Semiconductor Startup Shows Off the World’s Biggest Processor

 

The logic behind going big is straight forward, according to founder Andrew Feldman. Artificial intelligence software requires huge amounts of information to improve, so processors need to be as fast as possible to crunch all this data - even if that means the components get truly chunky. The company’s Wafer Scale Engine chip is large because it has 1.2 trillion transistors, 400,000 computing cores and 18 gigabytes of memory. (A typical PC processor will have about 2 billion transistors, four to six cores and a fraction of the memory).

 

“Every square millimeter is optimized for this work,” Feldman said. “AI work is growing like crazy. Our customers are in pain.” The biggest limitation of current AI systems is that it takes too long to train software, he added. Feldman has experience and industry backing that’s very important to tackling an engineering problem of this magnitude, he said. He co-founded server maker SeaMicro Inc. and sold it to chipmaker Advanced Micro Devices Inc. for more than 0 million in 2012. Cerebras has raised over 0 million from Silicon Valley investors including Benchmark, Andy Bechtolsheim and Sam Altman. Feldman has a team of 174 engineers and Taiwan Semiconductor Manufacturing Co. - Apple Inc.’s chipmaker of choice - is manufacturing the massive Cerebras processor.

 

Cerebras will definitely not sell the chips because it’s very complicated to connect and cool such a huge piece of silicon. On the other hand, the product will be offered as part of a new server that will be installed in data centers. The company said it has test systems working at a range of large potential customers and will start shipping the machines commercially in October. The AI chip market includes Nvidia Corp., Intel Corp. and U.K. startup Graphcore Ltd. Google has been so keen to speed up AI progress that the internet giant developed its own special chips called Tensor Processing Units.

 

Nvidia was the last company to adequately bring new semiconductor technology into servers, the machines that run data centers that Google, Facebook Inc. and others use to run internet services. Nvidia now gets almost billion a year in revenue from the business, which took years and thousands of engineers to build, according to Chief Executive Officer Jensen Huang. “It takes a long time to be successful in data center,” he said in an interview last week.

 

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