170 citations · 284 across the 12 of their papers we have counts for
3 papers · 1 filter
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
Woosuk Kwon, Gyeong-In Yu, Eunji Jeong +1
Deep learning (DL) frameworks take advantage of GPUs to improve the speed of DL inference and training. Ideally, DL frameworks should be able to fully utilize the computation power…
Accelerating Multi-Model Inference by Merging DNNs of Different Weights
Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu +2
Standardized DNN models that have been proved to perform well on machine learning tasks are widely used and often adopted as-is to solve downstream tasks, forming the transfer lear…
Hippo: Taming Hyper-parameter Optimization of Deep Learning with Stage Trees
Ahnjae Shin, Do Yoon Kim, Joo Seong Jeong +1
Hyper-parameter optimization is crucial for pushing the accuracy of a deep learning model to its limits. A hyper-parameter optimization job, referred to as a study, involves numero…