Description
Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule.
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Contributors
- Yu, Shimeng (Author)
- Gao, Bin (Author)
- Fang, Zheng (Author)
- Yu, Hongyu (Author)
- Kang, Jinfeng (Author)
- Wong, H.-S. Philip (Author)
- Ira A. Fulton Schools of Engineering (Contributor)
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2013-10-31
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- Digital object identifier: 10.3389/fnins.2013.00186
- Identifier TypeInternational standard serial numberIdentifier Value1662-4548
- Identifier TypeInternational standard serial numberIdentifier Value1662-453X
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Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., & Wong, H. P. (2013). Stochastic learning in oxide binary synaptic device for neuromorphic computing. Frontiers in Neuroscience, 7. doi:10.3389/fnins.2013.00186