NVIDIA's meteoric growth in the datacenter, where its business is now generating some $1.6B annually, has been largely driven by the demand to train deep neural networks for Machine Learning (ML) and Artificial Intelligence (AI)--an area where the computational requirements are simply mindboggling. Much of this business is coming from the largest datacenters in the US, including Amazon, Google, Facebook, IBM, and Microsoft. Recently, NVIDIA announced new technology and customer initiatives at its annual Beijing GTC event to help drive revenue in the inference market for Machine Learning, as well as solidify the company's position in the huge Chinese AI market. For those unfamiliar, inference is where the trained neural network is used to predict and classify sample data. It is likely that the inference market will eventually be larger, in terms of chip unit volumes, than the training market; after all, once you train a neural network, you probably intend to use it and use it a lot.
At this year's GPU Technology Conference, Nvidia's premier conference for technical computing with graphic processors, the company reserved the top keynote for its CEO Jensen Huang. Over the years, the GTC conference went from a segment in a larger, mostly gaming-oriented and somewhat scattershot conference called "nVision" to become one of the key conferences that mixes academic and commercial high-performance computing. Jensen's message was that GPU-accelerated machine learning is growing to touch every aspect of computing. While it's becoming easier to use neural nets, the technology still has a way to go to reach a broader audience. It's a hard problem, but Nvidia likes to tackle hard problems.
I started out writing a single blog on the coming year's expected AI chips, and how NVIDIA might respond to the challenges, but I quickly realized it was going to be much longer than expected. Since there is so much ground to cover, I've decided to structure this as three hopefully more consumable articles. I've included links to previous missives for those wanting to dig a little deeper. In the last five years, NVIDIA grew its data center business into a multi-billion-dollar juggernaut without once facing a single credible competitor. This is an amazing fact, and one that is unparalleled in today's technology world, to my recollection.
Embedded AI can transform a tabletop speaker into a personal assistant; give a robot brains and dexterity; and turn a smartphone into a smart camera, music player, or game console. Traditional processors, however, lack the computational power to support many of these intelligent features. Chipmakers, startups, and capital are taking this opportunity to the market. According to a Gartner report, the chip market's total revenue hit US$400 billion in 2017, and the figure is expected to exceed US$459 billion in 2018. Traditional chip makers are putting an increasing focus on AI chip development, venture capital is pumping significant investments into the market, and AI chip startups are emerging.
Today NVIDIA and Baidu today announced a broad partnership to bring the world's leading artificial intelligence technology to cloud computing, self-driving vehicles and AI home assistants. NVIDIA and Baidu have pioneered significant advances in deep learning and AI," said Ian Buck, NVIDIA vice president and general manager of accelerated computing. "We believe AI is the most powerful technology force of our time, with the potential to revolutionize every industry. Our collaboration aligns our exceptional technical resources to create AI computing platforms for all developers – from academic research, startups creating breakthrough AI applications, and autonomous vehicles." Speaking at Baidu's AI developer conference in Beijing, Baidu president and COO Lu Qi described his company's plans to work with NVIDIA to: Today, we are very excited to announce a broader and deeper strategic partnership with NVIDIA," said Lu Qi, Baidu president and COO, at Baidu Create 2017 in Beijing.