180 citations · 520 across the 35 of their papers we have counts for
12 papers · 1 filter
SwarmIO: Towards 100 Million IOPS SSD Emulation for Next-generation GPU-centric Storage Systems
Hyeseong Kim, Gwangoo Yeo, Minsoo Rhu
GPU-initiated I/O has emerged as a key mechanism for achieving high-throughput storage access by leveraging massive GPU thread-level parallelism, while recent industry trends point…
Mamba-X: An End-to-End Vision Mamba Accelerator for Edge Computing Devices
Dongho Yoon, Gungyu Lee, Jaewon Chang +3
Transformers have proven effective in language modeling but are limited by high computational and memory demands that grow quadratically with input sequence length. State space mod…
PIM-malloc: A Fast and Scalable Dynamic Memory Allocator for Processing-In-Memory (PIM) Architectures
Dongjae Lee, Bongjoon Hyun, Youngjin Kwon +1
The ability to dynamically allocate memory is fundamental in modern programming languages. However, this feature is not adequately supported in current general-purpose PIM devices.…
PIM-MMU: A Memory Management Unit for Accelerating Data Transfers in Commercial PIM Systems
Dongjae Lee, Bongjoon Hyun, Taehun Kim +1
Processing-in-memory (PIM) has emerged as a promising solution for accelerating memory-intensive workloads as they provide high memory bandwidth to the processing units. This appro…
PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models
Yunjae Lee, Hyeseong Kim, Minsoo Rhu
Training recommendation systems (RecSys) faces several challenges as it requires the "data preprocessing" stage to preprocess an ample amount of raw data and feed them to the GPU f…
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology
Bongjoon Hyun, Taehun Kim, Dongjae Lee +1
Processing-in-memory (PIM) has been explored for decades by computer architects, yet it has never seen the light of day in real-world products due to their high design overheads an…
DiVa: An Accelerator for Differentially Private Machine Learning
Beomsik Park, Ranggi Hwang, Dongho Yoon +2
The widespread deployment of machine learning (ML) is raising serious concerns on protecting the privacy of users who contributed to the collection of training data. Differential p…
SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures
Yunjae Lee, Jinha Chung, Minsoo Rhu
Graph neural networks (GNNs) can extract features by learning both the representation of each objects (i.e., graph nodes) and the relationship across different objects (i.e., the e…
Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards
Youngeun Kwon, Minsoo Rhu
Personalized recommendation models (RecSys) are one of the most popular machine learning workload serviced by hyperscalers. A critical challenge of training RecSys is its high memo…
GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks
Ranggi Hwang, Minhoo Kang, Jiwon Lee +3
Graph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational. A unique property of GCNs is that its…
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training
Youngeun Kwon, Yunjae Lee, Minsoo Rhu
Personalized recommendations are one of the most widely deployed machine learning (ML) workload serviced from cloud datacenters. As such, architectural solutions for high-performan…
NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units
Bongjoon Hyun, Youngeun Kwon, Yujeong Choi +2
To satisfy the compute and memory demands of deep neural networks, neural processing units (NPUs) are widely being utilized for accelerating deep learning algorithms. Similar to ho…