Demo Data from VLDB 2026 / reviewers in the wild / expert
Chengxi Li 0023
dblp:415/5591
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author · 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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Electronic design automation
logic synthesis |
0.9 | 1 | 2025 | SmaRTLy: RTL Optimization with Logic Inferencing and Structural Rebuilding · DAC 2025 |
Electronic design automation › logic synthesis › digital system synthesis
RTL optimization |
0.9 | 1 | 2025 | SmaRTLy: RTL Optimization with Logic Inferencing and Structural Rebuilding · DAC 2025 |
Methods — techniques the papers use, named apart from their topics
structural rebuilding · 0.9AIG optimization · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | SmaRTLy: RTL Optimization with Logic Inferencing and Structural RebuildingabstractThis paper proposes smaRTLy: a new optimization technique for multiplexers in Register-Transfer Level (RTL) logic synthesis. Multiplexer trees are very common in RTL designs, and traditional tools like Yosys optimize them by traversing the tree and monitoring control port values. However, this method does not fully exploit the intrinsic logical relationships among signals or the potential for structural optimization. To address these limitations, we develop innovative strategies to remove redundant multiplexer trees and restructure the remaining ones, significantly reducing the overall gate count. We evaluate smaRTLy on the IWLS-2005 and RISC-V benchmarks, achieving an additional 8.95% reduction in AIG area compared to Yosys. We also evaluate smaRTLy on an industrial benchmark in the scale of millions of gates, results show that smaRTLy can remove 47.2% more AIG area than Yosys. These results demonstrate the effectiveness of our logic inferencing and structural rebuilding techniques in enhancing the RTL optimization process, leading to more efficient hardware designs. Chengxi Li 0023, Mingxuan Yuan, Evangeline F. Y. Young |
DAC | 1 |