People are increasingly optimistic about the prospects of artificial intelligence and its potential explosive power, and whether it is possible to develop a chip with ultra-high computing power and market-oriented to become a key battle for artificial intelligence platforms. 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 was hesitant whether artificial intelligence spurred Nvidia's stock price to be high, 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 6.55 billion US dollars. The year-on-year growth was 216%. "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. 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. 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. Investors and chipmakers are watching every move of the Internet giant. For example, NVIDIA's data center business has been providing data services to Google for a long time. NVIDIA is not the only leader in GPUs, and both Intel and AMD have different advantages in this area. In November 2016, Intel released an AI processor called Nervana, which they announced would test the prototype in the middle of next year. If all goes well, the final form of the Nervana chip will be available in 2017. The chip name is based on a company called Nervana that Intel bought earlier. According to Intel, the company is the first company in the world to build chips for AI. Intel disclosed some details about the chip. According to them, the project code is "Lake Crest" and will use Nervana Engine and Neon DNN related software. The chip accelerates a variety of neural networks, such as the Google TensorFlow framework. The chip consists of a so-called "processing cluster" array that handles simplified mathematical operations called "active points." This method requires less data than floating-point operations, resulting in a 10x performance boost. Lake Crest uses private data connections to create larger, faster clusters with a circular or other topology. This helps users create larger, more diverse neural network models. This data connection contains 12 100Gbps bidirectional connections, and its physical layer is based on 28G serial-to-parallel conversion. In addition to the above-mentioned chip giant's advancement in the GPU field, more companies are trying to trigger a full-scale subversion. Its representative announced in 2016 that it will independently develop a new processing system called TPU. The TPU is a dedicated chip designed specifically for machine learning applications. By reducing the computational accuracy of the chip and reducing the number of transistors required to implement each computational operation, the number of operations per second of the chip can be increased, so that a fine-tuned machine learning model can run on the chip. Faster, and thus faster, for users to get smarter results. Google embeds the TPU accelerator chip into the board and uses the existing hard disk PCI-E interface to access the data center server. According to Urs Holzle, senior vice president of Google, the current use of Google TPU and GPU will continue for some time, but he said that the GPU can perform graphics computing work and has many uses; TPU belongs to ASIC, which is designed for specific purposes. The special specification logic IC, because it only performs a single job, is faster, but the disadvantage is higher cost. In addition to the aforementioned Google, Microsoft is also using a new type of processor called the Field Variable Programming Gate Array (FPGA). According to reports, this FPGA currently supports Microsoft Bing, in the future they will drive a new search algorithm based on deep neural networks - artificial intelligence based on human brain structure - when executing several commands of artificial intelligence The speed is several orders of magnitude faster than the average chip. With it, your computer screen will only be blank for 23 milliseconds instead of 4 seconds. In the third generation prototype, the chip is located at the edge of each server and plugged directly into the network, but still creates an FPGA pool that any machine can access. This starts to look like something that is available for Office 365. Eventually, Project Catapult is ready to go live. In addition, Catapult hardware costs only 30% of the total cost of all other accessories in the server, and requires less than 10% of the operating energy, but the processing speed is twice that. In addition, some companies, such as Nervada and Movidius, simulate parallel modes of GPUs, but focus on moving data more quickly, omitting the functionality required for images. Other companies, including IBM, which uses a chip called "True North," have developed chip designs inspired by other brain features such as neurons and synapses. Due to the great prospects of deep learning and artificial intelligence in the future, the giants are trying to gain technical advantages. If one of these companies, such as Google, replaces the existing chip with a new chip, it basically amounts to subverting the entire chip industry. "Neither NVIDIA, Intel, Google or Baidu are looking for a foundation for future widespread use of artificial intelligence," said Therese PoletTI. Many people hold the same view as Urs Holzle, vice president of Google. They believe that in the distant future of artificial intelligence, GPUs will not replace CPUs, and TPU will not replace GPUs. The chip market will have greater demand and prosperity.
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