no code implementations • NeurIPS 2021 • Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeong-In Yu, Byung-Gon Chun
Recently, several systems have been proposed to combine the usability of imperative programming with the optimized performance of symbolic graph execution.
1 code implementation • NeurIPS 2020 • Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun
Ideally, DL frameworks should be able to fully utilize the computation power of GPUs such that the running time depends on the amount of computation assigned to GPUs.
no code implementations • 24 Nov 2019 • Ahnjae Shin, Dong-Jin Shin, Sungwoo Cho, Do Yoon Kim, Eunji Jeong, Gyeong-In Yu, Byung-Gon Chun
As deep learning techniques advance more than ever, hyper-parameter optimization is the new major workload in deep learning clusters.
no code implementations • 4 Dec 2018 • Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dong-Jin Shin, Byung-Gon Chun
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 support straightforward programming models for quickly implementing and experimenting with complex network structures.
no code implementations • 4 Sep 2018 • Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun
Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data.
1 code implementation • 8 Aug 2018 • Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun
The employment of high-performance servers and GPU accelerators for training deep neural network models have greatly accelerated recent advances in machine learning (ML).
Distributed, Parallel, and Cluster Computing