Put a Tiger in your Lake: Intel’s next-gen mobile CPUs pack a punch
Yesterday at CES 2020, Intel previewed its next-generation line of mobile CPUs, code-named Tiger Lake, in several new form factors while running brand-new (and impressive) software designed with the platform in mind.
Tiger Lake plays into Intel’s ongoing Project Athena program, which aims to bring a performance and usability standard with concrete, testable metrics to mobile computing—that includes at least nine hours of battery life with the screen at 250 nits of brightness, out-of-the-box display and system settings, and multiple tabs and applications running. Project Athena has now been expanded to cover some new Chromebook models, as well as traditional Windows PCs.
Several new foldable designs were announced during the presentation, ranging from a relatively conventional Dell hinged two-in-one to much more outré designs such as Lenovo’s X1 Fold—presented onstage by Lenovo President Christian Teismann—and an Intel concept design prototype called Horseshoe Bend. Both the X1 Fold and Horseshoe Bend will look immediately familiar to anyone who has been following Ron Amadeo’s coverage of the Samsung and Motorola foldable smartphones; in each design, the screen itself folds down the middle.
The software demonstrations, presented by Adobe “Principal Worldwide Evangelist” Jason Levine, were by far the most compelling part of Intel’s mobile presentation. Levine performed three separate demonstrations of AI-empowered work using Adobe Sensei, with all of the manic energy of Vince Offer selling you a Slap Chop. Levine’s antics aside, the demonstrations were impressive—an automatic boundary selection of a bird in the foreground of a complex photo, another of a rose with significant light bloom muddying up its edges, and finally an automatic video conversion from landscape to portrait for a short clip of an extreme skier. The automatic selection of the bird and rose weren’t instant, but they took place in about five seconds apiece—far less time than it would take even the most skilled human artist to manually trace the edges of the photos—and appeared extremely high quality. Levine placed the cropped-out images into other scenes quickly after selecting them, with excellent visual results.
He also showed the audience a clip of an extreme skier, slaloming back and forth and doing flips across the entire field of a landscape video. Commenting about how many social media platforms were designed for portrait images, he then engaged an automatic conversion on Adobe Sensei, which automatically recognized the skier as “foreground” of the clip and panned the portrait frame automatically back and forth as necessary to keep the skier in center-frame on conversion.
After the entire set of demos was complete, we got the reveal that Levine had been performing them live using a Tiger-Lake equipped 13-inch ultralight notebook. All of the automatic selection, cropping, and panning work shown uses Intel’s OpenVINO AI framework and is greatly accelerated by its DLB x86 instruction-set extensions—so while the same tasks should run on non-Intel (and/or non-DLB-capable) hardware also, they’ll likely run several times slower.
When Ars tested the impact of Deep Learning Boost hands-on, benchmarking the i9-10980XE versus AMD’s much more powerful Threadripper 3970x, we saw the DLB-equipped i9-10980XE perform image-classification tasks at roughly double to quadruple the rate of either the Threadripper or Intel’s own non-DLB-equipped i9-9980XE. As AI-powered tasks become more and more common in applications ranging from Office to Photoshop, we expect that the ability to make short work of inference workloads will become nearly as important as general CPU performance itself.
https://arstechnica.com/?p=1640189