A $2 Billion Chip to Accelerate Artificial Intelligence
The field of artificial intelligence has experienced a striking spurt of progress in recent years, with software becoming much better at understanding images, speech, and new tasks such as how to play games. Now the company whose hardware has underpinned much of that progress has created a chip to keep it going.
On Tuesday Nvidia announced a new chip called the Tesla P100 that’s designed to put more power behind a technique called deep learning. This technique has produced recent major advances such as the Google software AlphaGo that defeated the world’s top Go player last month (see “Five Lessons from AlphaGo’s Historic Victory”).
Deep learning involves passing data through large collections of crudely simulated neurons. The P100 could help deliver more breakthroughs by making it possible for computer scientists to feed more data to their artificial neural networks or to create larger collections of virtual neurons.
Artificial neural networks have been around for decades, but deep learning only became relevant in the last five years, after researchers figured out that chips originally designed to handle video-game graphics made the technique much more powerful. Graphics processors remain crucial for deep learning, but Nvidia CEO Jen-Hsun Huang says that it is now time to make chips customized for this use case.
At a company event in San Jose, he said, “For the first time we designed a [graphics-processing] architecture dedicated to accelerating AI and to accelerating deep learning.” Nvidia spent more than $2 billion on R&D to produce the new chip, said Huang. It has a total of 15 billion transistors, roughly three times as many as Nvidia’s previous chips. Huang said an artificial neural network powered by the new chip could learn from incoming data 12 times as fast as was possible using Nvidia's previous best chip.
Deep-learning researchers from Facebook, Microsoft, and other companies that Nvidia granted early access to the new chip said they expect it to accelerate their progress by allowing them to work with larger collections of neurons.
“I think we’re going to be able to go quite a bit larger than we have been able to in the past, like 30 times bigger,” said Bryan Catanzero, who works on deep learning at the Chinese search company Baidu. Increasing the size of neural networks has previously enabled major jumps in the smartness of software. For example, last year Microsoft managed to make software that beats humans at recognizing objects in photos by creating a much larger neural network.
Huang of Nvidia said that the new chip is already in production and that he expects cloud-computing companies to start using it this year. IBM, Dell, and HP are expected to sell it inside servers starting next year.
He also unveiled a special computer for deep-learning researchers that packs together eight P100 chips with memory chips and flash hard drives. Leading academic research groups, including ones at the University of California, Berkeley, Stanford, New York University, and MIT, are being given models of that computer, known as the DGX-1, which will also be sold for $129,000.
How Rust went from a side project to the world’s most-loved programming language
For decades, coders wrote critical systems in C and C++. Now they turn to Rust.
Welcome to the oldest part of the metaverse
Ultima Online, which just turned 25, offers a lesson in the challenges of building virtual worlds.
A new paradigm for managing data
Open data lakehouse architectures speed insights and deliver self-service analytics capabilities.
Three ways networking services simplify network management
The right networking services orchestrate note-perfect network performance.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.