Photonic integrated circuits are facilitating the development of optical neural networks, which have the potential to be both faster and more energy efficient than their electronic counterparts since optical signals are especially well-suited for implementing matrix multiplications. However, accurate programming of photonic chips for optical matrix multiplication remains a difficult challenge. Here, we describe both simple analytical models and data-driven models for offline training of optical matrix multipliers. We train and evaluate the models using experimental data obtained from a fabricated chip featuring a Mach-Zehnder interferometer mesh implementing 3-by-3 matrix multiplication. The neural network-based models outperform the simple physics-based models in terms of prediction error. Furthermore, the neural network models are also able to predict the spectral variations in the matrix weights for up to 100 frequency channels covering the C-band. The use of neural network models for programming the chip for optical matrix multiplication yields increased performance on multiple machine learning tasks.
翻译:光学综合电路正在推动光学神经网络的发展,这些光学神经网络有可能比电子神经网络更快、更节能,因为光学信号特别适合于执行矩阵乘法;然而,光学矩阵乘法光学芯片的准确编程仍是一个困难的挑战;在这里,我们描述了光学矩阵乘数离线培训的简单分析模型和数据驱动模型;我们利用一个编造芯片获得的实验数据对模型进行训练和评价,该芯片是Mach-Zehnder干涉仪网格,用于实施3x3矩阵乘法乘法;以神经网络为基础的模型在预测错误方面超过了简单的物理模型;此外,神经网络模型还能够预测覆盖C波段100个频率频道的矩阵重量的光谱变化;使用神经网络模型来规划光学矩阵乘法芯,提高了多机学习任务的性能。</s>