Neural networks and "feedforward" systems have been a growing buzzword lately. IBM has been at it for a while now with their SyNAPSE hardware and plenty of other software companies have added it as a tool for data analysis. The basic mechanism is to create a system that models biological neurons and can learn over time. Typically, a human will train the system by telling it which behavior is "right" and "wrong," although there are some tasks which are done without any human intervention. It's just like training your dog, only now your dog can answer things like "How many different types of objects are there in this picture?"
Why is neural networking a big deal? Because despite their talent for arithmetic, typical computers don't "think" the way that we do, and computers have to expend a lot of energy to emulate easy "human" tasks. If you want a great example, look up how much computing power and camera work goes into painting the first down line for NFL games on TV.
Yeah, that one line takes 7 computers... |
How will this Zeroeth product fare for Qualcomm? We'll have to wait and find out. Certainly I'd love to see more accurate individualized learning in my smartphone, but I do have my reservations on this particular strategy. On the other hand, the company has been on a tear lately, introducing new products in all kinds of spaces. The next few years will either redefine Qualcomm as a broad silicon IP company, or solidify them as the big fish in mobile SoCs.