170 citations · 284 across the 12 of their papers we have counts for
12 papers
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…
Stage-based Hyper-parameter Optimization for Deep Learning
Ahnjae Shin, Dong-Jin Shin, Sungwoo Cho +4
As deep learning techniques advance more than ever, hyper-parameter optimization is the new major workload in deep learning clusters. Although hyper-parameter optimization is cruci…
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim +4
Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network m…
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs
Eunji Jeong, Sungwoo Cho, Gyeong-In Yu +3
The rapid evolution of deep neural networks is demanding deep learning (DL) frameworks not only to satisfy the requirement of quickly executing large computations, but also to supp…
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Yunseong Lee, Alberto Scolari, Byung-Gon Chun +3
Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction ser…
Improving the Expressiveness of Deep Learning Frameworks with Recursion
Eunji Jeong, Joo Seong Jeong, Soojeong Kim +2
Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data. However, embedded control flow deep learni…
Coexistence of Intrinsic Superconductivity and Topological Insulator State in Monoclinic Phase WS2
Yuqiang Fang, Jie Pan, Dongqin Zhang +14
Recently, intriguing phenomena of superconductivity, type-II Weyl semimetal or quantum spin Hall states were discovered in metastable 1T'-type VIB-group transition metal dichalcoge…
Mantis: Predicting System Performance through Program Analysis and Modeling
Byung-Gon Chun, Ling Huang, Sangmin Lee +2
We present Mantis, a new framework that automatically predicts program performance with high accuracy. Mantis integrates techniques from programming language and machine learning f…
CloneCloud: Boosting Mobile Device Applications Through Cloud Clone Execution
Byung-Gon Chun, Sunghwan Ihm, Petros Maniatis +1
Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity wit…
A Data Capsule Framework For Web Services: Providing Flexible Data Access Control To Users
Jayanthkumar Kannan, Petros Maniatis, Byung-Gon Chun
This paper introduces the notion of a secure data capsule, which refers to an encapsulation of sensitive user information (such as a credit card number) along with code that implem…