New-Tech Europe | June 2017

Embedded Solutions Special Edition

Vision C5 DSP for Standalone Neural Network Processing

Paul McLellan, Cadence

I pointed out recently that although La La Land is a romance, the movie opens with cars. The semiconductor industry is like that, too—no matter which way you turn it is automotive. It may not show yet in manufacturing volume and revenue, since it is about 10% of the market. However, the newer parts of automotive, those associated with autonomous driving, have ~30% growth rates (which is close to doubling every two years, by the rule of 70). There are several really big changes, such as automotive Ethernet or security, which I won't discuss today. But probably the biggest change is the need for vision processing. There are two separate reasons that this is such a big change. Firstly, vision processing has to be done on-vehicle. The amounts of data are insanely large, too large to

upload to the cloud for processing. But more fundamentally, a vehicle cannot require network connectivity to decide whether a light is green or red, or whether that thing ahead is a pedestrian or a mailbox. This is a level of computation that cars have never required before, so is a challenge for the automotive semiconductor ecosystem. The traditional suppliers don't understand high-performance processors and leading-edge processes. The mobile semiconductor ecosystem does, but it doesn't understand automotive reliability and only recently heard the magic number 26262. (For more on ISO 26262, see my recent post "The Safest Train Is One That Never Leaves the Station". For an introduction to convolutional neural nets (CNN), see Why is Google So Good at Recognizing Cats?. Also,

last year Cadence ran a seminar in Vegas that I wrote up in a full week of posts here, starting on Monday with Power Efficient Recognition Systems for Embedded Applications.) The second change is with vision processing itself. If you go back only a few years, vision processing was algorithmic, with the focus of research on edge-detection algorithms, building 3D models from 2D data, and so on. Now the whole field has switched to convolutional neural nets (CNN). But it is not just vision processing that has gone neural, a lot of the decision processing has, too. Arguably, vision processing has advanced more in the last two to three years then since...cue dramatic music...the dawn of time. Embedded Vision Summit embedded vision summit badgeToday

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