Research

Research Highlights

Research

Research Highlights

Research Highlights

KAIST EE Prof. Myoungsoo Jung’s team, Memorable Paper Award from NVMW

[Prof. Myoungsoo Jung, Miryeong Kwon , Donghyun Gouk, from left]

 

KAIST EE department’s Professor Myoungsoo Jung’s research team(Miryeong Kwon — first author, Donghyun Gouk, Sangwon Lee) has won the Memorable Paper Award from NVMW (Non-Volatile Memories Workshop) 2022 for their paper “HolisticGNN: Geometric Deep Learning Engines for Computational SSDs”.
 
The NVMW memorable paper award is one of the prestige awards in non-volatile memory areas. It selects two papers published in the past two years in top-tier venues and journals such as OSDI, SOSP, FAST, ISCA, MICRO, ASPLOS, and ATC. Among them, NVMW committee members not only examine the quality of all the top venue papers and corresponding presentations, but also check the significant impact on non-volatile memory research fields. 
 
Founded in 2010, NVMW is a non-volatile memory workshop held annually by the Center for Memory and Recording Research(CMRR) and Non-Volatile Systems Laboratory (NVSL). For the past 13 years, there have been nine NVMW memorable paper awards. 
 
PhD candidate Miryeong Kwon (Advised by Myoungsoo Jung) has published a paper titled “HolisticGNN: Geometric Deep Learning Engines for Computational SSDs” and was selected as a winner for its excellence among all the candidates this year, and she also got $1000 cash prize. It is the first award that KAIST has achieved.
 
This work deals with in-storage processing for large-scale GNN (graph neural network), utilizing an FPGA based computational SSD (CSSD) architecture and machine learning framework. Basically, it performs preprocessing of GNN in storage such as graph conversion, sampling, etc., and accelerates inference procedures over reconfigurable hardware. The team fabricates HolisticGNN’s hardware RTL and implements its software on an FPGA-based CSSD as well.
 
The research was supported by the Samsung Science & Technology Foundation. More information on this paper can be found at http://camelab.org.