Photonic neural network (PNN) is a remarkable analog artificial intelligence (AI) accelerator that computes with photons instead of electrons to feature low latency, high energy efficiency, and high parallelism. However, the existing training approaches cannot address the extensive accumulation of systematic errors in large-scale PNNs, resulting in a significant decrease in model performance in physical systems. Here, we propose dual adaptive training (DAT) that allows the PNN model to adapt to substantial systematic errors and preserves its performance during the deployment. By introducing the systematic error prediction networks with task-similarity joint optimization, DAT achieves the high similarity mapping between the PNN numerical models and physical systems and high-accurate gradient calculations during the dual backpropagation training. We validated the effectiveness of DAT by using diffractive PNNs and interference-based PNNs on image classification tasks. DAT successfully trained large-scale PNNs under major systematic errors and preserved the model classification accuracies comparable to error-free systems. The results further demonstrated its superior performance over the state-of-the-art in situ training approaches. DAT provides critical support for constructing large-scale PNNs to achieve advanced architectures and can be generalized to other types of AI systems with analog computing errors.
翻译:光学神经网络(PNN)是一个非凡的模拟人工智能(AI)加速器,它用光子而不是电子进行计算,以显示低延迟性、高能效和高平行性能;然而,现有的培训方法无法解决大规模PNN大规模系统系统系统系统错误的大规模累积,导致物理系统模型性能显著下降。在这里,我们建议进行双重适应性培训,使PNNN模型能够适应重大系统错误,并在部署期间保持其性能。通过引入具有任务相似性联合优化的系统错误预测网络,DAT实现了PNN数字模型和物理系统之间的高度相似性映射,并在双向反向分析培训期间实现了高精确度梯度计算。我们通过在图像分类任务上使用多动性 PNNN和干扰性PNNNP模型,成功地培训大型PAT模型分类,保留了与无误差系统相仿的模型性能。结果进一步证明了它在州级数字数字模型和高级计算机化系统上高超超度性性性性能。我们验证了DAT对图像分类系统进行其他类型的关键支持。