Slimmed optical neural networks with multiplexed neuron sets and a corresponding backpropagation training algorithm

TL;DR

This paper introduces multiplexed neuron sets (MNS) with a new backpropagation algorithm for optical neural networks, enabling system compression and energy efficiency improvements while mitigating inter-channel crosstalk.

Contribution

The paper proposes a universal WDM structure called MNS and a backpropagation training algorithm to reduce crosstalk effects in optical neural networks.

Findings

Significant system downsizing achieved.

Energy efficiency improved by tens of times.

Maintained comparable performance to traditional ONNs.

Abstract

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.