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

Wookyung Sun, Junhee Park, Sumin Jo, Jungwon Lee, Hyungsoon Shin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationICEIC 2019 - International Conference on Electronics, Information, and Communication
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788995004449
DOIs
StatePublished - 3 May 2019
Event18th International Conference on Electronics, Information, and Communication, ICEIC 2019 - Auckland, New Zealand
Duration: 22 Jan 201925 Jan 2019

Publication series

NameICEIC 2019 - International Conference on Electronics, Information, and Communication

Conference

Conference18th International Conference on Electronics, Information, and Communication, ICEIC 2019
Country/TerritoryNew Zealand
CityAuckland
Period22/01/1925/01/19

Bibliographical note

Publisher Copyright:
© 2019 Institute of Electronics and Information Engineers (IEIE).

Keywords

  • Guide training algorithm
  • Hardware architecture
  • Multi-layer
  • Neral network
  • Reinforcement learning

Fingerprint

Dive into the research topics of 'Implementation of multi-layer neural network system for neuromorphic hardware architecture'. Together they form a unique fingerprint.

Cite this