2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
From Block to Byte: Transforming PCIe SSDs with CXL Memory Protocol and Instruction Annotation
Miryeong Kwon, Donghyun Gouk, Junhyeok Jang +8
This paper explores how Compute Express Link (CXL) can transform PCIe-based block storage into a scalable, byte-addressable working memory. We address the challenges of adapting bl…
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…
GraphTensor: Comprehensive GNN-Acceleration Framework for Efficient Parallel Processing of Massive Datasets
Junhyeok Jang, Miryeong Kwon, Donghyun Gouk +2
We present GraphTensor, a comprehensive open-source framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use pr…
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…