中心助理教授徐绍夫与博士生王静共同完成的工作——High-order tensor flow processing using integrated photonic circuits（基于光子集成线路的高阶张量流处理）的相关成果近期被Nature Communications期刊正式接收，该工作得到了国家重点研发计划（2019YFB2203700）、国家自然科学基金（T2225023, 62205203）的部分资助。对高阶张量进行快速处理是信息技术发展的重要需求，而传统电学处理器面临着时钟频率受限的性能瓶颈。本工作提出一种将光学时间、空间、波长三个自由度进行独立复用的光子卷积处理器，实现了高阶张量的卷积处理，多个输入通道进入光子卷积处理器后进行流式处理，直接在输出端得到多个卷积输出通道。本工作中研制了验证性光子集成芯片，计算时钟频率达到20GHz，超越电学计算最佳水平约4倍，在多通道图像处理，视频处理等多个任务中验证了光子卷积处理器的功能可行性，在KTH动作数据集中，识别准确率达到97.9%。工作成果光电融合信号处理、光学智能计算领域发展具有重要意义。
摘要: Tensor analytics lays the mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions into matrix multiplications to enhance the parallelism of computing. However, such order-reducing transformation produces data duplicates and consumes additional memory. Here, we propose an integrated photonic tensor flow processor (PTFP) without digitally duplicating the input data. It outputs the convolved tensor as the input tensor ‘flows’ through the processor. The hybrid manipulation of optical wavelengths, space dimensions, and time delay steps, enables the direct representation and processing of high-order tensors in the optical domain. In the proof-of-concept experiment, an integrated processor manipulating wavelengths and delay steps is implemented for demonstrating the key functionalities of PTFP. The multi-channel images and videos are processed at the modulation rate of 20 Gbaud. A convolutional neural network for video action recognition is demonstrated on the processor, which achieves an accuracy of 97.9%.