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How Tesla is Using a Supercomputer to Train its Self-Driving Tech

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A Tesla Model S.

The supercomputer cluster has 5,760 GPUsprocessing power it needs to help power its self-driving aspirations.

Credit: Tesla

You can't buy a fully self-driving car today, but automakers around the globe are racing to become the first company to place such a vehicle on dealer lots. No two companies are taking the same technological path to achieve this plan, either. Some make use of remote sensing methods like Light Detection and Ranging (LiDAR), while others rely on radar-based sensors to help pick out hard-to-see obstacles in the roadway. And typically, firms working on autonomous tech will use a combination of LiDAR, radar, and cameras.

Then there's Tesla, which believes vision-based image recognition using only cameras is the key to affordable and reliable autonomy.

But there's a catch to Tesla's method: perfecting vision-based autonomy is difficult. It requires the use of a continuously improving system that can quickly adapt to new and changing road conditions, and then it must be capable of sharing that information with other vehicles on the roadway. That kind of learning takes significantly more processing power than what is available in a single vehicle—it takes a supercomputer.

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