We propose an optical convolutional neural network (OCNN) architecture for high-speed and energy-efficient deep learning accelerators. The WDM-based optical patching scheme (WDM-OPS) is adopted as the data-feeding structure for its superior energy efficiency and the microring banks are used for the large-scale weighting and summing (the computing core). We thoroughly investigate the performance (including prediction accuracy, speed, and energy efficiency) of this architecture in different system defects. The results indicate that, the prediction accuracy of OCNN can reach 97% in the MNIST dataset with a computing speed of over 100 TMAC/s on condition of achievable low insertion loss. It is also observed that the WDM-OPS notably reduces the energy consumption of the electro-optic modulation and thus the OCNN becomes an exceptionally energy-efficient architecture among several well-known optical architectures. In the evaluations, instead of merely considering the computing core, we take the holistic optical system including lasers, electro-optic modulators, data preprocessing, photodetection and transimpedance amplification into consideration. Therefore, this work provides a potential guide for the systematic implementation of the OCNN architecture.

中文翻译：

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具有基于 WDM 的光修补和微环加权库的光卷积神经网络

我们为高速和节能的深度学习加速器提出了一种光学卷积神经网络 (OCNN) 架构。采用基于WDM的光补丁方案（WDM-OPS）作为数据馈送结构，具有优越的能效，微环组用于大规模加权和求和（计算核心）。我们彻底调查了该架构在不同系统缺陷中的性能（包括预测精度、速度和能源效率）。结果表明，在可实现低插入损耗的情况下，OCNN 在 MNIST 数据集中的预测准确率可以达到 97%，计算速度超过 100 TMAC/s。还观察到 WDM-OPS 显着降低了电光调制的能耗，因此 OCNN 成为几种众所周知的光学架构中非常节能的架构。在评估中，我们不是仅仅考虑计算核心，而是考虑了包括激光器、电光调制器、数据预处理、光电检测和跨阻放大在内的整体光学系统。因此，这项工作为 OCNN 架构的系统实现提供了潜在的指导。光电检测和跨阻抗放大考虑在内。因此，这项工作为 OCNN 架构的系统实现提供了潜在的指导。光电检测和跨阻抗放大考虑在内。因此，这项工作为 OCNN 架构的系统实现提供了潜在的指导。