Online training and pruning of photonic neural networks

Photonic neural networks have unique weight-actuating mechanisms and manufacturing variations, resulting in a suboptimal performance by conventional offline training. By incorporating a power-pruning regularization term in the loss function, we demonstrate an online training method that can overcome manufacturing errors and minimize power consumption.