Training Noise-Resilient Recurrent Photonic Networks for Financial Time Series Analysis

Photonic-based neuromorphic hardware holds the credentials for providing fast and energy efficient implementations of computationally complex Deep Learning (DL) models. At the same time, the unique nature of neuromorphic photonics also imposes a number of limitations that hinders its application, including the need to re-train DL models in order to be compliant with the underlying hardware architecture, as well as the existence of various noise sources, which are prevalent in virtually all neuromorphic photonic architectures and negatively affect the accuracy of the deployed models. In this paper we propose a novel noise-aware approach for training neural networks realized on photonic hardware, which can alleviate some of these limitations. To this end we first provide an extensive characterization of the various noise sources that affect sigmoid-based recurrent photonic architectures, as well as provide an extensive study on the effect of various signal-to-noise-ratios (SNRs) levels on the performance of such DL models. The effectiveness of the proposed method is demonstrated on a challenging forecasting problem that involves high frequency financial time series using a state-of-the-art recurrent photonic architecture, which natu-rally fits the requirements of such latency-critical applications. Apart from providing more accurate models, the proposed method opens several interesting future research directions on co-designing neuromorphic photonics, including developing DL models that can work on lower SNRs, leading to more energy efficient solutions.