Demo Data from EDBT 2026 / reviewers in the wild / expert
Haiyuan Su
dblp:327/2196
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 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 |
Electronic design automation · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Electronic design automation
physical design |
0.6 | 1 | 2022 | Subgraph matching based reference placement for PCB designs: late breaking results · DAC 2022 |
Electronic design automation › physical design
placement |
0.6 | 1 | 2022 | Subgraph matching based reference placement for PCB designs: late breaking results · DAC 2022 |
Electronic design automation › physical design
printed circuit board design |
0.2 | 1 | 2022 | Subgraph matching based reference placement for PCB designs: late breaking results · DAC 2022 |
Methods — techniques the papers use, named apart from their topics
subgraph matching · 0.6branch backtracking · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Subgraph matching-based reference placement for printed circuit board designs
Ziran Zhu, Miaodi Su, Haiyuan Su, Yifeng Xiao, Jianli Chen, Yao-Wen Chang |
J. Supercomput. | 5 |
| 2022 | Subgraph matching based reference placement for PCB designs: late breaking resultsabstractReference placement is promising to handle the increasing complexity in PCB design. We model the netlist into a graph and use a subgraph matching algorithm to find the isomorphism of the placed template in component combination to reuse the placement. The state-of-the-art VF3 algorithm can achieve high matching accuracy while suffering from high computation time in large-scale instances. Thus, we propose the D2BS algorithm to guarantee matching quality and efficiency. We build and filter the candidate set (CS) according to designed features to construct the CS structure. In the CS optimization, a graph diversity tolerance strategy is adopted to achieve inexact matching. Then, hierarchical match is developed to search the template embeddings in the CS structure guided by branch backtracking and matched nodes snatching. Experimental results show that D2BS outperforms VF3 in accuracy and runtime, achieving 100% accuracy on PCB instances. Miaodi Su, Yifeng Xiao, Haiyuan Su, Ziran Zhu, Jianli Chen, Yao-Wen Chang |
DAC | 4 |