Compact lensless convolution processor for an optoelectronic convolutional neural network

Abstract

To our knowledge, an optical 4f system has been widely used as a convolutional layer to perform convolutional computation in free-space optical neural networks (ONNs), which makes ONNs too bulky to be easily applied to miniaturized smart systems. Hence, we propose a compact lensless optoelectronic convolutional neural network (LOE-CNN) architecture in which a single optimized diffractive phase mask (DPM) acts as an analog convolution processor to perform convolution operation without Fourier lens or lenslet array. We demonstrate that the LOE-CNN can be functionally comparable to existing electronic counterparts in classification performance, achieving a classification accuracy of 98.07% and 95% over the Modified National Institute of Standards and Technology (MNIST) dataset in simulation and experiment, respectively, which not only opens new application prospects for free-space ONNs based on compact single-chip convolution processor, but also facilitates the development of ONNs-based smart devices.

Compact lensless optoelectronic convolutional neural network for image classification

“…In this paper, we propose a compact LOE-CNN framework that integrates a lensless optical convolution processor [30] that employs a single diffractive phase mask as the convolution layer to perform convolution operations (see Fig. 1).…”