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Xinbin Liu

dblp:119/3181 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 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
Embedded and real-time systems · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Embedded and real-time systems › real-time scheduling
cause-effect chain
0.612022
Efficient maximum data age analysis for cause-effect chains in automotive systems · DAC 2022
Embedded and real-time systems
real-time scheduling
0.612022
Efficient maximum data age analysis for cause-effect chains in automotive systems · DAC 2022

Methods — techniques the papers use, named apart from their topics

interval bounding · 0.6formal analysis · 0.6
YearPublicationVenuePosition
2022 Efficient maximum data age analysis for cause-effect chains in automotive systems
abstract
Automotive systems are often subjected to stringent requirements on the maximum data age of certain cause-effect chains. In this paper, we present an efficient method for formally analyzing maximum data age of cause-effect chains. In particular, we decouple the problem of bounding the maximum data age of a chain into a problem of bounding the releasing interval of successive Last-to-Last data propagation instances in the chain. Owing to the problem decoupling, a relatively tighter data age upper bound can be effectively obtained in polynomial time. Experiments demonstrate that our approach can achieve high precision analysis with lower computational cost.
Ran Bi 0001, Xinbin Liu, Jiankang Ren, Pengfei Wang 0013, Huawei Lv, Guozhen Tan
DAC2