Convolution neural network (CNN), as one of the most powerful and popular technologies, has achieved remarkable progress for image and video classification since its invention in 1989. However, with the high definition video-data explosion, convolution layers in the CNN architecture will occupy a great amount of computing time and memory resources due to high computation complexity of matrix multiply accumulate operation. In this paper, a novel integrated photonic CNN is proposed based on double correlation operations through interleaved time-wavelength modulation. Micro-ring based multi-wavelength manipulation and single dispersion medium are utilized to realize convolution operation and replace the conventional optical delay lines. 200 images are tested in MNIST datasets with accuracy of 85.5% in our photonic CNN versus 86.5% in 64-bit computer.We also analyze the computing error of photonic CNN caused by various micro-ring parameters, operation baud rates and the characteristics of micro-ring weighting bank. Furthermore, a tensor processing unit based on 4x4 mesh with 1.2 TOPS (operation per second when 100% utilization) computing capability at 20G baud rate is proposed and analyzed to form a paralleled photonic CNN.

中文翻译：

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基于交错时间-波长调制的光子卷积神经网络

自1989年发明以来，卷积神经网络（CNN）作为最强大和最受欢迎的技术之一，在图像和视频分类方面已取得了显着进步。但是，随着高清视频数据的爆炸式发展，CNN架构中的卷积层将由于矩阵乘法累加运算的计算复杂度高，因此占用了大量的计算时间和存储资源。在本文中，提出了一种基于双重相关运算的新型集成光子CNN，其通过交错的时间-波长调制实现。利用基于微环的多波长操作和单一分散介质来实现卷积运算，并取代了常规的光学延迟线。在MNIST数据集中测试了200张图像，在我们的光子CNN中准确度为85.5％，而在64位计算机中为86.5％。我们还分析了由各种微环参数，操作波特率和微环加权银行的特性引起的光子CNN的计算误差。此外，提出并分析了基于4x4网格的张量处理单元，该张量处理单元具有20G波特率下的1.2 TOPS（在100％利用率时每秒运行）的计算能力，并形成了并行光子CNN。