Figure 4. Scaling architecture for all-optical neural networks.

a) The general neural network is composed of an input layer, an output layer and several hidden layers. Each of these layers consist of a collector gathering the information from the previous layer, a distributor that equally splits the signal to individual neurons and the neuronal and synaptic elements of the layer itself. Each neuron has a weighting unit and a multiplexer to calculate the weighted sum of the inputs. The sum is then fed to an activation unit which decides if an output pulse is generated. b) Photonic implementation of a single layer from the network. The collector unites the optical pulses from the previous layer using a WDM multiplexer. A distributor made from the same rings as the collector but with adjusted coupling efficiency equally distributes the input signal to the PCM synapses of each neuron. The letters “P”, “W” and “R” denote the input ports used to probe the output, set the weights and return the neuronal PCM to its initial state.