  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.
