Light-Powered Computers Brighten AI’s Future
The idea of building a computer that uses light rather than electricity goes back more than half a century. “Optical computing” has long promised faster performance while consuming much less energy than conventional electronic computers. The prospect of a practical optical computer has languished, however, as scientists have struggled to make the light-based components needed to outshine existing computers. Despite these setbacks, optical computers might now get a fresh start—researchers are testing a new type of photonic computer chip, which could pave the way for artificially intelligent devices as smart as self-driving cars, but small enough to fit in one’s pocket.
A conventional computer relies on electronic circuits that switch one another on and off in a dance carefully choreographed to correspond to, say, the multiplication of two numbers. Optical computing follows a similar principle, but instead of streams of electrons, the calculations are performed by beams of photons that interact with one another and with guiding components such as lenses and beam splitters. Unlike electrons, which must flow through twists and turns of circuitry against a tide of resistance, photons have no mass, travel at light-speed and draw no additional power once generated.
Researchers at Massachusetts Institute of Technology, writing in Nature Photonics, recently proposed that light-based computing would be especially helpful to enhancing deep learning, a technique underlying many of the recent advances in AI. Deep learning requires an enormous amount of computation: It entails feeding vast data sets into large networks of simulated artificial “neurons” based loosely on the neural structure of the human brain. Each artificial neuron takes in an array of numbers, performs a simple calculation on those inputs and sends the result to the next layer of neurons. By tuning the calculation each neuron performs, an artificial neural network can learn to perform tasks as diverse as recognizing cats and driving a car.
Deep learning has become so central to AI that companies including Google and high-performance chipmaker Nvidia have sunk millions into developing specialized chips for it. The chips take advantage of the fact that most of an artificial neural network’s time is spent on “matrix multiplications”—operations in which each neuron sums its inputs, placing a different value on each one. In a facial-recognition neural network, for example, some neurons might be looking for signs of noses. Those neurons would place a greater value on inputs corresponding to small, dark regions (likely nostrils), a slightly lower value on light patches (possibly skin) and very little on, say, the color neon green (highly unlikely to adorn someone’s nose). A specialized deep-learning chip performs many of these weighted sums simultaneously by farming them out to the chip’s hundreds of small, independent processors, yielding a substantial speedup.
That type of workload demands processing power equivalent to a mini supercomputer. Audi and other companies building self-driving vehicles have the luxury of stuffing a whole rack of computers in the trunk, but good luck trying to fit that kind of processing power in an artificially intelligent drone or a mobile phone. And even when a neural network can be run on large server farms, as with Google Translate or Facebook’s facial recognition, such heavy-duty computing can run up multimillion-dollar electricity bills.
In 2015 Yichen Shen, a postdoctoral associate at MIT and the new paper’s lead author, was seeking a novel approach to deep learning to solve these power and size issues. He came across the work of co-author Nicholas Harris, a PhD candidate at MIT in electrical engineering and computer science, who had built a new kind of optical computing chip.
