Almost all traditional PCs or notebooks use Intel's processors, but this tradition is being eroded by ARM-powered alternatives. Apple’s PC is now using iOS, Google’s flagship Chromebook is more like ARM, and Microsoft recently announced Windows for ARM. In addition, the burden of processing tasks is shifting from personal devices to networks of distributed server networks, which makes it increasingly difficult for Intel with a large number of chip combinations to find millions of chip customers.
If you want to talk about the most influential chip company in history, Intel can be said to be the lead. But if you want to ask the most influential chip company for the future? This is a more open question.
AMD (blue) and Nvidia (green) stock prices over the past year
During 2016, the stock prices of Nvidia and AMD soared. The current transaction prices of the two companies are several times that of a year ago, which indicates that the two companies have great potential for future development. During this year’s CES, we heard all the hype about artificial intelligence related to a large number of algorithms and mathematical calculations. In its most complex form, artificial intelligence uses machine learning and deep learning to realize the evolution of consciousness without human direct input. All of these new technologies require a lot of processing power. By coincidence, AMD and Nvidia have been developing the perfect processor for this task: the graphics card.
Unlike central processors, GPUs or graphics cards do not have many tasks to handle. They are best suited for massively parallelism. They do not have 4 or 8 cores but have hundreds or thousands of cores. Each core can handle small, repetitive tasks over and over again with surprisingly high efficiency. GPU acceleration has become the standard for machine learning, and this year Google’s Cloud Platform will be powered by AMD’s FirePro server GPU.
Not to mention that Intel does not care about the expanding machine learning market, they have set up a dedicated website for machine learning. However, because of the fundamental disadvantages of its chips, Intel did not obtain customers or obtain the same progress as its competitors.
AMD now has a full set of dedicated GPUs for accelerated machine learning called Radeon Instinct. Every new development in machine learning can benefit from these new graphics cards: from self-driving cars, drones, personal robots to financial technology, nanobots, and advanced medicine. Investors are buying AMD stocks because they know that the future information processing challenge is actually around the GPU's massively parallel architecture, and AMD has some very high quality GPUs.
The rise of Nvidia is not surprising. The green graphics giant talked about deep learning and driverless cars at least at the CES conference in the past three years. Nvidia did not start smoothly at first, but after a while people began to be interested in their projects. Now thanks to cooperation with Audi, their technology has been integrated into real driverless cars. At the same time, as Google and AMD announced plans to conduct machine learning driven by Radeon graphics cards in the cloud in 2017, Nvidia and IBM also announced their own agreement to provide "the world's fastest" deep learning enterprise solutions. Although these agreements have not been vigorously promoted, their importance will soon be highlighted, just like today’s submarine cables that maintain the world’s Internet connection.
The next time when a company provides you with cloud-based services (such as handwriting recognition in a Chromebook), it is possible that a GPU will handle computing tasks for you.
In addition, AMD and Nvidia will also benefit from the growing consumer interest in virtual reality. Both HTC Vive and Oculus Rift have high requirements for PC specifications and require strong graphics support. So with the popularity of VR, they will sell more and more GPUs.
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