VLDB 2026 Research / reviewers in the wild / expert
Gunnar Hammonds
dblp:318/9010
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 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 |
Memory systems · 67% Hardware accelerators and domain-specific architectures · 33% | |
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems › memory access patterns
irregular memory access |
0.6 | 1 | 2022 | Crescent: taming memory irregularities for accelerating deep point cloud analytics · ISCA 2022 |
Memory systems
memory access optimization |
0.6 | 1 | 2022 | Crescent: taming memory irregularities for accelerating deep point cloud analytics · ISCA 2022 |
Hardware accelerators and domain-specific architectures › vision accelerator
point cloud analytics accelerator |
0.6 | 1 | 2022 | Crescent: taming memory irregularities for accelerating deep point cloud analytics · ISCA 2022 |
Computer vision › 3D vision
point cloud processing |
0.2 | 1 | 2022 | Crescent: taming memory irregularities for accelerating deep point cloud analytics · ISCA 2022 |
Methods — techniques the papers use, named apart from their topics
hardware acceleration · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Crescent: taming memory irregularities for accelerating deep point cloud analyticsabstract3D perception in point clouds is transforming the perception ability of future intelligent machines. Point cloud algorithms, however, are plagued by irregular memory accesses, leading to massive inefficiencies in the memory sub-system, which bottlenecks the overall efficiency. Yu Feng 0007, Gunnar Hammonds, Yiming Gan, Yuhao Zhu 0001 |
ISCA | 2 |