中心博士生徐绍夫的工作——Optical Convolutional Neural Network with WDM-based Optical Patching and Microring Weighting Banks（基于波分复用光学分块技术与微环权值阵列的光学卷积神经网络架构）的相关成果近期被IEEE Photonics Technology Letters期刊接收，该工作得到了国家重点研发计划（2019YFB2203700）、国家自然科学基金（61822508）的部分资助。该工作将课题组的前期研究成果——光子分块技术——与微环谐振器阵列技术相结合，提出了一种可以集成化实现的光学卷积神经网络架构。通过理论分析与仿真，该工作验证了此架构在实现二维图像识别等卷积神经网络任务中具有较高的精度，并且可实现的计算规模极大，其算力有望达到100TMAC/s。此外，通过对比，此架构相对于现有的主流光学神经网络架构在执行卷积操作时具有更高的能效比。此工作为光学神经网络尤其是卷积层的实现提供全新高效的方案，有助于解决当前深度学习遭遇的算力瓶颈问题。
摘要: 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.