BY PETE SINGER, Editor-in-Chief
There’s a strongly held belief now that the way in which semiconductors will be designed and manufactured in the future will be largely determined by a variety of rapidly growing applications, including artificial intelligence/deep learning, virtual and augmented reality, 5G, automotive, the IoT and many other uses, such as bioelectronics and drones.
The key question for most semiconductor manufacturers is how can they benefit from these trends? One of the goals of a recent panel assembled by Applied Materials for an investor day in New York was to answer that question.
The panel, focused on “enabling the A.I. era,” was moderated by Sundeep Bajikar (former Sellside Analyst, ASIC Design Engineer). The panelists were: Christos Georgiopoulos (former Intel VP, professor), Matt Johnson (SVP in Automotive at NXP), Jay Kerley (CIO of Applied Materials), Mukesh Khare (VP of IBM Research) and Praful Krishna (CEO of Coseer). The panel discussion included three debates: the first one was “Data: Use or Discard”; the second was “Cloud versus Edge”; and the third was “Logic versus Memory.”
“There’s a consensus view that there will be an explosion of data generation across multiple new categories of devices,” said Bajikar, noting that the most important one is the self-driving car. NXP’s Johnson responded that “when it comes to data generation, automotive is seeing amazing growth.” He noted the megatrends in this space: the autonomy, connectivity, the driver experience, and electrification of the vehicle. “These are changing automotive in huge ways. But if you look underneath that, AI is tied to all of these,” he said.
He said that estimates of data generation by the hour are somewhere from 25 gigabytes per hour on the low end, up to 250 gigabytes or more per hour on the high end. or even more in some estimates.
“It’s going to be, by the second, the largest data generator that we’ve seen ever, and it’s really going to have a huge impact on all of us.”
Intel’s Georgiopoulos agrees that there’s an enormous amount of infrastructure that’s getting built right now. “That infrastructure is consisting of both the ability to generate the data, but also the ability to process the data both on the edge as well as on the cloud,” he said. The good news is that sorting that data may be getting a little easier. “One of the more important things over the last four or five years has been the quality of the data that’s getting generated, which diminishes the need for extreme algorithmic development,” he said. “The better data we get, the more reasonable the AI neural networks can be and the simpler the AI networks can be for us to extract information that we need and turn the data information into dollars.” Check out our website at www.solid-state.com for a full report on the panel.