Our brain’s neurons are, in essence, living logic gates: They take averages of the signals they receive from their neighbors and, depending on the results, either fire or don’t. In artificial neural networks, that process is replicated using matrix multiplication, but the task can be time and energy intensive. Now a study by Marin Soljačić, Dirk Englund (both at MIT), and colleagues has demonstrated that the matrix operations underlying neural network computing can be performed quickly and efficiently using photonic circuits. The team’s circuits exploit micron-sized beamsplitters and programmable phase shifters to manipulate input signals from an array of neurons and compute the values that determine neuronal responses. Couplings between waveguides can be adjusted to mimic how neuronal connections strengthen and weaken during real learning. (The image shows some of the circuit’s elements.)
The researchers used the photonic circuits to build a deep neural network—one comprising several layers of artificial...
