Boosting algorithm efficiency and the amount of memory that microcontrollers are budgeted are other prerequisites before machine learning can be pushed to Cortex-M devices, Rommel added. That would require lower memory prices or manufacturers to increase their bill-of-material budgets. “All of this can and will happen with time, we just aren’t there yet,” he said.
Dozens of semiconductor startups are trying muscle into Nvidia’s stronghold in data centers. But another battle is brewing over chips with the performance-per-watt for edge computing, where Nvidia has less command. NXP is fighting to stay ahead of new embedded rivals like Mythic, Syntiant and Intel’s Movidius unit as well as enemies like Infineon, Renesas and ST Microelectronics by wringing as much computing power as possible from its chips.
“There are companies that want to provide this single chip and say that’s what you need for artificial intelligence. But they don’t know,” said Humphries, adding that the company is debating whether to sacrifice space inside its chips for neural network silicon, which would likely raise costs for customers. “We don’t put in what we don’t need,” said Humphries, and not every processor needs an accelerator inside.
NXP is assessing a number of neural network cores, including Arm’s Project Trillium as well as in-memory processors, but nothing has been determined yet. “The key factors for dedicated accelerators are cost-effective performance, power efficiency and scalability for evolving A.I. applications,” Chindalore said. He is focused on the “long-term viability” of the company’s machine learning strategy rather than winning short-term skirmishes.
Qualcomm’s acquisition could complicate the calculus. The deal has been tangled in recent trade negotiations between the Trump administration and China, and it is still not guaranteed to close. The San Diego, California-based company’s machine learning strategy has been almost as understated as NXP’s. Last year, after shelving a custom neural network core, Qualcomm started giving customers a new software tool that could cut neural networks into smaller parts that are then assigned to the heterogeneous cores inside its chips to boost efficiency.
Whatever NXP does will have broad implications. The company, which was founded as Philips Semiconductor in 1953, has over 25,000 customers and keeps its embedded chips in production significantly longer than it would in the consumer electronics space. NXP – the world’s largest maker of automotive chips since its acquisition of Freescale Semiconductor – projects to sell over 100 million Cortex-A processors in 2018.