  Due to their intrinsic capabilities on parallel signal processing, optical
neural networks (ONNs) have attracted extensive interests recently as a
potential alternative to electronic artificial neural networks (ANNs) with
reduced power consumption and low latency. Preliminary confirmation of the
parallelism in optical computing has been widely done by applying the
technology of wavelength division multiplexing (WDM) in the linear
transformation part of neural networks. However, inter-channel crosstalk has
obstructed WDM technologies to be deployed in nonlinear activation in ONNs.
Here, we propose a universal WDM structure called multiplexed neuron sets (MNS)
which apply WDM technologies to optical neurons and enable ONNs to be further
compressed. A corresponding back-propagation (BP) training algorithm is
proposed to alleviate or even cancel the influence of inter-channel crosstalk
on MNS-based WDM-ONNs. For simplicity, semiconductor optical amplifiers (SOAs)
are employed as an example of MNS to construct a WDM-ONN trained with the new
algorithm. The result shows that the combination of MNS and the corresponding
BP training algorithm significantly downsize the system and improve the energy
efficiency to tens of times while giving similar performance to traditional
ONNs.
