeseong Kim, Minsoo Rhu
PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models. ISCA (2024)
Bongjoon Hyun, Taehun Kim, Dongjae Lee, Minsoo Rhu
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology. HPCA (2024)
Juntaek Lim, Youngeun Kwon, Ranggi Hwang, Kiwan Maeng, G. Edward Suh, Minsoo Rhu
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models. CoRR (2024)
Seonho Lee, Ranggi Hwang, Jongse Park, Minsoo Rhu
HAMMER: Hardware-Friendly Approximate Computing for Self-Attention With Mean-Redistribution And Linearization. IEEE Comput. Archit. Lett. 22 (1) (2023)
Ranggi Hwang, Minhoo Kang, Jiwon Lee, Dongyun Kam, Youngjoo Lee, Minsoo Rhu
GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks. HPCA (2023)
Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, G. Edward Suh
GPU-based Private Information Retrieval for On-Device Machine Learning Inference. CoRR (2023)
Ranggi Hwang, Jianyu Wei, Shijie Cao, Changho Hwang, Xiaohu Tang, Ting Cao, Mao Yang, Minsoo Rhu
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference. CoRR (2023)
Yujeong Choi, John Kim, Minsoo Rhu
Hera: A Heterogeneity-Aware Multi-Tenant Inference Server for Personalized Recommendations. CoRR (2023)
Bongjoon Hyun, Taehun Kim, Dongjae Lee, Minsoo Rhu
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology. CoRR (2023)
Jehyeon Bang, Yujeong Choi, Myeongwoo Kim, Yongdeok Kim, Minsoo Rhu
vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training. CoRR (2023)
Yunjae Lee, Jinha Chung, Minsoo Rhu
SmartSAGE: training large-scale graph neural networks using in-storage processing architectures. ISCA (2022)
Beomsik Park, Ranggi Hwang, Dongho Yoon, Yoonhyuk Choi, Minsoo Rhu
DiVa: An Accelerator for Differentially Private Machine Learning. MICRO (2022)
Yunseong Kim, Yujeong Choi, Minsoo Rhu
PARIS and ELSA: An Elastic Scheduling Algorithm for Reconfigurable Multi-GPU Inference Servers. CoRR (2022)
Minhoo Kang, Ranggi Hwang, Jiwon Lee, Dongyun Kam, Youngjoo Lee, Minsoo Rhu
GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks. CoRR (2022)
Jongmin Kim, Gwangho Lee, Sangpyo Kim, Gina Sohn, John Kim, Minsoo Rhu, Jung Ho Ahn
ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse. CoRR (2022)
Jongmin Kim, Gwangho Lee, Sangpyo Kim, Gina Sohn, Minsoo Rhu, John Kim, Jung Ho Ahn
ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse. MICRO (2022)
Yunseong Kim, Yujeong Choi, Minsoo Rhu
PARIS and ELSA: an elastic scheduling algorithm for reconfigurable multi-GPU inference servers. DAC (2022)
Beomsik Park, Ranggi Hwang, Dongho Yoon, Yoonhyuk Choi, Minsoo Rhu
DiVa: An Accelerator for Differentially Private Machine Learning. CoRR (2022)
Youngeun Kwon, Minsoo Rhu
Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards. CoRR (2022)
Youngeun Kwon, Minsoo Rhu
Training personalized recommendation systems from (GPU) scratch: look forward not backwards. ISCA (2022)
Sangpyo Kim, Jongmin Kim, Michael Jaemin Kim, Wonkyung Jung, John Kim, Minsoo Rhu, Jung Ho Ahn
BTS: an accelerator for bootstrappable fully homomorphic encryption. ISCA (2022)
Yunjae Lee, Jinha Chung, Minsoo Rhu
SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures. CoRR (2022)
Jaehyun Park, Byeongho Kim, Sungmin Yun, Eojin Lee, Minsoo Rhu, Jung Ho Ahn
TRiM: Enhancing Processor-Memory Interfaces with Scalable Tensor Reduction in Memory. MICRO (2021)
Yunjae Lee, Youngeun Kwon, Minsoo Rhu
Understanding the Implication of Non-Volatile Memory for Large-Scale Graph Neural Network Training. IEEE Comput. Archit. Lett. 20 (2) (2021)
Sangpyo Kim, Jongmin Kim, Michael Jaemin Kim, Wonkyung Jung, Minsoo Rhu, John Kim, Jung Ho Ahn
BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption. CoRR (2021)
Byeongho Kim, Jaehyun Park, Eojin Lee, Minsoo Rhu, Jung Ho Ahn
TRiM: Tensor Reduction in Memory. IEEE Comput. Archit. Lett. 20 (1) (2021)
Youngeun Kwon, Yunjae Lee, Minsoo Rhu
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training. HPCA (2021)
Yujeong Choi, Yunseong Kim, Minsoo Rhu
Lazy Batching: An SLA-aware Batching System for Cloud Machine Learning Inference. HPCA (2021)
Jaeguk Ahn, Cheolgyu Jin, Jiho Kim, Minsoo Rhu, Yunsi Fei, David R. Kaeli, John Kim
Trident: A Hybrid Correlation-Collision GPU Cache Timing Attack for AES Key Recovery. HPCA (2021)
Bongjoon Hyun, Jiwon Lee, Minsoo Rhu
Characterization and Analysis of Deep Learning for 3D Point Cloud Analytics. IEEE Comput. Archit. Lett. 20 (2) (2021)
Jiho Kim, Sanghun Cho, Minsoo Rhu, Ali Bakhoda, Tor M. Aamodt, John Kim
Bandwidth Bottleneck in Network-on-Chip for High-Throughput Processors. PACT (2020)
Bongjoon Hyun, Youngeun Kwon, Yujeong Choi, John Kim, Minsoo Rhu
NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units. ASPLOS (2020)
