Get all the news you need about Smart Buildings with the Memoori newsletter

Which research categories are you interested in?

In the last 18 months, a plethora of startups have begun working on their own variations of AI hardware and/or deep learning algorithms. Very few of these startups have yet to establish a significant installed base and most have yet to ship a product but have had no trouble raising finance.

Looking to optimize inference and machine training — two key parts of processes like image and speech recognition — startups have sought to find ways to pick away at these processes using methods that will make them faster, more power-efficient and generally better suited for the next generation of artificial intelligence-powered devices. Instead of the traditional computational architecture they have invested in GPUs which has become one of the go-to pieces of silicon for processing rapid-fire calculations required for AI processes. This is now extending to further new architectures attracting venture capital companies to invest big.

Cerebras Systems picked up funding from Benchmark Capital in December 2016 when it raised around $25 million. At the time, it seemed like the AI chip industry was not quite as obvious as it is in 2018, but throughout 2017 Nvidia’s dominance of the GPU market was a clear indicator that this would be a booming market. When Forbes reported in August 2017 that Nvidea was valued at nearly $900 million there could be little doubt that this market had taken off.

Graphcore, too, made some noise in 2017 when it announced a new $50 million financing round led by Sequoia Capital, shortly after a $30 million financing round in July led by Atomico. Graphcore still, like Cerebras Systems, doesn’t have a fully developed product on the market unlike Nvidea. And yet this startup was able to raise $80 million in a year, though hardware startups face many more challenges than businesses built on the back of software.

There’s also been a flurry of funding for Chinese AI startups: Alibaba poured financing into a startup called Cambricon Technology, which is reportedly valued at $1 billion; Intel Capital led a $100 million investment in Horizon Robotics; and a startup called ThinkForce raised $68 million earlier this month. A third Beijing chip start-up, DeePhi, has raised $40 million, and the country’s Ministry of Science and Technology has explicitly called for the production of Chinese chips that challenge Nvidia’s.

Groq, a startup run by former Google engineers raised around $10 million from Social+Capital, which seems small in the scope of some of the startups listed above. Mythic, yet another chip maker, has raised $9.3 million in financing.

Moving beyond startups, the biggest tech companies in the world are also looking to create their own systems. Google announced its next-generation TPU in May 2017 geared toward inference and machine training. Apple designed its own GPU for its next-generation iPhone. Both of these will go a long way toward trying to tune the hardware for their specific needs, such as Google Cloud applications or Siri. Intel also said in October it would ship its new Nervana Neural Network Processor by the end of 2017. Intel bought Nervana for a reported $350 million in August 2016.

All of these represent massive investments and undertakings by both the startups and the larger tech companies, each looking for their own interpretation of a GPU. But unseating Nvidia, which has begun the process of locking in developers onto its platform (called Cuda), may be an even more difficult task. That’s going to be particularly true for startups that are trying to get developers on board.

ARM is unveiling its ambitious new machine learning processor platform, called Project Trillium. The platform includes processors and sensors for improving artificial intelligence operations in mobile devices at the edge of networks, rather than in data centers. They have created a high-end processor to handle machine learning calculations, or those that enable computers to learn without explicitly being programmed to perform certain tasks.

Intel Corp.’s venture capital arm unveiled a dozen new investments in startups in May this year spanning AI, cloud, Internet of Things and semiconductor technologies. Intel Capital announced the new investments totaling $72 million (May 8) during a company summit. The chipmaker said the new investments bring its total for 2018 to more than $115 million.

They also announced investments in 5 AI startups, including one based in China. Reconova, develops computer vision and machine learning technologies that have been integrated into “smart” retail and home applications along with facial recognition products.

Machine learning patents grew at a 34% compound annual growth rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted. Machine learning’s potential impact across many of the world’s most data-prolific industries continues to fuel venture capital investment, private equity (PE) funding, mergers and acquisitions all focused on winning the race of Intellectual Property (IP) and patents in this field.

Enterprises are increasing their research, investment and piloting of machine learning programs in 2018. And while the methodologies all vary across the many sources of forecasts, market estimates, and projections, all reflect how machine learning is improving the acuity and insights of companies on how to grow faster and more profitably.

Click here to view more details about our new report The Global Market for Intelligent Video Analytics 2018 to 2023.