Google Brain’s deep learning approach to floorplanning is reminiscent of another Google AI project we’ve featured here, AlphaGo, which in 2015 became the first computer Go program to beat a human professional Go player without handicap on a full-sized board. AlphaGo later branched into AlphaStar, an AI that plays StarCraft II at the Grand Master level.
We have previously suggested that a robot-controlled future in which humans are hunted for sport is a likely one, but we’ve had a bit of trouble with our blood pressure lately, and have decided techno-utopianism is just as reasonable, in the style of Iain M Banks’ Culture novels.
The crucial difference here is that, while the murderous AI of the Terminator is a human invention, the benevolent Minds that oversee the Culture are AIs built by AIs, and it seems to work out better that way.
There’s a step in microchip design known as floorplanning, in which the building blocks of a chip are marked out before they go anywhere near a silicon wafer. You put some processing cores here, a touch of cache RAM there, top it all off with some GPU cores over here, and decorate with off-chip connections around the edges. Related areas cluster together so that the electrical pathways most commonly taken are the shortest. The whole thing needs to be verified before it’s made, of course, and one of the great skills of chip design is floorplanning them to be as efficient as possible.
And it’s very much like a game, the sort of optimization problem that you see in Spacechem or StarCraft build queues. The sort of thing, as we’ve definitely mentioned here before, that AIs are good at.
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