ARM’s latest mobile processors are tuned to crunch machine-learning algorithms as efficiently as possible. ARM announced a few days ago that it has created its first dedicated machine-learning chips, which are meant for use in mobile and smart-home devices. The company says it’s sharing the plans with its hardware partners, including smartphone chipmaker Qualcomm, and expects to see devices packing the hardware by early 2019.
Currently, small and portable devices lack the power to run AI algorithms, so they enlist the help of big servers in the cloud. But enabling mobile devices to run their own AI software is attractive. It can speed things up, cutting the lag inherent in sending information back and forth. It will allow hardware to run offline. And it pleases privacy advocates, who are comforted by the idea of data remaining on the device.
Jem Davies, the lead of Machine Learning group at ARM, said, “We analyze compute workloads, work out which bits are taking the time and the power, and look to see if we can improve on our existing processors.” The new chips use less power than the company’s other designs to perform the kinds of linear-algebra calculations that underpin modern artificial intelligence. They’re also better at moving data in and out of memory.
