Neural networks (NN) have demonstrated remarkable capabilities in various
tasks, but their computation-intensive nature demands faster and more
energy-efficient hardware implementations. Optics-based platforms, using
technologies such as silicon photonics and spatial light modulators, offer
promising avenues for achieving this goal. However, training multiple trainable
layers in tandem with these physical systems poses challenges, as they are
difficult to fully characterize and describe with differentiable functions,
hindering the use of error backpropagation algorithm. The recently introduced
Forward-Forward Algorithm (FFA) eliminates the need for perfect
characterization of the learning system and shows promise for efficient
training with large numbers of programmable parameters. The FFA does not
require backpropagating an error signal to update the weights, rather the
weights are updated by only sending information in one direction. The local
loss function for each set of trainable weights enables low-power analog
hardware implementations without resorting to metaheuristic algorithms or
reinforcement learning. In this paper, we present an experiment utilizing
multimode nonlinear wave propagation in an optical fiber demonstrating the
feasibility of the FFA approach using an optical system. The results show that
incorporating optical transforms in multilayer NN architectures trained with
the FFA, can lead to performance improvements, even with a relatively small
number of trainable weights. The proposed method offers a new path to the
challenge of training optical NNs and provides insights into leveraging
physical transformations for enhancing NN performance