Implementation of multi-layer neural network system for neuromorphic hardware architecture

被引：0

作者：

Sun, Wookyung ^{[1
]}

Park, Junhee ^{[1
]}

Jo, Sumin ^{[2
]}

Lee, Jungwon ^{[2
]}

Shin, Hyungsoon ^{[2
]}

机构：

[1] Ewha Womans Univ, Ctr Convergence Emerging Elect Technol, Seoul, South Korea

[2] Ewha Womans Univ, Dept Elect & Elect Engn, Seoul, South Korea

来源：

2019 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC) | 2019年

基金：

新加坡国家研究基金会;

关键词：

neral network; multi-layer; hardware architecture; reinforcement learning; guide training algorithm;

D O I：

10.23919/elinfocom.2019.8706456

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a new neuromorphic hardware system that is optimized to implement a multi-layer guide training algorithm, which is a kind of reinforcement training algorithm. To consider the hardware implementation, we apply the guide training algorithm that is simple and very suitable for memristor synapse. The system is modeled using Simulink and the accuracy of the system is verified by classifying 'T', 'X', and 'V' in 3x3 letter image. The target image of hidden layer is set to the inverted image of the input image. Using this proposed system architecture, the reinforcement learning in multi-layer can be easily implemented in hardware.

引用

页码：312 / 313

页数：2

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