Pocket brains: Neuromorphic hardware arrives for our brain-inspired algorithms. IBM’s TrueNorth helps usher in design that could again get around Moore’s Law limits.—Matthew Hutson, Ars Technica
As the world’s great companies pursue autonomous cars, they’re essentially spending billions of dollars to get machines to do what your average two-year-old can do without thinking—identify what they see. Of course, in some regards toddlers still have the advantage. Infamously last year, a driver died while in a Tesla sedan—he wasn’t paying attention when the vehicle’s camera mistook a nearby truck for the sky.
The degree to which these companies have had success so far is because of a long-dormant form of computation that models certain aspects of the brain. However, this form of computation pushes current hardware to its limits, since modern computers operate very differently from the gray matter in our heads. So, as programmers create “neural network” software to run on regular computer chips, engineers are also designing “neuromorphic” hardware that can imitate the brain more efficiently. Sadly, one type of neural net that has become the standard in image recognition and other tasks, something called a convolutional neural net, or CNN, has resisted replication in neuromorphic hardware.
That is, until recently.
IBM scientists reported in the Proceedings of the National Academy of Sciences that they’ve adapted CNNs to run on their TrueNorth chip. Other research groups have also reported progress on the solution. The TrueNorth system matches the accuracy of the best current systems in image and voice recognition, but it uses a small fraction of the energy and operates at many times the speed. Finally, combining convolutional nets with neuromorphic chips could create more than just a jargony mouthful; it could lead to smarter cars and to cellphones that efficiently understand our verbal commands—even when we have our mouths full.