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Nvidia To Halt AV Fleet Testing As Uber Investigates Fatal Crash

Nvidia To Halt AV Fleet Testing As Uber Investigates Fatal Crash

In front of almost 8,000 attendees at the San Jose Convention center in a two-and-a-half hour keynote, Nvidia CEO Jensen Huang announced that the company is upgrading its Tesla V100 products (SXM and PCIe modules) to now have 32 GB of memory each, a 2X boost, to help data scientists train deeper larger models and to boost the performance of memory-constrained HPC applications.

While Nvidia may be best-known for their popular GeForce line of gaming GPUs, those are far from the only products the company sells. There are many other possible configurations of switch topologies that are possible and we will be exploring them in a future article. "We view the automotive pause as temporary and continue to view Nvidia positively for gaming, virtual reality, auto and artificial intelligence".

This new offering from the GPU giant delivers a staggering performance with 2 petaflops processing power, 512GB RAM, and 10 kilowatts power input. "Penguin Computing has made high performance computing a priority designing GPU solutions into vertical markets ranging from oil and gas and higher education to government/defense and healthcare". "We spared no expense on the design of this to make sure we would never be limited by GPU to GPU communication". Having multiple servers helps with this.

The first server runs NVIDIA DRIVE Sim software to simulate a self-driving vehicle's sensors, such as cameras, lidar and radar.

Uber began using NVIDIA's GPUs for its autonomous cars with its initial test fleet, based on Volvo's XC90 SUV. It would appear there is a significant speed bump along that particular road. This "hardware-in-the-loop" cycle, which occurs 30 times a second, is used to validate that algorithms and software running on Pegasus are operating the simulated vehicle correctly.

The technology is created to allow automakers and others to validate their technology on billions of driving miles and increase the strength of their algorithms by repeatedly testing hard scenarios, which would be impractical in the real world.

"We are proud to be able to combine Penguin Computing's customized, Linux high-performance computing (HPC) capabilities with the latest Volta GPU architecture from NVIDIA and enable data scientists, researchers and engineers to tackle AI and computing challenges that were once impossible", said Tom Coull, CEO at Penguin Computing.

Nvidia also explained why the accident led the company to suspend its testing."It's a reminder of "how hard [self-driving car] technology is". It can simulate different weather such as rainstorms and snowstorms; blinding glare at different times of the day, or limited vision at night; and all different types of road surfaces and terrain.

"Dangerous situations can be scripted in simulation to test the autonomous car's ability to react", NVIDIA explains, "without ever putting anyone in harm's way".

"Autonomous vehicles need to be developed with a system that covers training to testing to driving." siad Luca De Ambroggi, research and analyst at IHS Markit.

The price tag for the DGX-2 server is $399,000; availability is scheduled for the third quarter.

"Many of these advances stand on Nvidia's deep learning platform, which has quickly become the world's standard", Huang added.



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