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By
Don Clark
Updated Jan. 11, 2017 3:52 p.m. ET
Companies that make computer chips are struggling with some of the toughest times in the industry’s history, thanks to slowing demand for certain devices and diminishing performance gains from making smaller circuitry.
Yet the pressures are spurring a renaissance of semiconductor innovation, along with a growing band of startups aiming to exploit it, industry executives say.
Big and small companies in the $335 billion global semiconductor industry are pushing to develop new chip designs, materials and manufacturing processes. One reason is the widening use of the artificial-intelligence technique known as deep learning, in tasks like classifying images, translating speech and autonomous driving, which benefit from new calculating techniques.
Some of the new development efforts are aimed squarely at disrupting entrenched incumbents like
Intel Corp.which has responded by modifying some of its own time-honored strategies.
“It is the best of times and the worst of times together,” said Dharmendra Modha, a chief scientist at
International Business Machines Corp. who is leading an effort to develop an unusual brain-like chip.
The surge in activity stems partly from the waning growth in sales of smartphones and personal computers, which has also led to unprecedented consolidation among chip makers. Dealogic tallies 707 mergers and acquisitions totaling $246 billion over the past two years.
Dharmendra Modha, chief scientist for Brain Inspired Computing at International Business Machines Corp., in a chip-testing room at the IBM Almaden Research Center in San Jose, Calif., in 2015. PHOTO: DAVID PAUL MORRIS/BLOOMBERG NEWS
At the same time, the industry’s standby strategy for increasing performance—steadily shrinking the tiny transistors on each sliver of silicon—is running into diminishing returns. For decades, chip makers followed Moore’s Law, the roughly two-year cadence for packing more transistors on chips, named after Intel co-founder Gordon Moore. Lately, though, the gains in calculating speed and power consumption have been less dramatic, while designing chips with more transistors costs more.
The Semiconductor Industry Association and its research affiliate have enlisted 22 tech companies to launch a broad study of technologies that might bring computing advances. Alternatives range from stacking circuitry in space-saving layers to making chips from biological materials such as proteins.
Development is particularly intense in deep learning. The technique involves training systems by exposing them to immense quantities of data rather than programming them with explicit instructions, which can take a long time and tends to produce less-reliable results. Web companies using deep learning have taken a particular interest in spurring the creation of hardware that can get faster results.
Deep-learning systems often use a combination of Intel processors and
NvidiaCorp. or
Advanced Micro Devices Inc. chips that were originally designed to render videogame images. Those chips comprise hundreds or thousands of simple processors performing calculations simultaneously, compared with dozens of sophisticated computing cores on a high-end Intel chip.
Some companies say even more specialized hardware is needed.
Alphabet Inc. ’s Google unit recently took the unusual step of designing its own chip from scratch for some deep-learning tasks. IBM is targeting deep learning with TrueNorth, a chip unveiled in 2014 and composed of one million structures patterned after the brain’s neurons. Mr. Modha said it has shown startling acceleration of deep-learning applications and is on track to create a “business at scale” by 2019.
Venture capitalists have taken notice. Technological challenges and stiff competition in the chip industry have led most venture capitalists to put their money elsewhere. But some entrepreneurs and investors see new opportunities to develop chips for markets like networking, where customers may want to diversify their sources rather than relying on one or two leading suppliers.
Cerebras Systems, a 25-employee company that plans to design processors targeting deep learning, found it surprisingly easy to raise venture-capital funds, said founder Andrew Feldman. “We raised money in eight days," he said, declining to say how much.
Other startups designing chips for deep learning include KnuEdge Inc., Graphcore Ltd., Cornami and Wave Computing. Wave says a system powered by a specialized processor it is developing can complete a typical text analysis job in 6.75 seconds, compared with 69 minutes on a system using a combination of Intel Xeon and Nvidia processors.
Intel, which supplies roughly 99% of the central processing units, or CPUs, used in server systems, is in the crosshairs of many of the new chip efforts. Compatible chips from AMD remain an option, and some web giants have been testing IBM’s Power chip technology or designs from ARM Holdings PLC used in mobile phones.
Other chip buyers are augmenting Intel’s processors with specialized chips for particular jobs.
Microsoft Corp. , for example, recently said it was outfitting each server in its Azure cloud service with chips called field programmable gate arrays, or FPGAs, which can be configured after leaving the factory. The company says the technology accelerates computing chores like Microsoft’s Bing search service while making the servers communicate faster.
“I need more than what Intel and AMD are doing in CPUs,” said Doug Burger, a distinguished engineer in a Microsoft research group spearheading the effort. “That is why we have this Cambrian explosion of new processor architectures.”
Intel is mounting its own offensives. The company paid $16.7 billion in late 2015 to buy Altera Corp., allowing it to sell FPGA chips to Microsoft and others. This year Intel purchased Nervana Systems and plans to add its deep-learning technology to its own processors.
Intel’s approach poses fewer programming hassles for customers than shifting over to specialty chips that tend to be supplanted over time, said Jason Waxman, vice president and general manager of Intel’s data center group.
“I’m excited to see a lot of innovation and ideas,” he said. “I tend to believe very few of them are going to last as discrete products.”
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