An Evaluation of the NVIDIA TX1 for Supporting Real-Time Computer-Vision Workloads

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Abstract

Autonomous vehicles are an exemplar for forward-looking safety-critical real-time systems where significant computing capacity must be provided within strict size, weight, and power (SWaP) limits. A promising way forward in meeting these needs is to leverage multicore platforms augmented with graphics processing units (GPUs) as accelerators. Such an approach is being strongly advocated by NVIDIA, whose Jetson TX1 board is currently a leading multicore+GPU solution marketed for autonomous systems. Unfortunately, no study has ever been published that expressly evaluates the effectiveness of the TX1, or any other comparable platform, in hosting safety-critical real-time workloads. In this paper, such a study is presented. Specifically, the TX1 is evaluated via benchmarking efforts, blackbox evaluations of GPU behavior, and case-study evaluations involving computer-vision workloads inspired by autonomousdriving use cases. Autonomous vehicles are an exemplar for forward-looking safety-critical real-time systems where significant computing capacity must be provided within strict size, weight, and power (SWaP) limits. A promising way forward in meeting these needs is to leverage multicore platforms augmented with graphics processing units (GPUs) as accelerators. Such an approach is being strongly advocated by NVIDIA, whose Jetson TX1 board is currently a leading multicore+GPU solution marketed for autonomous systems. Unfortunately, no study has ever been published that expressly evaluates the effectiveness of the TX1, or any other comparable platform, in hosting safety-critical real-time workloads. In this paper, such a study is presented. Specifically, the TX1 is evaluated via benchmarking efforts, blackbox evaluations of GPU behavior, and case-study evaluations involving computer-vision workloads inspired by autonomous-driving use cases.

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