提出轻量级光神经网络训练框架，显著提升大规模仿真效率
LuxIA: A Lightweight Unitary matriX-based Framework Built on an Iterative Algorithm for Photonic Neural Network Training
- 用分片法优化传递矩阵计算，兼容反向传播
- 内存占用和运行时间大幅降低，支持大规模光神经网络训练
- 适合光子芯片研发与高效神经网络架构探索者
光子神经网络（PNN）利用光子电路优势，有望加速机器学习。然而，现有PNN仿真工具在训练大规模网络时面临严重可扩展性挑战，源于传递矩阵计算的高计算开销，导致内存与时间消耗巨大。为克服这一瓶颈，我们提出分片法，一种高效的传递矩阵计算方法，且兼容反向传播。该方法被集成到LuxIA——一个统一的仿真与训练框架中。实验结果表明，在MNIST、Digits和Olivetti Faces等标准数据集上，LuxIA在多种光子架构下均显著优于现有工具，在速度与可扩展性方面实现突破。本工作推动了PNN仿真技术的前沿发展，使探索和优化更大、更复杂的光子神经网络成为可能。通过解决关键计算瓶颈，LuxIA促进了光子硬件在人工智能领域的广泛应用与创新。
原文摘要 · Abstract (English)
PNNs present promising opportunities for accelerating machine learning by leveraging the unique benefits of photonic circuits. However, current state of the art PNN simulation tools face significant scalability challenges when training large-scale PNNs, due to the computational demands of transfer matrix calculations, resulting in high memory and time consumption. To overcome these limitations, we introduce the Slicing method, an efficient transfer matrix computation approach compatible with back-propagation. We integrate this method into LuxIA, a unified simulation and training framework. The Slicing method substantially reduces memory usage and execution time, enabling scalable simulation and training of large PNNs. Experimental evaluations across various photonic architectures and standard datasets, including MNIST, Digits, and Olivetti Faces, show that LuxIA consistently surpasses existing tools in speed and scalability. Our results advance the state of the art in PNN simulation, making it feasible to explore and optimize larger, more complex architectures. By addressing key computational bottlenecks, LuxIA facilitates broader adoption and accelerates innovation in AI hardware through photonic technologies. This work paves the way for more efficient and scalable photonic neural network research and development.
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