Monday, November 6, 2017

The Devil Is in the Detail of Deep Learning Hardware by James Morra

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To identify skin cancer, perceive human speech, and run other deep learning tasks, chipmakers are editing processors to work with lower precision numbers. These numbers contain fewer bits than those with higher precision, which require heavier lifting from computers.

Intel’s Nervana unit plans to release a special processor before the end of the year that trains neural networks faster than other architectures. But in addition to improving memory and interconnects, Intel created a new way of formatting numbers for lower precision math. The numbers weigh fewer bits so the hardware can use less silicon, less computing power, and less electricity.

Intel’s numerology is an example of the dull and yet strangely elegant ways that chip companies are coming to grips with deep learning. It is still unclear whether ASICs, FPGAs, CPUs, GPUs, or other chips will be best at handling calculations like the human brain does. But every chip appears to be using lower precision math to get the job done.

Still, companies pay a surcharge for using numbers with less detail. “You are giving up something, but the question is whether it’s significant or not,” said Paulius Micikevicius, principal engineer in Nvidia’s computer architecture and deep learning research group. “At some point you start losing accuracy, and people start playing games to recover it.”

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