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.

翻译：由于光学神经网络（ONN）在并行信号处理方面的内在能力，近年来作为电子人工神经网络（ANN）的一种潜在替代方案备受关注，其具有降低功耗和低延迟的优势。通过将波分复用（WDM）技术应用于神经网络的线性变换部分，光学计算并行性的初步验证已广泛实现。然而，通道间串扰阻碍了WDM技术在ONN非线性激活中的部署。为此，我们提出一种称为复用神经元集（MNS）的通用WDM结构，将WDM技术应用于光学神经元，使ONN能够进一步压缩。同时提出一种对应的反向传播（BP）训练算法，以减轻甚至消除通道间串扰对基于MNS的WDM-ONN的影响。为简化起见，以半导体光放大器（SOA）作为MNS示例，构建了采用新算法训练的WDM-ONN。结果表明，MNS与对应BP训练算法的结合可显著缩小系统规模，并将能效提升数十倍，同时保持与传统ONN相近的性能。