Yujeong Choi, Yunseong Kim, Minsoo Rhu
LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference. CoRR (2020)
Youngeun Kwon, Yunjae Lee, Minsoo Rhu
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training. CoRR (2020)
Ranggi Hwang, Taehun Kim, Youngeun Kwon, Minsoo Rhu
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations. ISCA (2020)
Yujeong Choi, Minsoo Rhu
PREMA: A Predictive Multi-Task Scheduling Algorithm For Preemptible Neural Processing Units. HPCA (2020)
Ranggi Hwang, Taehun Kim, Youngeun Kwon, Minsoo Rhu
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations. CoRR (2020)
Youngeun Kwon, Minsoo Rhu
A Disaggregated Memory System for Deep Learning. IEEE Micro 39 (5) (2019)
Youngeun Kwon, Yunjae Lee, Minsoo Rhu
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning. MICRO (2019)
Bongjoon Hyun, Youngeun Kwon, Yujeong Choi, John Kim, Minsoo Rhu
NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units. CoRR (2019)
Yujeong Choi, Minsoo Rhu
PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units. CoRR (2019)
Youngeun Kwon, Yunjae Lee, Minsoo Rhu
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning. CoRR (2019)
Youngeun Kwon, Minsoo Rhu
Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep Learning. CoRR (2019)
Youngeun Kwon, Minsoo Rhu
Beyond the Memory Wall: A Case for Memory-Centric HPC System for Deep Learning. MICRO (2018)
Maohua Zhu, Jason Clemons, Jeff Pool, Minsoo Rhu, Stephen W. Keckler, Yuan Xie
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training. CoRR (2018)
Youngeun Kwon, Minsoo Rhu
A Case for Memory-Centric HPC System Architecture for Training Deep Neural Networks. IEEE Comput. Archit. Lett. 17 (2) (2018)
Minsoo Rhu
Accelerator-centric deep learning systems for enhanced scalability, energy-efficiency, and programmability. ASP-DAC (2018)
Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Youngeun Kwon, Stephen W. Keckler
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks. HPCA (2018)
Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel S. Emer, Stephen W. Keckler, William J. Dally
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks. ISCA (2017)
Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel S. Emer, Stephen W. Keckler, William J. Dally
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks. CoRR (2017)
Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Stephen W. Keckler
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks. CoRR (2017)
Niladrish Chatterjee, Mike O'Connor, Donghyuk Lee, Daniel R. Johnson, Stephen W. Keckler, Minsoo Rhu, William J. Dally
Architecting an Energy-Efficient DRAM System for GPUs. HPCA (2017)
Youngsok Kim, Jae-Eon Jo, Hanhwi Jang, Minsoo Rhu, Hanjun Kim, Jangwoo Kim
GPUpd: a fast and scalable multi-GPU architecture using cooperative projection and distribution. MICRO (2017)
Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, Stephen W. Keckler
Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design. CoRR (2016)
Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, Stephen W. Keckler
vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design. MICRO (2016)
Seong-Lyong Gong, Minsoo Rhu, Jungrae Kim, Jinsuk Chung, Mattan Erez
CLEAN-ECC: high reliability ECC for adaptive granularity memory system. MICRO (2015)
Dong Li, Minsoo Rhu, Daniel R. Johnson, Mike O'Connor, Mattan Erez, Doug Burger, Donald S. Fussell, Stephen W. Redder
Priority-based cache allocation in throughput processors. HPCA (2015)
Jingwen Leng, Yazhou Zu, Minsoo Rhu, Meeta Sharma Gupta, Vijay Janapa Reddi
GPUVolt: modeling and characterizing voltage noise in GPU architectures. ISLPED (2014)
Minsoo Rhu, Mattan Erez
Maximizing SIMD resource utilization in GPGPUs with SIMD lane permutation. ISCA (2013)
Minsoo Rhu, Mattan Erez
The dual-path execution model for efficient GPU control flow. HPCA (2013)
Minsoo Rhu, Michael B. Sullivan, Jingwen Leng, Mattan Erez
A locality-aware memory hierarchy for energy-efficient GPU architectures. MICRO (2013)
Minsoo Rhu, Mattan Erez
CAPRI: Prediction of compaction-adequacy for handling control-divergence in GPGPU architectures. ISCA (2012)
Minsoo Rhu, In-Cheol Park
Optimization of Arithmetic Coding for JPEG2000. IEEE Trans. Circuits Syst. Video Technol. 20 (3) (2010)
Minsoo Rhu, In-Cheol Park
Memory-less bit-plane coder architecture for JPEG2000 with concurrent column-stripe coding. ICIP (2009)
Minsoo Rhu, In-Cheol Park
Architecture design of a high-performance dual-symbol binary arithmetic coder for JPEG2000. ICIP (2009)
Minsoo Rhu, In-Cheol Park
A novel trace-pipelined binary arithmetic coder architecture for JPEG2000. SiPS (2009)