Demo Data from VLDB 2026 / reviewers in the wild / expert
Igor Lima de Paula
dblp:289/1274
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Computer networks · 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 networks
2 papers |
Routing and switching · 72% Software-defined and programmable networks · 28% | |
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
Hardware accelerators and domain-specific architectures · 79% Memory systems · 21% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Routing and switching › IP lookup
longest prefix matching |
0.7 | 1 | 2023 | NeuroLPM - Scaling Longest Prefix Match Hardware with Neural Networks · MICRO 2023 |
Routing and switching
packet forwarding |
0.7 | 1 | 2023 | NeuroLPM - Scaling Longest Prefix Match Hardware with Neural Networks · MICRO 2023 |
Hardware accelerators and domain-specific architectures
network accelerator |
0.7 | 1 | 2023 | NeuroLPM - Scaling Longest Prefix Match Hardware with Neural Networks · MICRO 2023 |
Software-defined and programmable networks
programmable data plane |
0.6 | 1 | 2022 | SwiSh: Distributed Shared State Abstractions for Programmable Switches · NSDI 2022 |
Memory systems › shared memory
distributed shared memory |
0.2 | 1 | 2022 | SwiSh: Distributed Shared State Abstractions for Programmable Switches · NSDI 2022 |
Methods — techniques the papers use, named apart from their topics
prefix matching · 1.3neural network · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | NeuroLPM - Scaling Longest Prefix Match Hardware with Neural NetworksabstractLongest Prefix Match engines (LPM) are broadly used in computer systems and especially in modern network devices such as Network Interface Cards (NICs), switches and routers. However, existing LPM hardware fails to scale to millions of rules required by modern systems, is often optimized for specific applications, and thus is performance-sensitive to the structure of LPM rules. Alon Rashelbach, Igor Lima de Paula, Mark Silberstein |
MICRO | 2 |
| 2022 | SwiSh: Distributed Shared State Abstractions for Programmable Switches
Lior Zeno, Dan R. K. Ports, Jacob Nelson 0001, Daehyeok Kim, Shir Landau Feibish, Idit Keidar, Arik Rinberg, Alon Rashelbach, Igor Lima de Paula, Mark Silberstein |
NSDI | 9 |