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 (see “Nvidia CEO: Software Is Eating the World, but AI Is Going to Eat Software”).

Although they have been widely adopted, graphics chips have some drawbacks. One of the biggest is that when large numbers of them work in parallel they soak up a lot of energy. Carnegie Mellon University, a leading AI research center, has even had to ask researchers there to throttle back their use of the chips temporarily because they were putting a strain on the university’s power system. Franz Franchetti, a professor at CMU, says the university is looking at alternative power sources to alleviate the issue.

The AI chip startups are planning to produce more power-efficient processors. But what’s really energizing them is their belief that tailor-made processors for AI applications can beat less specialized chips at a wide range of machine-learning tasks. The new generation of chips combine multiple processing functions into a single step, whereas graphics processors take multiple steps to achieve the same result. The functions are typically bundled to optimize specific use cases, such as training algorithms to help an autonomous car spot potential obstacles ahead.