Bio-inspired programming of resistive memory devices for implementing spiking neural networks
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| Date | |
| Authors | Vianello E., Werner T., Grossi A., Nowak E., De Salvo B., Perniola L., Bichler O., Yvert B. |
| Year | 2017-0227 |
| Source-Title | Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI |
| Affiliations | CEA, LETI, Minatec Campus, Grenoble, France, CEA, LIST, Saclay, France, INSERM, Clinatec, UA01, France |
| Abstract | In this work, we will focus on the role that non-volatile resistive memory technologies (RRAM) can play for modeling key features of biological synapses. We will present an architecture and a reading/programming strategy to emulate both Short and Long Term Plasticity (STP, LTP) rules using non-volatile OxRAM arrays. A visual-pattern extraction application is discussed using spiking neural networks. We demonstrated that Long-Term plasticity allows the neural networks to learn patterns and the Short Term plasticity allows to improve accuracy (reduction of the false positive events generated by white noise in the input data) in presence of significant background noise in the input data. © 2017 ACM. |
| Author-Keywords | Artificial synapses, Long-term plasticity, RRAM, Short-term plasticity, Spiking Neural Networks, Unsupervised learning |
| Index-Keywords | Input output programs, Neural networks, RRAM, Unsupervised learning, VLSI circuits, White noise, Artificial synapses, Background noise, Biological synapsis, False positive, Resistive memory, Short term plasticity, Spiking neural networks, Visual pattern, Random access storage |
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| Link | Link |