中圖分類號(hào)： TP183
文獻(xiàn)標(biāo)識(shí)碼： A
DOI： 10.19358/j.issn.2096-5133.2021.06.007
引用格式： 陳雅倩，黃魯. 基于浮柵器件的低位寬卷積神經(jīng)網(wǎng)絡(luò)研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40(6)：38-42.

Quantification research of convolutional neural network oriented Nor Flash

Abstract： Flash is one of the most promising candidates to bulid processing-in-memory(PIM)structures. However,the data width in one flash is 4bit at most. This article is oriented to Nor Flash and studies the quantitzation of convolution neural network. It performs quantitative perception training on the classic AlexNet, VGGNet and ResNet, and uses asymmetric quantization to quantify the model parameters from 32-bit floating point to 4-bit, and the model size becomes 1/8 of the original. For the Cifar10 data set, the accuracy of the 4-bit quantization model is only less than 2% lower than that of the full-precision network. Finally, the quantized convolutional neural network model is accelerated by the Nor Flash array. Hspice simulation results show that the accuracy of the quantized model bulided in the Nor Flash array is only reduced by 2.25% compared to the full-precision model. The feasibility of deploying the convolutional neural network on Nor Flash is verified.

Key words : convolution neural network；quantification；computation in memory；Nor Flash