Xiao Sun

Artificial-intelligence systems often require a vast amount of computation. That’s why in recent years, AI hardware researchers have been striving to achieve lower precision, which is good enough to produce a correct answer but avoids the use of calculations that require keeping track of lots of digits.

Deep learning relies on networks that might have dozens of layers, and millions, or even billions, of parameters that must be adjusted to the correct values, a process called training the network. This often takes days or weeks of computations using hundreds of specialized chips.

Xiao Sun is part of a research group at IBM that has been finding ways to perform those computations using three-digit, or even just two-digit, numbers (in contrast, a modern laptop or cell phone uses 20 digits to make calculations, while most dedicated machine-learning chips use five). 

The real trick is in finding techniques that allow for small numbers to be used throughout the computation. You might still have to do many trillions of computations, but each one will be far simpler. This saves both time and energy—using two-digit numbers is more than 20 times more energy efficient than doing the same calculations using numbers in the billions, according to a paper by Sun and colleagues at IBM.

In February, IBM announced a new chip, based in part on Sun’s work, that trains neural networks using computations involving mostly three-digit numbers. The company hopes to use it not only to train large neural networks in cloud computing centers but also in mobile phones that could train on local data.