Vol. 53, Issue 3, pp. 483-493

Vol. 53 Issue 3 pp. 483-493

Noise quantization simulation analysis of optical convolutional networks

Ye Zhang, Saining Zhang, Danni Zhang, Yanmei Su, Junkai Yi, Pengfei Wang, Ruiting Wang, Guangzhen Luo, Xuliang Zhou, Jiaoqing Pan


optical neural network, convolutional neural network, noise, quantization


Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.

Vol. 53
Issue 3
pp. 483-493

0.71 MB
OPTICA APPLICATA - a quarterly of the Wrocław University of Science and Technology, Faculty of Fundamental Problems of Technology