NVIDIA Builds Supercomputer to Develop Self-Driving Cars

NVIDIA Builds Supercomputer to Develop Self-Driving Cars
Nvidia's DGX SuperPOD
Published: 18 June 2019 - 11:18 a.m.

Nvidia have today unveiled the world’s 22nd fastest supercomputer — DGX SuperPOD — which provides AI infrastructure that meets the massive demands of the company’s autonomous-vehicle deployment program.

Nvidia developing self driving tech makes sense, given the companies capabilites to develop the most powerful GPUs. The DGX system was built in just three weeks with 96 NVIDIA DGX-2H supercomputers and Mellanox interconnect technology. Delivering 9.4 petaflops of processing capability, it has the muscle for training the vast number of deep neural networks required for safe self-driving vehicles.

A single data-collection vehicle generates 1 terabyte of data per hour. Multiply that by years of driving over an entire fleet, and you quickly get to petabytes of data. That data is used to train algorithms on the rules of the road — and to find potential failures in the deep neural networks operating in the vehicle, which are then re-trained in a continuous loop.
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“AI leadership demands leadership in compute infrastructure,” said Clement Farabet, vice president of AI infrastructure at NVIDIA. “Few AI challenges are as demanding as training autonomous vehicles, which requires retraining neural networks tens of thousands of times to meet extreme accuracy needs. There’s no substitute for massive processing capability like that of the DGX SuperPOD.”

Powered by 1,536 NVIDIA V100 Tensor Core GPUs interconnected with NVIDIA NVSwitch and Mellanox network fabric, the DGX SuperPOD can tackle data with performance for a supercomputer its size.

The system is hard at work around the clock, optimizing autonomous driving software and retraining neural networks at a much faster turnaround time than previously possible.

NVIDIA DGX systems have already been adopted by other organizations with massive computational needs of their own ranging from automotive companies such as BMW, Continental, Ford and Zenuity to enterprises including Facebook, Microsoft and Fujifilm, as well as research leaders like Riken and U.S. Department of Energy national labs.


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