The push towards fielding autonomous-driving capabilities in vehicles is happening at breakneck speed. Semi-autonomous features are becoming increasingly common, and fully autonomous vehicles are optimistically forecast to be widely available in just a few years. Today, graphics processing units (GPUs) are seen as a key technology in this push towards greater autonomy. However, realizing full autonomy in mass-production vehicles will necessitate the use of stringent certification processes. Currently available GPUs pose challenges in this regard, as they tend to be closed-source “black boxes” that have features that are not publicly disclosed. For certification to be tenable, such features must be documented. This paper reports on such a documentation effort. This effort was directed at the NVIDIA TX2, which is one of the most prominent GPU-enabled platforms marketed today for autonomous systems. In this paper, important aspects of the TX2's GPU scheduler are revealed as discerned through experimental testing and validation.
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Amert et al. (2017) studied this question.
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