Hierarchical deep learning architecture. Tesla’s self-driving team needed a very efficient and well-designed neural network to make the most out of the high-quality dataset they had gathered.

Tesla didn’t just build a supercomputer—they reimagined how it works from the ground up. In this video, we take a deep dive into DOJO, Tesla’s custom-built AI supercomputer designed to power the ...

Nvidia's new GPUs called the Tesla P40 and P4 are built for those kind of deep-learning systems that aid in correlation and classification of data.

And now it is announcing two more deep-learning Tesla chips today at an event in China. Jen-Hsun Huang, CEO of Nvidia, announced the Tesla P4 and Tesla P40 graphics processing units (GPU) at the ...

Ditto for deep learning, where double precision floating point is not particularly useful. In this case, the Maxwell-based Tesla M4 and M40 GPUs can be offered at a very attractive price for those ...

FREMONT, Calif., Sept. 28, 2017 /PRNewswire/ -- AMAX, a leading provider of Deep Learning, HPC, Cloud/IaaS servers and appliances, today announced that its GPU solutions, including Deep Learning ...

Nvidia announced that its PCIe Tesla P100 GPU for servers will be on the shelves this year, and the preliminary tests are promising. The Pascal architecture that powers the GPU should yield ferocious ...

"With the Tesla P100 and now Tesla P4 and P40, NVIDIA offers the only end-to-end deep learning platform for the data center, unlocking the enormous power of AI for a broad range of industries ...

It announced TensorRT, a compiler for deep learning frameworks TensorFlow and Caffe to improve inference performance. Nvidia said inference on the Tesla V100 is 15 to 25 times faster than Intel's ...

To train their deep learning architecture, the Tesla team needed a massive dataset of millions of videos, carefully annotated with the objects they contain and their properties.