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Avionics graphics hardware performance prediction with machine learning

Simon R. Girard, Vincent Legault, Guy Bois and Jean-François Boland

Article (2019)

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Cite this document: Girard, S. R., Legault, V., Bois, G. & Boland, J.-F. (2019). Avionics graphics hardware performance prediction with machine learning. Scientific Programming, 2019. doi:10.1155/2019/9195845
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Abstract

Within the strongly regulated avionic engineering field, conventional graphical desktop hardware and software application programming interface (API) cannot be used because they do not conform to the avionic certification standards. We observe the need for better avionic graphical hardware, but system engineers lack system design tools related to graphical hardware. The endorsement of an optimal hardware architecture by estimating the performance of a graphical software, when a stable rendering engine does not yet exist, represents a major challenge. As proven by previous hardware emulation tools, there is also a potential for development cost reduction, by enabling developers to have a first estimation of the performance of its graphical engine early in the development cycle. In this paper, we propose to replace expensive development platforms by predictive software running on a desktop computer. More precisely, we present a system design tool that helps predict the rendering performance of graphical hardware based on the OpenGL Safety Critical API. First, we create nonparametric models of the underlying hardware, with machine learning, by analyzing the instantaneous frames per second (FPS) of the rendering of a synthetic 3D scene and by drawing multiple times with various characteristics that are typically found in synthetic vision applications. The number of characteristic combinations used during this supervised training phase is a subset of all possible combinations, but performance predictions can be arbitrarily extrapolated. To validate our models, we render an industrial scene with characteristic combinations not used during the training phase and we compare the predictions to those real values. We find a median prediction error of less than 4 FPS.

Open Access document in PolyPublie
Subjects: 2700 Technologie de l'information > 2719 Architecture d'ordinateur et conception
Department: Département de génie informatique et génie logiciel
Date Deposited: 10 May 2022 09:25
Last Modified: 11 May 2022 01:20
PolyPublie URL: https://publications.polymtl.ca/4895/
Document issued by the official publisher
Journal Title: Scientific Programming (vol. 2019)
Publisher: Hindawi
Official URL: https://doi.org/10.1155/2019/9195845

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