Learning with memristive devices: How should we model their behavior?

Authors:
Damien Querlioz;Philippe Dollfus;Olivier Bichler;Christian Gamrat
Affiliations:
Institut d'Electronique Fondamentale, Univ. Paris-Sud, CNRS, Orsay, France;Institut d'Electronique Fondamentale, Univ. Paris-Sud, CNRS, Orsay, France;CEA, LIST, Embedded Computers Laboratory, Gif-Sur-Yvette, France;CEA, LIST, Embedded Computers Laboratory, Gif-Sur-Yvette, France
Venue:
NANOARCH '11 Proceedings of the 2011 IEEE/ACM International Symposium on Nanoscale Architectures
Year:
2011

Citing 5
Cited 3

Hybrid CMOS/nanoelectronic digital circuits: devices, architectures, and design automation

ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Defect-tolerant nanoelectronic pattern classifiers: Research Articles

International Journal of Circuit Theory and Applications - Nanoelectronic Circuits
Spike-timing-dependent learning in memristive nanodevices

NANOARCH '08 Proceedings of the 2008 IEEE International Symposium on Nanoscale Architectures
The brain of a new machine

IEEE Spectrum
Array-based architecture for FET-based, nanoscale electronics

IEEE Transactions on Nanotechnology

2012 Special Issue: Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity

Neural Networks
Pavlov's dog associative learning demonstrated on synaptic-like organic transistors

Neural Computation
Robust learning approach for neuro-inspired nanoscale crossbar architecture

ACM Journal on Emerging Technologies in Computing Systems (JETC) - Special Issue on Reliability and Device Degradation in Emerging Technologies and Special Issue on WoSAR 2011

Quantified Score

Hi-index	0.02

Visualization

Abstract

This work discusses the modeling of memristive devices, for architectures where they are used as synapses. It is shown that the most common models used in this context do not always accurately reflect the actual behavior of popular devices in pulse regime. We introduce a new behavioral model, intended towards the nanoarchitecture community. It fits the conductance evolution of Univ. Michigan's synaptic memristive devices. A variation of the model fits HP labs's memristors' behavior in the same conditions. Finally, we discuss using a simple example the importance of this type of modeling for learning architectures and how it can impact the behavior of the learning.