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