Optical multi-imaging–casting accelerator for fully parallel universal convolution computing

Abstract

Recently, optical computing has emerged as a potential solution to computationally heavy convolution, aiming at accelerating various large science and engineering tasks. Based on optical multi-imaging–casting architecture, we propose a paradigm for a universal optical convolutional accelerator with truly massive parallelism and high precision. A two-dimensional Dammann grating is the key element for generating multiple displaced images of the kernel, which is the core process for kernel sliding on the convolved matrix in optical convolutional architecture. Our experimental results indicate that the computing accuracy is typically about 8 bits, and this accuracy could be improved further if high-contrast modulators are used. Moreover, a hybrid analog–digital coding method is demonstrated to improve computing accuracy. Additionally, a convolutional neural network for the standard MNIST dataset is demonstrated, with recognition accuracy for inference reaching 97.3%. Since this architecture could function under incoherent light illumination, this scheme will provide opportunities for handling white-light images directly from lenses without photoelectric conversion, in addition to convolutional accelerators.