Demo Data from VLDB 2026 / reviewers in the wild / expert
Cody Jones
dblp:155/6444
· 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.
| Theoretical computer science
1 paper |
Quantum computing and quantum information · 56% Coding theory · 44% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Hardware accelerators and domain-specific architectures · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware accelerators and domain-specific architectures › video coding accelerator
decoder accelerator |
0.6 | 1 | 2022 | LILLIPUT: a lightweight low-latency lookup-table decoder for near-term Quantum error correction · ASPLOS 2022 |
Coding theory › error-correcting codes
decoding |
0.6 | 1 | 2022 | LILLIPUT: a lightweight low-latency lookup-table decoder for near-term Quantum error correction · ASPLOS 2022 |
Quantum computing and quantum information
quantum error correction |
0.6 | 1 | 2022 | LILLIPUT: a lightweight low-latency lookup-table decoder for near-term Quantum error correction · ASPLOS 2022 |
Quantum computing and quantum information › quantum error correction
syndrome extraction |
0.2 | 1 | 2022 | LILLIPUT: a lightweight low-latency lookup-table decoder for near-term Quantum error correction · ASPLOS 2022 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | LILLIPUT: a lightweight low-latency lookup-table decoder for near-term Quantum error correctionabstractThe error rates of quantum devices are orders of magnitude higher than what is needed to run most quantum applications. To close this gap, Quantum Error Correction (QEC) encodes logical qubits and distributes information using several physical qubits. By periodically executing a syndrome extraction circuit on the logical qubits, information about errors (called syndrome) is extracted while running programs. A decoder uses these syndromes to identify and correct errors in real time, which is necessary to prevent accumulation of errors. Unfortunately, software decoders are slow and hardware decoders are fast but less accurate. Thus, almost all QEC studies so far have relied on offline decoding. Poulami Das 0005, Aditya Locharla, Cody Jones |
ASPLOS | 3 |