Work-in-Progress Abstract: WKS, a local unsupervised statistical algorithm for the detection of transitions in timing analysis - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Work-in-Progress Abstract: WKS, a local unsupervised statistical algorithm for the detection of transitions in timing analysis

Abstract

The increased complexity of programs and pro-cessors is an important challenge that the embedded real-time systems community faces today, as it implies substancial timing variability. Processor features like pipelines or communication buses are not always completely described, while black-box programs integrated by third parties are hidden for IP reasons. This situation explains the use of statistical approaches to study the timing variability of programs. Most existing work is concentrated on the guarantees provided by positive answers to statistical tests, while our current work concerns potential algorithms based on the negative answers to these tests and their impact on the timing analysis. We introduce here one such algorithm, the Walking Kolmogorov-Smirnov test (WKS).
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hal-03537554 , version 1 (20-01-2022)

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Liliana Cucu-Grosjean, Marwan Wehaiba El Khazen, Adriana Gogonel, Hadrien Clarke, Yves Sorel. Work-in-Progress Abstract: WKS, a local unsupervised statistical algorithm for the detection of transitions in timing analysis. RTCSA 2021 - IEEE 27th International Conference on Embedded and Real-Time Computing Systems and Applications, Aug 2021, Online, France. pp.201-203, ⟨10.1109/RTCSA52859.2021.00032⟩. ⟨hal-03537554⟩

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