While there are lots of things that artificial intelligence can't do yet—science being one of them—neural networks are proving themselves increasingly adept at a huge variety of pattern recognition tasks. These tasks can range anywhere from recognizing specific faces in photos to identifying specific patterns of particle decays in physics.

Right now, neural networks are typically run on regular computers. Unfortunately, those networks are a poor architectural match; neurons combine both memory and calculations into a single unit, while our computers keep those functions separate. For this reason, some companies are exploring dedicated neural network chips. But a US-Canadian team is now suggesting an alternative: optical computing. While not as compact or complex as the competing options, optical computing is incredibly quick and energy-efficient.

Optical computing works because static optical elements perform transformations on light that are the equivalent of mathematical transformations. For example, the authors note, a plain old lens like in a magnifying glass effectively performs a Fourier transform without using any power whatsoever. It's also possible to perform things like matrix operations using optical elements. Speed comes from the fact that our light sources and detectors are fast, operating at speeds of up to 100GHz.

Light training

Of course, having a completely static optical system means that you can't do one critical aspect of neural networks: training. Training involves each node adjusting its behavior based on the accuracy of the system's output. So here, the team worked with an adjustable silicon-based photonic chip.

To perform calculations, a traditional computer can quickly encode information in several beams of light that are sent into one side of the chip. As the light moves through the chip, it passes through a series of nodes, where an optical element called a Mach–Zehnder interferometer could cause two light inputs to interfere with each other, altering the properties of the light that continued out the other side. This operation is the equivalent of matrix multiplication. After several nodes of interference, the light would travel through a series of attenuators, which cut the intensity of the light down slightly.

The behavior of these individual nodes is adjustable, allowing the optical neural network to undergo training. Once trained, however, the optical chip can be kept in its trained state with very little energy input. The authors indicate that some tweaks to the hardware would allow the chip to maintain its state without expending any energy. If that works out, then the only power consumption will be from the laser that produces the input light and the computer that encodes information in it.

To show that their network works, the authors had 90 people record one of four different vocal sounds and then used half of this set to train a neural network to recognize vowels. The researchers then tested their network using the remaining half of the set. The full neural network required more nodes than were on the photonic chip, so they simply read the light after one pass through the chip, re-encoded it, and sent it back through a second time.

Overall, the performance wasn't great. Training a traditional neural network on the same data set produced a vowel-recognition accuracy of just over 90 percent. For the optical neural network, accuracy was just over 75 percent. The authors ascribe most of the problems to a combination of their detection hardware and thermal crosstalk among the photonic devices. The latter at least is easy to address by adding some insulating features to the chip.

Good and bad

As a proof-of-principle, the work is impressive. If the researchers can get the energy use down and accuracy up—and they think they already know how to do both—then the system could run a trained neural network using 100,000 times less power than a traditional GPU. And the fast speeds and low latency of optical equipment means that performance should be excellent.

But there are some significant limitations right now. To begin with, the size of existing optical chips kept the full neural network from being implemented in a single pass; computations involved reading the output of a first pass and sending light back through a second time. And that's for a relatively simple neural network. The authors calculate that a neural network five layers deep would take a centimeter-sized chip to host. Since current "deep learning" neural networks involve 20 layers or more, implementing them in their entirety would require a pretty big chip.

The alternative is to keep solving problems by sending light through multiple times. But that requires reading the results and calculating how to generate light with the appropriate properties to make each additional burst of light work properly. At that point, much of the speed and latency advantages are going to evaporate. Similarly, it's hard to see how to take full advantage of the speed of the optical hardware when you have to calculate the properties of the input light in advance. Switching among multiple light sources to keep the neural network busy should be possible, but that would add to the complexity and energy use.

Possibly the biggest positive here is that companies like IBM are working on integrating more optical capabilities on standard silicon chips. The technology needed to make optical neural networks more effective may possibly be developed for some other purpose entirely.

Nature Photonics, 2017. DOI: 10.1038/nphoton.2017.93 (About DOIs).