  The ever-growing deep learning technologies are making revolutionary changes
for modern life. However, conventional computing architectures are designed to
process sequential and digital programs, being extremely burdened with
performing massive parallel and adaptive deep learning applications. Photonic
integrated circuits provide an efficient approach to mitigate bandwidth
limitations and power-wall brought by its electronic counterparts, showing
great potential in ultrafast and energy-free high-performance computing. Here,
we propose an optical computing architecture enabled by on-chip diffraction to
implement convolutional acceleration, termed optical convolution unit (OCU). We
demonstrate that any real-valued convolution kernels can be exploited by OCU
with a prominent computational throughput boosting via the concept of structral
re-parameterization. With OCU as the fundamental unit, we build an optical
convolutional neural network (oCNN) to implement two popular deep learning
tasks: classification and regression. For classification, Fashion-MNIST and
CIFAR-4 datasets are tested with accuracy of 91.63% and 86.25%, respectively.
For regression, we build an optical denoising convolutional neural network
(oDnCNN) to handle Gaussian noise in gray scale images with noise level
{\sigma} = 10, 15, 20, resulting clean images with average PSNR of 31.70dB,
29.39dB and 27.72dB, respectively. The proposed OCU presents remarkable
performance of low energy consumption and high information density due to its
fully passive nature and compact footprint, providing a highly parallel while
lightweight solution for future computing architecture to handle high
dimensional tensors in deep learning.
