Neural network implemented with light instead of electrons
Neural networks have a reputation for being computationally expensive. But only the training portion of things really stresses most computer hardware, since it involves regular evaluations of performance and constant trips back and forth to memory to tweak the connections among its artificial neurons. Using a trained neural network, in contrast, is a much simpler process, one that isn’t nearly as computationally complex. In fact, the training and execution stages can be performed on completely different hardware.
And there seems to be a fair bit of flexibility in the hardware that can be used for either of these two processes. For example, it’s possible to train neural networks using a specialized form of memory called a memristor or execute trained neural networks using custom silicon chips. Now, researchers at UCLA have done something a bit more radical. After training a neural network using traditional computing hardware, they 3D printed a set of panels that manipulated light in a way that was equivalent to processing information using the neural network. In the end, they got performance at the speed of light—though with somewhat reduced accuracy compared to more traditional hardware.
So how do you implement a neural network using light? To understand that, you have to understand the structure of a deep-learning neural network. In each layer, signals from an earlier one (or the input from a source) are processed by “neurons,” which then take the results and forward signals on to neurons in the next layer. Which neurons they send it to and how strong a signal they pass on are determined by the training they’ve undergone.
To do this with light, the UCLA team created a translucent, refracting surface. As light hits it, the precise structure of the surface determines how much light passes through and where it’s directed to. If you place another, similar layer behind the first, it’ll continue to redirect the light to specific locations. This is similar in principle to how the deep-learning network works, with each layer of the network redirecting signals to specific locations in the layer beyond.
In practical terms, the researchers trained a neural network, identified the connections it made to the layer beneath it, and then translated those into surface features that would direct light in a similar manner. By printing a series of these layers, the light would be gradually concentrated in a specific area. By placing detectors in specific locations behind the final layer, they’re able to tell where the light ends up. And, if everything’s done properly, where the light ends up should tell us the neural network’s decision.
The authors tried this with two different types of image-recognition tasks. In the first, they trained the neural network to recognize hand-written numbers, and then they translated and printed the appropriate screens for a grid of 10 photodetectors to record the output.
