Online training and pruning of photonic neural networks

Photonic neural networks (PNNs) have garnered significant interest due to their potential to offer low latency, high bandwidth, and energy efficiency in neuromorphic computing and machine learning. In PNNs, weights are photonic devices that make them susceptible to environmental factors and fabrication variations. These vulnerabilities can result in inaccurate parameter mapping, increased tuning power consumption, and reduced network performance when conventional offline training methods are used. Here, we experimentally demonstrate an online training and pruning method to address these challenges. By incorporating a power-related term into the conventional loss function, our approach minimizes the inference power budget. With this method, PNNs achieve 96% accuracy while reducing the power consumption by almost 45% on the Iris dataset, despite fabrication and thermal variations. Furthermore, our method is validated with a two-layer convolutional neural network (CNN) experiment for radio-frequency (RF) fingerprinting applications and simulations across larger CNNs on image classification datasets, including MNIST, CIFAR-10, and CIFAR-100. This work represents a significant milestone in enabling adaptive online training of PNNs and showcases their potential for real-world applications.