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：由于其固有的并行信号处理能力，光学神经网络（ONNs）最近作为电子人工神经网络（ANNs）的潜在替代方案引起了广泛关注，能够降低功耗和延迟。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ONNs非线性激活中的部署。本文提出一种通用的WDM结构，即复用神经元集（MNS），该结构将WDM技术应用于光学神经元，使ONNs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-ONNs的影响。为简化起见，采用半导体光放大器（SOAs）作为MNS的示例，构建了经新算法训练的WDM-ONN。结果表明，MNS与对应BP训练算法的结合能够显著缩小系统规模，并将能效提升数十倍，同时保持与传统ONNs相似的性能。