Part of the trouble is that perceptrons are just software abstractions -- running a perceptron network on a GPU requires translating that network into the language of hardware, which takes time and energy. Building a network directly from hardware components does away with a lot of those costs. One day, they could even be built directly into chips used in smartphones and other devices, dramatically reducing the need to send data to and from servers.

Felix Petersen, who did this work as a postdoctoral researcher at Stanford University, has a strategy for making that happen. He designed networks composed of logic gates, which are some of the basic building blocks of computer chips. Made up of a few transistors apiece, logic gates accept two bits -- 1s or 0s -- as inputs and, according to a rule determined by their specific pattern of transistors, output a single bit. Just like perceptrons, logic gates can be chained up into networks. And running logic-gate networks is cheap, fast, and easy: in his talk at the Neural Information Processing Systems (NeurIPS) conference, Petersen said that they consume less energy than perceptron networks by a factor of hundreds of thousands.

Logic-gate networks don't perform nearly as well as traditional neural networks on tasks like image labeling. But the approach's speed and efficiency make it promising, according to Zhiru Zhang, a professor of electrical and computer engineering at Cornell University. "If we can close the gap, then this could potentially open up a lot of possibilities on this edge of machine learning," he says.

Petersen didn't go looking for ways to build energy-efficient AI networks. He came to logic gates through an interest in "differentiable relaxations," or strategies for wrangling certain classes of mathematical problems into a form that calculus can solve. "It really started off as a mathematical and methodological curiosity," he says.