Author(s): Zhang, DN (Zhang, Danni); Zhang, YJ (Zhang, Yejin); Zhang, Y (Zhang, Ye); Su, YM (Su, Yanmei); Yi, JK (Yi, Junkai); Wang, PF (Wang, Pengfei); Wang, RT (Wang, Ruiting); Luo, GZ (Luo, Guangzhen); Zhou, XL (Zhou, Xuliang); Pan, JQ (Pan, Jiaoqing)

Source: APPLIED SCIENCES-BASEL Volume: 11 Issue: 8 Article Number: 3692 DOI: 10.3390/app11083692 Published: APR 2021

Abstract: Optical neural networks (ONNs) are getting more and more attention due to their advantages such as high-speed and low power consumption. However, in a non-ideal environment, the noise and low-bits control may heavily lead to a decrease in the accuracy of ONNs. Since there is AD/DA conversion in a simulated neural network, it needs to be quantified in the model. In this paper, we propose a quantitative method to adapt ONN to a non-ideal environment with fixed-point transmission, based on the new chip structure we designed previously. An MNIST hand-written data set was used to test and simulate the model we established. The experimental results showed that the quantization-noise model we established has a good performance, for which the accuracy was up to about 96%. Compared with the electrical method, the proposed quantization method can effectively solve the non-ideal ONN problem.

Accession Number: WOS:000643961500001

eISSN: 2076-3417

Full Text: https://www.mdpi.com/2076-3417/11/8/3692