Photonic integrated circuits are emerging as a promising platform for accelerating matrix multiplications in deep learning, leveraging the inherent parallel nature of light. Although various schemes have been proposed and demonstrated to realize such photonic matrix accelerators, the in situ training of artificial neural networks using photonic accelerators remains challenging due to the difficulty of direct on-chip backpropagation on a photonic chip. In this work, we propose a silicon microring resonator (MRR) optical crossbar array with a symmetric structure that allows for simple on-chip backpropagation, potentially enabling the acceleration of both the inference and training phases of deep learning. We demonstrate a 4×4 circuit on a Si-on-insulator platform and use it to perform inference tasks of a simple neural network for classifying iris flowers, achieving a classification accuracy of 93.3%. Subsequently, we train the neural network using simulated on-chip backpropagation and achieve an accuracy of 91.1% in the same inference task after training. Furthermore, we simulate a convolutional neural network for handwritten digit recognition, using a 9×9 MRR crossbar array to perform the convolution operations. This work contributes to the realization of compact and energy-efficient photonic accelerators for deep learning.

中文翻译：

---

用于加速深度学习推理和训练的对称硅微环谐振器光学交叉阵列


光子集成电路正在成为一种有前景的平台，利用光固有的并行特性，加速深度学习中的矩阵乘法。尽管已经提出并论证了各种方案来实现这种光子矩阵加速器，但就地由于在光子芯片上直接进行片上反向传播的困难，使用光子加速器训练人工神经网络仍然具有挑战性。在这项工作中，我们提出了一种具有对称结构的硅微环谐振器（MRR）光学交叉阵列，该阵列允许简单的片上反向传播，有可能加速深度学习的推理和训练阶段。我们在绝缘体上硅平台上演示了一个 4×4 电路，并用它来执行简单神经网络的推理任务，以对鸢尾花进行分类，实现了 93.3% 的分类准确率。随后，我们使用模拟片上反向传播来训练神经网络，训练后在相同的推理任务中达到了 91.1% 的准确率。此外，我们模拟了用于手写数字识别的卷积神经网络，使用 9×9 MRR 交叉阵列来执行卷积运算。这项工作有助于实现用于深度学习的紧凑且节能的光子加速器。