Low-Wattage Chip Modeled From Human Brain to Power New Federal Supercomputer – MeriTalk

In light of recent advances in performance–not to mention the history of computing–it's reasonable to assume that artificial intelligence and machine learning systems will become smarter and faster. But government-funded research that is being put into practice at the Air Force Research Laboratory (AFRL) could achieve new levels of performance while also consuming minimal amounts of power. AFRL and IBM, working from a program started nearly a decade ago by the Defense Advanced Research Projects Agency (DARPA), have developed a "neuromorphic chip" called TrueNorth that is patterned on the neurons in the brain and can perform heavy-duty calculations while using a fraction of the energy of conventional processors. "The major advantage of this chip," Qing Wu, AFRL's principal electronics engineer said in a statement, "is it runs machine learning algorithms–the same ones as we run, the same functionality, same accuracy, but with much less power dissipation." IBM has started work on building AFRL a supercomputer made with 64 TrueNorth chips that will be used for pattern and object recognition.