VLDB 2026 Research / reviewers in the wild / expert
Ali Asgari Khoshouyeh
dblp:359/5865
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
0009-0006-7193-075XORCID · reported
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 |
Hardware reliability and fault tolerance · 62% Hardware accelerators and domain-specific architectures · 38% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware reliability and fault tolerance
soft errors |
0.7 | 1 | 2023 | Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults · SC 2023 |
Hardware accelerators and domain-specific architectures › machine learning accelerator
CNN reliability |
0.2 | 1 | 2023 | Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults · SC 2023 |
Hardware accelerators and domain-specific architectures
machine learning accelerator |
0.2 | 1 | 2023 | Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults · SC 2023 |
Methods — techniques the papers use, named apart from their topics
structural coding · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory FaultsabstractThe advent of High-Performance Computing has led to the adoption of Convolutional Neural Networks (CNNs) in safety-critical applications such as autonomous vehicles. However, CNNs are vulnerable to DRAM errors corrupting their parameters, thereby degrading their accuracy. Existing techniques for protecting CNNs from DRAM errors are either expensive or fail to protect from large-granularity, multi-bit errors, which occur commonly in DRAMs. Ali Asgari Khoshouyeh, Florian Geissler, Syed Sha Qutub, Michael Paulitsch, Prashant J. Nair, Karthik Pattabiraman |
SC | 1 |