Are Self-Driving Cars Reliable? Evaluation of Radiation-Induced Errors in GPUs for Automotive Applications
With Professor Paolo Rech - Federal University of Rio Grande do Sul, Brazil
To be implemented, a self-driving platform needs to be able to analyze a huge amount of images and signals in real time.
Graphics Processing Units (GPUs), thanks to their low cost, increased energy efficiency, and flexible development platforms, are extremely attractive for the automotive market.
The Tesla self-driving system, for instance, is powered by NVIDIA embedded GPUs. GPUs were originally designed for multimedia applications, for which reliability is not an issue.
Their architecture is then optimized to increase performances, not reliability. In the talk we will discuss the reliability of GPUs and evaluate if they are compliant with the strict ISO 26262, which is the standard that define the reliability constraints for automotive applications.
The talk will focus on the reliability of pedestrian-detection algorithm and convolution neutral networks (including YOLO and Faster RCNN). We will understand how to identify radiation-induced errors in GPUs and distinguish between tolerable errors and critical errors.
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