Neural networks learn to recognize objects in images and perform other artificial intelligence tasks with a very low error rate. (Just last week, a neural network built by Google’s Deep Mind lab in London beat a master of the complex Go game—one of the grand challenges of AI.) But they’re typically too complex to run on a smartphone, where, you have to admit, they’d be pretty useful. Perhaps no more. At the IEEE International Solid State Circuits Conference in San Francisco on Tuesday, MIT engineers presented a chip designed to use run sophisticated image-processing neural network software on a smartphone’s power budget.
The great performance of neural networks doesn’t come free. In image processing, for example, neural networks like AlexNet work so well because they put an image through a huge number of filters, first finding image edges, then identifying objects, then figuring out what’s happening in a scene. All that requires moving data around a computer again and again, which takes a lot of energy, says Vivienne Sze, an electrical engineering professor at MIT. Sze collaborated with MIT computer science professor Joel Emer, who is also a senior research scientist at GPU-maker Nvidia.