7 citations · 13 across the 5 of their papers we have counts for
5 papers
AutoGNN: End-to-End Hardware-Driven Graph Preprocessing for Enhanced GNN Performance
Seungkwan Kang, Seungjun Lee, Donghyun Gouk +9
Graph neural network (GNN) inference faces significant bottlenecks in preprocessing, which often dominate overall inference latency. We introduce AutoGNN, an FPGA-based accelerator…
CXL-GPU: Pushing GPU Memory Boundaries with the Integration of CXL Technologies
Donghyun Gouk, Seungkwan Kang, Seungjun Lee +8
This work introduces a GPU storage expansion solution utilizing CXL, featuring a novel GPU system design with multiple CXL root ports for integrating diverse storage media (DRAMs a…
Failure Tolerant Training with Persistent Memory Disaggregation over CXL
Miryeong Kwon, Junhyeok Jang, Hanjin Choi +2
This paper proposes TRAININGCXL that can efficiently process large-scale recommendation datasets in the pool of disaggregated memory while making training fault tolerant with low o…
Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs
Miryeong Kwon, Donghyun Gouk, Sangwon Lee +1
Graph neural networks (GNNs) process large-scale graphs consisting of a hundred billion edges. In contrast to traditional deep learning, unique behaviors of the emerging GNNs are e…
An Effective Pipeline for a Real-world Clothes Retrieval System
Yang-Ho Ji, HeeJae Jun, Insik Kim +7
In this paper, we propose an effective pipeline for clothes retrieval system which has sturdiness on large-scale real-world fashion data. Our proposed method consists of three comp…