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공과대학
전자전기공학전공
Journal papers
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Implementation of multi-layer neural network system for neuromorphic hardware architecture
Title
Implementation of multi-layer neural network system for neuromorphic hardware architecture
Authors
Sun W.
;
Park J.
;
Jo S.
;
Lee J.
;
Shin H.
Ewha Authors
신형순
;
선우경
;
이정원
SCOPUS Author ID
신형순
; 선우경
; 이정원
Issue Date
2019
Journal Title
ICEIC 2019 - International Conference on Electronics, Information, and Communication
Citation
ICEIC 2019 - International Conference on Electronics, Information, and Communication
Keywords
Guide training algorithm
;
Hardware architecture
;
Multi-layer
;
Neral network
;
Reinforcement learning
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCOPUS
Document Type
Conference Paper
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. © 2019 Institute of Electronics and Information Engineers (IEIE).
DOI
10.23919/ELINFOCOM.2019.8706456
ISBN
9788995004449
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