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Design of In-Situ Learning Bidirectional Associative Memory Neural Network Circuit With Memristor Synapse
- Source :
- IEEE Transactions on Emerging Topics in Computational Intelligence. 5:743-754
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Memristor is considered as a promising synaptic device for neural networks because of its tunable and non-volatile resistance states, which is similar to the biological synapses. In this article, a novel network circuit based on memristor synapses is proposed for bidirectional associative memory with in-situ learning method. An analog neuron circuit is designed to emulate the cubic activation function of neural networks. A memristive synapse circuit is constructed to map both positive and negative weights on a single memristor. Moreover, an in-situ learning circuit fitting memristor's nonlinear characteristic is proposed. Feedback control strategy is incorporated in this learning circuit to adjust the resistance of the memristor and avoid the encoding error of the memristor's write voltage. The performance of the proposed network circuit is verified by the training and recalling simulations. The comparison between the proposed approach and related works is analyzed to demonstrate the advantage of the proposed circuit design.
- Subjects :
- Hardware_MEMORYSTRUCTURES
Control and Optimization
Artificial neural network
Computer science
Circuit design
Activation function
Memristor
Computer Science Applications
law.invention
Computational Mathematics
Nonlinear system
Artificial Intelligence
law
Encoding (memory)
Electronic engineering
Bidirectional associative memory
Voltage
Subjects
Details
- ISSN :
- 2471285X
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Accession number :
- edsair.doi...........7c9cb15e645a76bfe1942650ef46ec07