NXP can use its wide reach to understand customer requirements and adjust its machine learning plans accordingly, said Nadine Manjaro of market research firm Compass Intelligence. Nvidia and Intel – which has poured billions into its acquisitions of Nervana Systems, Movidius and Mobileye – have more complete strategies, but the market is constantly changing, she said. “NXP has the resources to invest further.”
NXP can benefit from its understanding of the automotive, networking, aerospace and other industries that only implement new technologies after relentless testing. “Given NXP’s primary markets in embedded, they are not really late” Rommel told Electronic Design, adding that Nvidia’s position in the machine learning market is far from unassailable. “It is still early days,” he said.
Customers are already wading into machine learning. Orcam, a startup founded by the former chief executive of Mobileye, uses NXP’s i.MX 7 processor inside its wearable device for the elderly and visually impaired. The device can be attached to a pair of glasses, reading out loud newspaper articles and other text and remembering faces. The wearable uses an on-device deep learning algorithm instead of offloading inference tasks to the cloud.
NXP foresees customers using microcontrollers for simpler but similar tasks. At the NXP FTF Connects conference last month, the company showed that its Cortex-M4 devices could use machine learning to sense vibrational anomalies in industrial machines, so that factory owners can address issues sooner. Another processor that only costs a few dollars was used to identify foods placed in a microwave in a tenth of a microsecond. Both chips, however, were plugged into a power source, not battery-powered.
Unplugging and lowering the cost of these devices would check off boxes for researchers like Pete Warden, who works on Google’s open-source machine learning tools. He foresees an artificially intelligence future in which microcontrollers only cost pennies but have the processing power to grasp basic voice commands like on and off and monitor for a grasshopper’s chirping on farms. They would also be able to function on a single battery charge for several years.
“We’ve been talking about the potential of A.I. and neural networks in the embedded market for a decade,” said Rommel. “Now there finally is some momentum and economical processor performance to support it.” He added: “Target system memory availability is still going to be a gate for widespread A.I. implementation” and “further improvements in algorithm engineering and compression tools.”
This article first appeared in Electronic Design - www.electronicdesign.com