Chipmakers are spending billions of dollars to research and develop fundamentally new computing architectures and processor designs as the ability to build more and more transistors into a chip inevitably approaches its physical limit.
Hewlett-Packard, for example, has constructed a prototype computer, known as the Machine, that incorporates memristors, which enable the integration of storage and random-access memory functionality. This combination promises to significantly boost efficiency and performance, and mitigate the von Neumann bottleneck.
Meanwhile, IBM is exploring post-silicon computing, using graphene and carbon nanotubes as possible materials. Graphene transistors have been proven to upgrade computing speed exponentially in comparison to silicon devices, and at reasonable power density; however, they cannot reliably encode digital logic. Graphene sheets rolled into carbon nanotubes have silicon-like semiconducting properties, but their delicacy can be a disadvantage.
Another IBM project is the TrueNorth chip, a device with more than 5 billion transistors configured to model 1 million neurons and 256 million synaptic links, so it can emulate cortical columns in the mammalian brain with no bus bottlenecking the connection.
Some researchers believe the general-purpose model of computation will be succeeded by a specialized approach that can give cars, network routers, and other formerly "dumb" objects and systems the semiautonomous flexibility and context-specific proficiency of domestic animals.
From Scientific American
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