Electronic enthusiasts eight o'clock: People are increasingly optimistic about the prospects of artificial intelligence and its potential explosive power, and whether it can develop a chip with ultra-high computing power and market-oriented to become the key battle of artificial intelligence platform. As a result, 2016 became a year in which chip companies and Internet giants were fully deployed in the chip space. Among them, Nvidia maintains an absolute leading position. But with the giants including Google, Facebook, Microsoft, Amazon and Baidu joining the decisive battle, the future of the artificial intelligence field is still to be solved.
In 2016, everyone saw the prospects of artificial intelligence and its potential explosive power, but whether it is AlphaGo or autonomous vehicles, the basis for hardware computing is to make any sophisticated algorithm possible: that is, Whether to develop ultra-high computing power and meet the market demand for chips has become a key battle for artificial intelligence platforms.
Therefore, there is no doubt that 2016 has also become a year for chip companies and Internet giants to fully deploy in the chip field: First, CPU chip giant Intel acquired three companies in the field of artificial intelligence and GPU in the year; Develop their own processing systems, and Apple, Microsoft, Facebook and Amazon have joined.
Among them, the leader Nvidia has become the absolute darling of the capital market because of its advantages in the field of artificial intelligence: in the past year, Nvidia, which was known for its game chips, has been stable for more than a decade. The $30 price quickly soared to $120.
When the capital market is hesitating whether artificial intelligence has made Nvidia's stock price illusory, on February 10, Nvidia released its fourth quarter 2016 financial report, showing that its revenue increased by 55% year-on-year and net profit reached 655 million. The US dollar grew by 216% year-on-year.
"When giants such as Intel and Microsoft invest in artificial intelligence-based chip technology, Nvidia has already reported in Q4 that the chip company that has invested in artificial intelligence for nearly 12 years has already begun to make considerable profits." Senior technical review Therese PoletTI pointed out after the release of its earnings report.
Research firm TracTIca LLC estimates that hardware costs from deep learning projects will rise from $43.6 million in 2015 to $4.1 billion in 2024, while corporate software spending will rise from $109 million to $10 billion over the same period. Dollar.
It is this huge market that has attracted giants such as Google, Facebook, Microsoft, Amazon and Baidu to announce the company's technology shift to artificial intelligence. "In terms of artificial intelligence related technology, NVIDIA still maintains an absolute lead, but with the continuous introduction of technologies such as TPU including Google, the future AI hardware structure remains to be solved." A notable European veteran Practitioners said to the 21st Century Business Herald.
NVIDIA leads significantly in the GPU field
According to Nvidia's latest annual report, its main business areas have seen double-digit growth. In addition to its growing gaming business, its more growth has actually come from the two new business segments of data center business and autonomous driving.
The annual report data shows that data center business has a growth of 138%, while autonomous driving has a 52% increase.
“In fact, this is the most eloquent content of the entire NVIDIA financial report, because the growth of data services and autonomous driving is fundamentally driven by the development of artificial intelligence and deep learning.†An American computer hardware analyst to the 21st century The economic report said.
In the current field of deep learning, putting neural networks into practical applications goes through two phases: first, training, and second, execution. From the current environment, the training phase is very demanding GPU (graphics processor, the same below) that handles large amounts of data, which is the leading field of NVIDIA that started with image rendering with games and highly graphical applications. In the transformation stage, CPUs that need to handle complex programs, which is Microsoft's leading field for more than a decade.
"NVIDIA's current success actually represents the success of the GPU, which is one of the earliest GPU leaders," said the industry analyst.
Deep learning neural networks, especially hundreds of thousands of layers of neural networks, have high requirements for high-performance computing, and GPUs have a natural advantage in dealing with complex operations: it has excellent parallel matrix computing power, training and classification for neural networks. Both can provide significant acceleration.
For example, instead of manually defining a face from the beginning, the researcher can display images of millions of faces and let the computer define what the face should look like. When learning such an example, the GPU can be faster than a traditional processor, greatly speeding up the training process.
Therefore, supercomputers equipped with GPUs have become the only choice for training various deep neural networks. For example, Google used to use Nvidia's GPU for deep learning. "We are building a camera with tracking function, so we need to find the most suitable chip, GPU is our first choice." EU AR start-up Quine CEO Gunleik Groven at the CES (International Consumer Electronics Show) site in January this year To the reporter.
Currently, Internet giants such as Google, Facebook, Microsoft, Twitter and Baidu are using this chip called GPU to let the server learn a lot of photos, videos, sound documents, and information on social media to improve search and automated photos. Various software features such as tags. Some car manufacturers are also using this technology to develop driverless cars that can sense the surrounding environment and avoid dangerous areas.
In addition to its long-standing leadership in GPU and graphics computing, Nvidia is also one of the first technology companies to invest in artificial intelligence. In 2008, Wu Enda, who was doing research at Stanford, published a paper on neural network training with CUDA on GPU. Alex Krizhevsky, a student of Geoff Hilton, one of the "Deep Learning Big Three" in 2012, used GeForce's GeForce graphics card to increase image recognition accuracy in ImageNet. This is the beginning of Nvidia's deep learning that Nvidia CEO Huang Renxun often mentioned.
According to reports, there are currently more than 3,000 AI startups in the world, most of which use the hardware platform provided by Nvidia.
“Deep learning has proven to be very effective.†Huang Renxun said in the quarterly report on February 10. While citing current GPU computing platforms being rapidly evolving in the areas of artificial intelligence, cloud computing, gaming and autonomous driving, Huang Renxun said that in the next few years, deep learning will become a fundamental core tool for computer computing.
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