Local loss function based single pass training of optical neural networks

As neural networks (NNs) become more capable, their computational resource requirements also increase exponentially. Optical systems can provide alternatives with higher parallelizability and lower energy consumption. However, the conventional training method, error backpropagation, is challenging to implement with these analog systems since it requires the characterization of the hardware. In contrast, the Forward-Forward Algorithm defines a local loss function for each layer and trains them sequentially without tracking the error gradient between different layers. In this study, we experimentally demonstrate the suitability of this approach for optical NNs by utilizing the multimode nonlinear propagation inside an optical fiber as a building block of the NN. Compared to the all-digital implementation, the optical NN achieves significantly higher classification accuracy while utilizing the optical system only one epoch per layer.