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Memristor Neural Network Training with Clock Synchronous Neuromorphic System
- Title
- Memristor Neural Network Training with Clock Synchronous Neuromorphic System
- Authors
- Jo, Sumin; Sun, Wookyung; Kim, Bokyung; Kim, Sunhee; Park, Junhee; Shin, Hyungsoon
- Ewha Authors
- 신형순; 박준희; 선우경
- SCOPUS Author ID
- 신형순; 박준희; 선우경
- Issue Date
- 2019
- Journal Title
- MICROMACHINES
- ISSN
- 2072-666X
- Citation
- MICROMACHINES vol. 10, no. 6
- Keywords
- neuromorphic system; Hebbian training; guide training; memristor; image classification
- Publisher
- MDPI
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Memristor devices are considered to have the potential to implement unsupervised learning, especially spike timing-dependent plasticity (STDP), in the field of neuromorphic hardware research. In this study, a neuromorphic hardware system for multilayer unsupervised learning was designed, and unsupervised learning was performed with a memristor neural network. We showed that the nonlinear characteristic memristor neural network can be trained by unsupervised learning only with the correlation between inputs and outputs. Moreover, a method to train nonlinear memristor devices in a supervised manner, named guide training, was devised. Memristor devices have a nonlinear characteristic, which makes implementing machine learning algorithms, such as backpropagation, difficult. The guide-training algorithm devised in this paper updates the synaptic weights by only using the correlations between inputs and outputs, and therefore, neither complex mathematical formulas nor computations are required during the training. Thus, it is considered appropriate to train a nonlinear memristor neural network. All training and inference simulations were performed using the designed neuromorphic hardware system. With the system and memristor neural network, the image classification was successfully done using both the Hebbian unsupervised training and guide supervised training methods.
- DOI
- 10.3390/mi10060384
- Appears in Collections:
- 공과대학 > 전자전기공학전공 > Journal papers
- Files in This Item:
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Memristor Neural Network Training.pdf(1.89 MB)
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