This year, NVIDIA's share price has risen sharply because of the huge demand for its applications in various fields, including games, databases, and its main potential applications in the field of artificial intelligence.
However, although Nvidia's share price and its chart are already one of the most jaw-dropping events of 2017, in the field of artificial intelligence, the pace of artificial intelligence development continues, but the situation is more subtle and may have far-reaching effects.
This year, a large number of startups began to develop their own hardware to support the future of artificial intelligence infrastructure, and these companies have received huge financing. Some of these startups have not even released their products, but there seems to be no difficulty in financing.
To optimize reasoning and machine training—two key parts of the process of image and speech recognition—one of these startups is researching faster, more efficient, and more suitable for the next generation of artificial intelligence devices. Compared to the computational architecture CPUs we're used to, GPUs have become the first dazzling of the fast calculations needed to handle artificial intelligence. These startups think they will make better products.
Before we get to know these startups, let's take a quick look back at the NVIDIA stock price chart mentioned above to find out about this. Even at the end of the year, Nvidia’s share price is still rising nearly 80% when it is about to enter 2018:
Therefore, naturally, we will probably see a group of startups trying to find NVIDIA's potential vulnerabilities in the artificial intelligence market. Investors will also notice this.
The first thing we got was, last December, Cerebras Systems? The company received approximately $25 million in financing from Benchmark Capital. At the time, it seemed that the artificial intelligence chip industry was not as hot as it is today – but, over time, NVIDIA’s dominant position in the GPU market clearly shows that this will be a booming area. In August of this year, Forbes magazine reported that the company's valuation is close to 900 million US dollars.
This year, Graphcore also caused some sensation. Shortly after the announcement of the $30 million Series A financing, the company announced an investment of $5,000 in Sequoia Capital. Like Cerebras Systems, Graphcore does not have a compelling product on the market like NVIDIA. Although hardware startups face more challenges than software development companies, the startup has raised $80 million in funding within a year.
In addition, China's artificial intelligence start-ups have received substantial financial support: Alibaba has invested heavily in a startup called Cambricon Technology; Intel invested $100 million in Horizon RoboTIcs; earlier this month, a company called ThinkForce The startup raised $68 million in investment.
Groq, a startup run by former Google engineers, raised about $10 million from Social+Capital, which seems a bit smaller than the startups listed above. Another chip maker, Mythic, also completed $9.3 million in financing.
So we can see more than one or two startups doing business in this area, many of which have raised tens of millions of dollars, and even a startup has a valuation of around $900 million. These next-generation hardware startups will also need more investment. This is still a part that cannot be ignored.
In addition to startups, the world's largest companies are also looking to develop their own systems. In May of this year, Google released the next generation of TPU for reasoning and machine training. Apple designed its own GPU for the next generation iPhone. Both products will greatly help to adjust hardware for specific user needs, such as Google Cloud Apps or Siri Assistant. Intel also said in October that it will launch a new neural network processor by the end of 2017. In August last year, Intel acquired Nervana for $350 million.
All of the above are great efforts by startups and big companies, and every company is looking for its own definition of GPU. However, for NVIDIA, which has locked developers on its platform, this task may be even more daunting. This is even more true for startups that are trying to push hardware development to the madness and let developers get involved.
Maybe you still have some doubts when you talk to investors in Silicon Valley. For example, when old cards on Amazon servers can still be used for their training, why would companies consider buying faster chips for training? But there is still a lot of money flowing into the field, such as Uber (although the company has a lot of turmoil recently) and big companies like WhatsApp.
NVIDIA remains a leader in this field, and as devices such as self-driving cars become more important, the company will continue to strive to maintain its dominance. But as we enter 2018, we may begin to better understand whether these startups really have the opportunity to pull down Nvidia. This is an attractive opportunity to create faster, lower-power chips that will enter the IoT space and truly realize the role of these devices for more efficient reasoning. And, when they want to train models, they have the opportunity to make these servers faster, more energy efficient, and more powerful.
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