125 citations · 136 across the 8 of their papers we have counts for
14 papers
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…
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…
PARIS and ELSA: An Elastic Scheduling Algorithm for Reconfigurable Multi-GPU Inference Servers
Yunseong Kim, Yujeong Choi, Minsoo Rhu
In cloud machine learning (ML) inference systems, providing low latency to end-users is of utmost importance. However, maximizing server utilization and system throughput is also c…
LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference
Yujeong Choi, Yunseong Kim, Minsoo Rhu
In cloud ML inference systems, batching is an essential technique to increase throughput which helps optimize total-cost-of-ownership. Prior graph batching combines the individual…
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…