Robust Architecture-Agnostic and Noise Resilient Training of Photonic Deep Learning Models

Neuromorphic photonic accelerators for Deep Learning (DL) have increasingly gained attention over the recent years due to their ability for ultra fast matrix-based calculations and low power consumption providing a great potential for DL implementations to deal with a wide range of different applications. At the same time, physical properties of the optical components hinder their application since they introduce a number of limitations, such as easily saturated activation functions as well as the existence of various noise sources. As a result, photonic DL models are especially challenging to be trained and deployed, compared with regular DL models, since traditionally used methods do not take into account the aforementioned constraints. To overcome these limitations and motivated by the fact that the information lost in one layer cannot be easily recovered when gradient-descent based algorithms are employed, we propose a novel training method for photonic neuromorphic architectures that is capable of taking into account a wide range of limitations of the actual hardware, including noise sources and easily saturated activation mechanisms. Compared to existing works, the proposed method takes a more holistic view of the training process, focusing both on the initialization process, as well as on the actual weight updates. The effectiveness of the proposed method is demonstrated on a variety of different problems and photonic neural network (PNN) architectures, including a noisy photonic recurrent neural network evaluated on high-frequency time series forecasting and a deep photonic feed-forward setup consisting of a transmitter, noisy channel, and receiver, which is used as an intensity modulation/direct detection system (IM/DD).