Simulation of a memristor-based spiking neural network immune to device variations

Damien Querlioz (1) , Olivier Bichler (2) , Christian Gamrat (3)

1 IEF - Institut d'électronique fondamentale
2 LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies
3 LCEI - Laboratoire Calculateurs Embarqués et Image

— We propose a design methodology to exploit adaptive nanodevices (memristors), virtually immune to their variability. Memristors are used as synapses in a spiking neural network performing unsupervised learning. The memristors learn through an adaptation of spike timing dependent plasticity. Neurons' threshold is adjusted following a homeostasis-type rule. System level simulations on a textbook case show that performance can compare with traditional supervised networks of similar complexity. They also show the system can retain functionality with extreme variations of various memristors' parameters, thanks to the robustness of the scheme, its unsupervised nature, and the power of homeostasis. Additionally the network can adjust to stimuli presented with different coding schemes.

Domaines: Sciences de l'ingénieur [physics]

Damien Querlioz, Olivier Bichler, Christian Gamrat. Simulation of a memristor-based spiking neural network immune to device variations. 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose), Jul 2011, San Jose, United States. ⟨10.1109/IJCNN.2011.6033439⟩. ⟨hal-01827055⟩