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2020 – today
- 2024
[j9]Hyeseong Kim
, Yunjae Lee, Minsoo Rhu:
FPGA-Accelerated Data Preprocessing for Personalized Recommendation Systems. IEEE Comput. Archit. Lett. 23(1): 7-10 (2024)- [j8]Dongho Yoon, Taehun Kim, Jae W. Lee, Minsoo Rhu:
A Quantitative Analysis of State Space Model-Based Large Language Model: Study of Hungry Hungry Hippos. IEEE Comput. Archit. Lett. 23(2): 154-157 (2024) - [c41]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: 197-214 - [c40]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: 616-630 - [c39]Bongjoon Hyun, Taehun Kim, Dongjae Lee, Minsoo Rhu:
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology. HPCA 2024: 263-279 - [c38]Yunjae Lee, Hyeseong Kim, Minsoo Rhu:
PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models. ISCA 2024: 340-353 - [c37]Yujeong Choi, Jiin Kim, Minsoo Rhu:
ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Models. ISCA 2024: 410-423 - [c36]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. MICRO 2024: 153-167 - [c35]Dongjae Lee, Bongjoon Hyun, Taehun Kim, Minsoo Rhu:
PIM-MMU: A Memory Management Unit for Accelerating Data Transfers in Commercial PIM Systems. MICRO 2024: 627-642 - [c34]Zhixian Jin, Christopher Rocca, Jiho Kim, Hans Kasan, Minsoo Rhu, Ali Bakhoda, Tor M. Aamodt, John Kim:
Uncovering Real GPU NoC Characteristics: Implications on Interconnect Architecture. MICRO 2024: 885-898 - [i27]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 abs/2404.08847 (2024) - [i26]Yujeong Choi, Jiin Kim, Minsoo Rhu:
ElasticRec: A Microservice-based Model Serving Architecture Enabling Elastic Resource Scaling for Recommendation Models. CoRR abs/2406.06955 (2024) - [i25]Yunjae Lee, Hyeseong Kim, Minsoo Rhu:
PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models. CoRR abs/2406.14571 (2024) - [i24]Dongjae Lee, Bongjoon Hyun, Taehun Kim, Minsoo Rhu:
PIM-MMU: A Memory Management Unit for Accelerating Data Transfers in Commercial PIM Systems. CoRR abs/2409.06204 (2024) - 2023
- [j7]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): 13-16 (2023) - [c33]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: 42-55 - [i23]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. CoRR abs/2301.10904 (2023) - [i22]Yujeong Choi, John Kim, Minsoo Rhu:
Hera: A Heterogeneity-Aware Multi-Tenant Inference Server for Personalized Recommendations. CoRR abs/2302.11750 (2023) - [i21]Bongjoon Hyun, Taehun Kim, Dongjae Lee, Minsoo Rhu:
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology. CoRR abs/2308.00846 (2023) - [i20]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 abs/2308.12066 (2023) - [i19]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 abs/2312.12391 (2023) - 2022
- [c32]Yunseong Kim, Yujeong Choi, Minsoo Rhu:
PARIS and ELSA: an elastic scheduling algorithm for reconfigurable multi-GPU inference servers. DAC 2022: 607-612 - [c31]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: 711-725 - [c30]Youngeun Kwon, Minsoo Rhu:
Training personalized recommendation systems from (GPU) scratch: look forward not backwards. ISCA 2022: 860-873 - [c29]Yunjae Lee, Jinha Chung, Minsoo Rhu:
SmartSAGE: training large-scale graph neural networks using in-storage processing architectures. ISCA 2022: 932-945 - [c28]Beomsik Park, Ranggi Hwang, Dongho Yoon, Yoonhyuk Choi, Minsoo Rhu:
DiVa: An Accelerator for Differentially Private Machine Learning. MICRO 2022: 1200-1217 - [c27]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: 1237-1254 - [i18]Yunseong Kim, Yujeong Choi, Minsoo Rhu:
PARIS and ELSA: An Elastic Scheduling Algorithm for Reconfigurable Multi-GPU Inference Servers. CoRR abs/2202.13481 (2022) - [i17]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 abs/2203.00158 (2022) - [i16]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 abs/2205.00922 (2022) - [i15]Youngeun Kwon, Minsoo Rhu:
Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards. CoRR abs/2205.04702 (2022) - [i14]Yunjae Lee, Jinha Chung, Minsoo Rhu:
SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures. CoRR abs/2205.04711 (2022) - [i13]Beomsik Park, Ranggi Hwang, Dongho Yoon, Yoonhyuk Choi, Minsoo Rhu:
DiVa: An Accelerator for Differentially Private Machine Learning. CoRR abs/2208.12392 (2022) - 2021
- [j6]Byeongho Kim, Jaehyun Park, Eojin Lee, Minsoo Rhu, Jung Ho Ahn:
TRiM: Tensor Reduction in Memory. IEEE Comput. Archit. Lett. 20(1): 5-8 (2021) - [j5]Bongjoon Hyun, Jiwon Lee, Minsoo Rhu:
Characterization and Analysis of Deep Learning for 3D Point Cloud Analytics. IEEE Comput. Archit. Lett. 20(2): 106-109 (2021) - [j4]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): 118-121 (2021) - [c26]Youngeun Kwon, Yunjae Lee, Minsoo Rhu:
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training. HPCA 2021: 235-248 - [c25]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: 332-344 - [c24]Yujeong Choi, Yunseong Kim, Minsoo Rhu:
