EDBT 2026 Demo / reviewers in the wild / expert
Linyong Huang
dblp:320/0757
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2022
0000-0003-4758-7702ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Reconfigurable computing and FPGAs · 77% Storage systems · 23% | |
| Artificial intelligence
1 paper |
Graph learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Graph learning
graph neural network |
0.6 | 1 | 2022 | Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network · ISCA 2022 |
Reconfigurable computing and FPGAs › cloud FPGA
FPGA-as-a-service |
0.6 | 1 | 2022 | Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network · ISCA 2022 |
Storage systems › distributed storage
distributed in-memory storage |
0.2 | 1 | 2022 | Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network · ISCA 2022 |
Methods — techniques the papers use, named apart from their topics
distributed training · 1.1FPGA acceleration · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural networkabstractGraph neural network (GNN) is a promising emerging application for link prediction, recommendation, etc. Existing hardware innovation is limited to single-machine GNN (SM-GNN), however, the enterprises usually adopt huge graph with large-scale distributed GNN (LSD-GNN) that has to be carried out with distributed in-memory storage. The LSD-GNN is very different from SM-GNN in terms of system architecture demand, workflow and operators, and hence characterizations. Shuangchen Li, Dimin Niu, Yuhao Wang 0002, Zhe Zhang 0006, Tianchan Guan, Yijin Guan, Linyong Huang, Zhaoyang Du, Yuanwei Fang, Hongzhong Zheng, Yuan Xie 0001 |
ISCA | 9 |
| 2022 | EPQuant: A Graph Neural Network compression approach based on product quantization
Linyong Huang, Zhe Zhang 0006, Zhaoyang Du, Shuangchen Li, Hongzhong Zheng, Yuan Xie 0001, Nianxiong Tan |
Neurocomputing | 1 |