Building Faithful High-level Models and Performance Evaluation of Manycore Embedded Systems - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

Building Faithful High-level Models and Performance Evaluation of Manycore Embedded Systems

Abstract

Performance and functional correctness are key for successful design of modern embedded systems. Both aspects must be considered early in the design process to enable founded decision making towards final implementation. Nonetheless, building abstract system-level models that faithfully capture performance information along to functional behavior is a challenging task. In contrast to functional aspects, performance details are rarely available during early design phases and no clear method is known to characterize them. Moreover, once such system-level models are built they are inherently complex as they usually mix software models, hardware architecture constraints and environment abstractions. Their analysis by using traditional performance evaluation methods is reaching the limits and the need for more scalable and accurate techniques is becoming urgent. In this paper, we introduce a systematic method for building stochastic abstract performance models using statistical inference and model calibration and we propose statistical model checking as performance evaluation technique upon the obtained models. We experimented our method on a real-life case study. We were able to verify different timing properties.
Fichier principal
Vignette du fichier
memocode14.pdf (751.38 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01087671 , version 1 (26-11-2014)

Identifiers

Cite

Ayoub Nouri, Marius Bozga, Anca Molnos, Axel Legay, Saddek Bensalem. Building Faithful High-level Models and Performance Evaluation of Manycore Embedded Systems. MEMOCODE, Oct 2014, Lausanne, Switzerland. ⟨10.1109/MEMCOD.2014.6961864⟩. ⟨hal-01087671⟩
556 View
290 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More