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 language | English |
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Title of host publication | ICEIC 2019 - International Conference on Electronics, Information, and Communication |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9788995004449 |
DOIs | |
State | Published - 3 May 2019 |
Event | 18th International Conference on Electronics, Information, and Communication, ICEIC 2019 - Auckland, New Zealand Duration: 22 Jan 2019 → 25 Jan 2019 |
Publication series
Name | ICEIC 2019 - International Conference on Electronics, Information, and Communication |
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Conference
Conference | 18th International Conference on Electronics, Information, and Communication, ICEIC 2019 |
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Country/Territory | New Zealand |
City | Auckland |
Period | 22/01/19 → 25/01/19 |
Bibliographical note
Publisher Copyright:© 2019 Institute of Electronics and Information Engineers (IEIE).
Keywords
- Guide training algorithm
- Hardware architecture
- Multi-layer
- Neral network
- Reinforcement learning