The Race to Power AI’s Silicon Brains Posted on : Nov 20 - 2017

Semiconductor startup based in the U.K., recalls that only a couple of years ago many venture capitalists viewed the idea of investing in semiconductor chips as something of joke. “You’d take an idea to a meeting,” he says, “and many of the partners would roll about on the floor laughing.” Now some chip entrepreneurs are getting a very different reception. Instead of rolling on the floor, investors are rolling out their checkbooks.

Venture capitalists have good reason to be wary of silicon, even though it gave Silicon Valley its name. Semiconductor chips cost far more to develop than software, and until recently there has been little room for radical innovations to distinguish new versions. Even if they survive, young companies often end up with profit margins thinner than the silicon wafers their chips are made from. Giant incumbents such as Intel and Nvidia are formidable competitors with deep industry knowledge and even deeper pockets.

What’s changed is a growing belief among some investors that AI could be a unique opportunity to create significant new semiconductor companies. Venture capitalists have invested $113 million in AI-focused chip startups this year—almost three times as much as in all of 2015, according to data from PitchBook, a service that tracks private company transactions.

Graphcore has been one of the beneficiaries of this shift, recently adding $50 million in funding from Sequoia Capital, a leading Silicon Valley venture firm. A number of other chip startups, including Mythic, Wave Computing, and Cerebras in the United States and DeePhi Tech and Cambricon in China, are also developing new chips tailored for AI applications. Cambricon, one of the most prominent Chinese startups in the field, has raised $100 million in an initial financing led by a Chinese government fund.

Ever since the advent of the mainframe, advances in computing hardware have triggered innovations in software. These, in turn, have inspired subsequent improvements in hardware. AI is the latest twist in this digital cycle. Companies in many industries have been investing heavily in hardware to run deep-learning systems (see “10 Breakthrough Technologies 2013: Deep Learning”). But as these become more sophisticated, they are exposing the limitations of existing chips used for AI work.

Many of those processors come from Nvidia, whose graphics chips are widely used to power games and graphic production. The processors have thousands of tiny computers operating in parallel to render pixels. With some tweaks, they’ve been adapted to run deep-learning algorithms, which also involve very large numbers of parallel computations. View More