NVIDIA's CEO outlines how GPU advances enable AI

Source: NVIDIA blog post: Huang addresses the audience at SC16, an international conference for high performance computing, networking, storage and analysis.

The artificial intelligence (AI) boom will create a path to exascale computing, one of the supercomputing world’s loftiest goals, NVIDIA CEO Jen-Hsun Huang said in a blog post around mid-November.

“Several years ago deep learning came along, like Thor’s hammer falling from the sky, and gave us an incredibly powerful tool to solve some of the most difficult problems in the world,” Huang said. “Every industry has awoken to AI.”

2016 has been a great year for deep learning and GPU computing, he explained. There are now more than 400 GPU-optimised high-performance computing applications, and all of the top 10 applications are now GPU optimised. The number of deep learning developers has tripled in two years to 400,000. The launch of NVIDIA's Pascal GPU architecture means all these applications will run more quickly, and efficiently, than ever.

Huang also shared a number of announcements, including that Microsoft and NVIDIA had released the Microsoft Cognitive Toolkit. This is the first purpose-built enterprise AI framework that is optimised to run on NVIDIA Tesla GPUs with Microsoft Azure on-premise. “We’re partnering with the company with the largest reach with companies around the world, and we now have the ability to bring AI to companies all around the world,” Huang said.

The parallel processing power of GPUs give researchers the ability to design deep neural networks that loosely mimic the structure of the human mind. These deep neural networks give machines the ability to perceive — and understand — the world in ways that match or exceed our own.

At the same time, GPUs — driven forward by the vast economies of scale afforded by the market for PC gaming — give supercomputer scientists the ability to design machines that extract more power out of each unit of energy, key to creating machines with the ability to reach ever faster speeds on a realistic power budget.

“Deep learning is both an opportunity, as well as a challenge, that requires supercomputing,” Huang said. He predicted that the next generation of supercomputers will be able to do both kinds of work — performing 64-bit floating point math to tackle computational science challenges, such as predicting physical and biological behaviour, plus tackling tasks in which the information is incomplete.

Interested?

Read the TechTrade Asia blog post about the Microsoft Cognitive Toolkit
 

posted from Bloggeroid