The Open Electrical & Electronic Engineering Journal
2018, 12 : 132-147Published online 2018 December 31. DOI: 10.2174/1874129001812010132
Publisher ID: TOEEJ-12-132
RESEARCH ARTICLE
Multilayer Neural Network with Synapse Based on Two Successive Memristors
Department of Electrical and Electronics Engineering, Ho Chi Minh University of Technology and Education, 01 Vo Van Ngan, Thu Duc District, Ho Chi Minh City, Vietnam
* Address correspondence to this author at the Department of Electrical and Electronics Engineering, Ho Chi Minh University of Technology and Education, 01 Vo Van Ngan, Thu Duc District, Ho Chi Minh City, Vietnam; Tel: 909437522; E-mail: huanvm@hcmute.edu.vn
* Address correspondence to this author at the Department of Electrical and Electronics Engineering, Ho Chi Minh University of Technology and Education, 01 Vo Van Ngan, Thu Duc District, Ho Chi Minh City, Vietnam; Tel: 909437522; E-mail: huanvm@hcmute.edu.vn
ABSTRACT
Introduction:
Synapse based on two successive memristors builds the synaptic weights of the artificial neural network for training three-bit parity problem and five-character recognition.
Methods:
The proposed memristor synapse circuit creates positive weights in the range [0;1], and maps it to range [-1;1] to program both the positive and negative weights. The proposed scheme achieves the same accuracy rate as the conventional bridge synapse schemes which consist of four memristors.
Results and Conclusion:
However, proposed synapse circuit decreases 50% the number of memristors and 76.88% power consumption compared to the conventional bridge memristor synapse.