VLDB 2026 Research / reviewers in the wild / expert
Mahla Salehi Sheikhali Kelayeh
dblp:415/9704
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0006-2087-6706ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 first-author · 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 |
Emerging computing paradigms · 61% Reconfigurable computing and FPGAs · 30% Hardware accelerators and domain-specific architectures · 9% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Emerging computing paradigms
approximate computing |
1.0 | 1 | 2026 | LUTEA: LUT-Based Energy-Efficient Approximate Multiplier for More Sustainable Neural Network Applications · IEEE Trans. Computers 2026 |
Emerging computing paradigms › approximate computing
approximate multiplier |
1.0 | 1 | 2026 | LUTEA: LUT-Based Energy-Efficient Approximate Multiplier for More Sustainable Neural Network Applications · IEEE Trans. Computers 2026 |
Hardware accelerators and domain-specific architectures › machine learning accelerator › neural network accelerator
energy-efficient neural network accelerator |
0.3 | 1 | 2026 | LUTEA: LUT-Based Energy-Efficient Approximate Multiplier for More Sustainable Neural Network Applications · IEEE Trans. Computers 2026 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | ARTS: An approximate reduced tree and segmentation-based multiplier
Mahla Salehi Sheikhali Kelayeh, Sahand Divsalar, Shaghayegh Vahdat, Nima Taherinejad |
Future Gener. Comput. Syst. | 1 |
| 2026 | LUTEA: LUT-Based Energy-Efficient Approximate Multiplier for More Sustainable Neural Network Applications
Mahla Salehi Sheikhali Kelayeh, Sahand Divsalar, Shaghayegh Vahdat, Nima Taherinejad |
IEEE Trans. Computers | 1 |