Training Process of Memristor-Based Spiking Neural Networks For Non-linearity

被引：0

作者：

Chen, Tsu-Hsiang ^{[1
]}

Chang, Chih-Chun ^{[1
]}

Huang, Chih-Tsun ^{[1
]}

Liou, Jing-Jia ^{[2
]}

机构：

[1] Natl Tsing Hua Univ, Dept Comp Sci, Hsinchu, Taiwan

[2] Natl Tsing Hua Univ, Dept Elect Engn, Hsinchu, Taiwan

来源：

2024 INTERNATIONAL VLSI SYMPOSIUM ON TECHNOLOGY, SYSTEMS AND APPLICATIONS, VLSI TSA | 2024年

关键词：

Spiking Neural Network; Memristor Array; Training; Variability;

D O I：

10.1109/VLSITSA60681.2024.10546383

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The implementation of a spiking neural network (SNN) with memristor arrays has the potential to improve the area and power efficiency of edge-device inference. However, due to non-linearity, we cannot maintain the inference accuracy on a memristor array with the trained weights based on a regular SNN. In this paper, we proposed a training process to consider the non-linear circuit effects. With the proposed method, the experimental results showed that the accuracy of the trained SNN was improved from 62.99% to 92.81%.

引用

页数：4

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