Bioinspired Programming of Resistive Memory Devices for Implementing Spiking Neural Networks
Authors: 
Elisa Vianello, Thilo Werner, Alessandro Grossi, Etienne Nowak, Barbara De Salvo, Luca Perniola, Olivier Bichler, Blaise YvertAuthors Info & Claims
Elisa Vianello, Thilo Werner, Alessandro Grossi, Etienne Nowak, Barbara De Salvo, Luca Perniola, Olivier Bichler, Blaise YvertAuthors Info & ClaimsPages 393 - 398
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.
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Index Terms
- Bioinspired Programming of Resistive Memory Devices for Implementing Spiking Neural Networks
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May 2017
516 pages
ISBN:9781450349727
DOI:10.1145/3060403
- General Chairs:
- Laleh Behjat,
- Jie Han,
- Program Chairs:
- Miroslav N. Velev,
- Deming Chen
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