170 citations · 218 across the 9 of their papers we have counts for
9 papers
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
Taebum Kim, Eunji Jeong, Geon-Woo Kim +4
Imperative programming allows users to implement their deep neural networks (DNNs) easily and has become an essential part of recent deep learning (DL) frameworks. Recently, severa…
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…
A Tensor Compiler for Unified Machine Learning Prediction Serving
Supun Nakandala, Karla Saur, Gyeong-In Yu +4
Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure---the bespoke solutions typical in large web companies are simply unten…
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…
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…
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…