Avionics Graphics Hardware Performance Prediction with Machine Learning

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.

Architecture of Graphics System with 3D Acceleration Support for Embedded Operating Systems

“…Reference [5] was devoted to the creation of devices using RTOS; however, applied approaches do not involve GPU for acceleration and performance of all calculations on the CPU. Notably, a previous study [6] described an approach to the creation of devices using embedded systems as well as ARINC 661 and OpenVG standards. Another study [7] presented the influence of various factors on the performance of aviation device visualization when using graphical API OpenGL ES and OpenGL SC.…”

“…This often requires the execution of queued operations on the GPU. (6) finish_cpu_access terminates access to the data buffer by the CPU.…”

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