Lazy Batching: An SLA-aware Batching System for Cloud Machine Learning Inference. HPCA 2021: 493-506 - [c23]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: 268-281 - [i12]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 abs/2112.15479 (2021) - 2020
- [c22]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: 157-158 - [c21]Bongjoon Hyun, Youngeun Kwon, Yujeong Choi, John Kim, Minsoo Rhu:
NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units. ASPLOS 2020: 1109-1124 - [c20]Yujeong Choi, Minsoo Rhu:
PREMA: A Predictive Multi-Task Scheduling Algorithm For Preemptible Neural Processing Units. HPCA 2020: 220-233 - [c19]Ranggi Hwang, Taehun Kim, Youngeun Kwon, Minsoo Rhu:
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations. ISCA 2020: 968-981 - [i11]Ranggi Hwang, Taehun Kim, Youngeun Kwon, Minsoo Rhu:
Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations. CoRR abs/2005.05968 (2020) - [i10]Youngeun Kwon, Yunjae Lee, Minsoo Rhu:
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training. CoRR abs/2010.13100 (2020) - [i9]Yujeong Choi, Yunseong Kim, Minsoo Rhu:
LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference. CoRR abs/2010.13103 (2020)
2010 – 2019
- 2019
- [j3]Youngeun Kwon, Minsoo Rhu:
A Disaggregated Memory System for Deep Learning. IEEE Micro 39(5): 82-90 (2019) - [c18]Youngeun Kwon, Yunjae Lee, Minsoo Rhu:
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning. MICRO 2019: 740-753 - [i8]Youngeun Kwon, Minsoo Rhu:
Beyond the Memory Wall: A Case for Memory-centric HPC System for Deep Learning. CoRR abs/1902.06468 (2019) - [i7]Youngeun Kwon, Yunjae Lee, Minsoo Rhu:
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning. CoRR abs/1908.03072 (2019) - [i6]Yujeong Choi, Minsoo Rhu:
PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units. CoRR abs/1909.04548 (2019) - [i5]Bongjoon Hyun, Youngeun Kwon, Yujeong Choi, John Kim, Minsoo Rhu:
NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units. CoRR abs/1911.06859 (2019) - 2018
- [j2]Youngeun Kwon, Minsoo Rhu:
A Case for Memory-Centric HPC System Architecture for Training Deep Neural Networks. IEEE Comput. Archit. Lett. 17(2): 134-138 (2018) - [c17]Minsoo Rhu:
Accelerator-centric deep learning systems for enhanced scalability, energy-efficiency, and programmability. ASP-DAC 2018: 527-533 - [c16]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: 78-91 - [c15]Youngeun Kwon, Minsoo Rhu:
Beyond the Memory Wall: A Case for Memory-Centric HPC System for Deep Learning. MICRO 2018: 148-161 - [i4]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 abs/1806.00512 (2018) - 2017
- [c14]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: 73-84 - [c13]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: 27-40 - [c12]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: 574-586 - [i3]Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Stephen W. Keckler:
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks. CoRR abs/1705.01626 (2017) - [i2]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 abs/1708.04485 (2017) - 2016
- [c11]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: 18:1-18:13 - [i1]Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, Stephen W. Keckler:
Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design. CoRR abs/1602.08124 (2016) - 2015
- [c10]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: 89-100 - [c9]Seong-Lyong Gong, Minsoo Rhu, Jungrae Kim, Jinsuk Chung, Mattan Erez:
CLEAN-ECC: high reliability ECC for adaptive granularity memory system. MICRO 2015: 611-622 - 2014
- [c8]Jingwen Leng, Yazhou Zu, Minsoo Rhu, Meeta Sharma Gupta, Vijay Janapa Reddi:
GPUVolt: modeling and characterizing voltage noise in GPU architectures. ISLPED 2014: 141-146 - 2013
- [c7]Minsoo Rhu, Mattan Erez:
The dual-path execution model for efficient GPU control flow. HPCA 2013: 591-602 - [c6]Minsoo Rhu, Mattan Erez:
Maximizing SIMD resource utilization in GPGPUs with SIMD lane permutation. ISCA 2013: 356-367 - [c5]Minsoo Rhu, Michael B. Sullivan, Jingwen Leng, Mattan Erez:
A locality-aware memory hierarchy for energy-efficient GPU architectures. MICRO 2013: 86-98 - 2012
- [c4]Minsoo Rhu, Mattan Erez:
CAPRI: Prediction of compaction-adequacy for handling control-divergence in GPGPU architectures. ISCA 2012: 61-71 - 2010
- [j1]Minsoo Rhu, In-Cheol Park:
Optimization of Arithmetic Coding for JPEG2000. IEEE Trans. Circuits Syst. Video Technol. 20(3): 446-451 (2010)
2000 – 2009
- 2009
- [c3]Minsoo Rhu, In-Cheol Park:
Architecture design of a high-performance dual-symbol binary arithmetic coder for JPEG2000. ICIP 2009: 2665-2668 - [c2]Minsoo Rhu, In-Cheol Park:
Memory-less bit-plane coder architecture for JPEG2000 with concurrent column-stripe coding. ICIP 2009: 2673-2676 - [c1]Minsoo Rhu, In-Cheol Park:
A novel trace-pipelined binary arithmetic coder architecture for JPEG2000. SiPS 2009: 243-248
Coauthor Index
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last updated on 2024-12-10 20:51 CET by the dblp team
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