Demo Data from VLDB 2026 / reviewers in the wild / expert
Dean Wu
dblp:180/3665
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1Databases, data management, data science and information retrieval · 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 |
Electronic design automation · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Electronic design automation › physical design › clock network synthesis
clock network optimization |
0.2 | 1 | 2016 | Flip-flop clustering by weighted K-means algorithm · DAC 2016 |
Electronic design automation › clustering
flip-flop clustering |
0.2 | 1 | 2016 | Flip-flop clustering by weighted K-means algorithm · DAC 2016 |
Electronic design automation
physical design |
0.2 | 1 | 2016 | Flip-flop clustering by weighted K-means algorithm · DAC 2016 |
Electronic design automation › physical design
placement |
0.2 | 1 | 2016 | Flip-flop clustering by weighted K-means algorithm · DAC 2016 |
Methods — techniques the papers use, named apart from their topics
weighted k-means · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Dynamic Reasoning with Language Model and Knowledge Graph for Question Answering
Dean Wu |
ICDAR (4) | 2 |
| 2016 | Flip-flop clustering by weighted K-means algorithmabstractThis paper presents a novel flip-flop clustering and relocation framework to help reduce the overall chip power consumption. Given an initial legalized placement, our goal is to reduce the wirelength of the clock network by reducing distance between flip-flops and their drivers, while minimize the disturbance of original placement result. The idea is to form flip-flops into clusters, such that all flip-flops within each cluster can be placed near a single clock buffer and connected by a simple routing structure. Therefore, overall clock network wirelength can be greatly reduced and significant power savings can be achieved. In particular, we propose a modified K-means algorithm which effectively assigns flops into clusters at the clustering step. Then, at the relocation step, flops are actually relocated and regularly structured clusters are formed. Our framework is evaluated on real industrial benchmarks. We compare our framework with a flow without flop clustering and an industrial window based flop clustering flow. Experimental results show our framework can achieve significant dynamic power savings while has less disturbance of the original placement. Gang Wu 0002, Dean Wu, Manoj Ragupathy, Yu-Yen Mo, Chris C. N. Chu |
DAC | 3 |