期刊
FRONTIERS IN NEUROSCIENCE
卷 7, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2013.00186
关键词
resistive switching; oxide RRAM; synaptic device; binary synapse; neurommphic computing; stochastic learning; switching variability
资金
- Samsung Global Research Outreach (GRO) program
- Nanoelectronics Research Initiative (NRI) of the Semiconductor Research Corporation (SRC) through the NSF/NRI
- member companies of the Stanford Non-Volatile Memory Technology Research Initiative (NMTRI)
- SONIC Center
- Semiconductor Technology Advanced Research Network (STARnet)
- SRC subsidiary
- 973 Program in China [2011CBA00602]
- 1000-plan funding in China
- Stanford School of Engineering China Research Exchange Program
- Stanford Graduate Fellowship
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. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning. The simulation illustrates that with such stochastic learning, the orientation classification function of input patterns can be effectively realized. The system performance metrics were compared between the conventional approach using the analog synapse and the approach in this work that employs the binary synapse utilizing the stochastic learning. The feasibility of using binary synapse in the neurormorphic computing may relax the constraints to engineer continuous multilevel intermediate states and widens the material choice for the synaptic device design.
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