Experimental demonstration of short and long term synaptic plasticity using OxRAM multi k-bit arrays for reliable detection in highly noisy input data
| Description | |
| Date | |
| Authors | Werner T., Vianello, Bichler O., Grossi A., Nowak E., Nodin J.-F., Yvert B., Desalvo B., Perniola L. |
| Year | 2017-0125 |
| Source-Title | Technical Digest - International Electron Devices Meeting, IEDM |
| Affiliations | CEA, LETI, Minatec Campus, Grenoble, France, CEA, LIST, Saclay, France, INSERM, Clinatec, France |
| Abstract | In this paper, we propose a new circuit architecture and a reading/programming strategy to emulate both Short and Long Term Plasticity (STP, LTP) rules using non-volatile OxRAM cells. For the first time, we show how the intrinsic OxRAM device switching probability at ultra-low power can be exploited to implement STP as well as LTP learning rules. Moreover, we demonstrate the computational power that STP can provide for reliable signal detection in highly noisy input data. A Fully Connected Neural Network incorporating STP and LTP learning rules is used to demonstrate two applications: (i) visual pattern extraction and (ii) decoding of neural signals. A high accuracy is obtained even in presence of significant background noise in the input data. © 2016 IEEE. |
| Author-Keywords | |
| Index-Keywords | Electron devices, Input output programs, Background noise, Circuit architectures, Computational power, Experimental demonstrations, Fully connected neural network, Reliable detection, Switching probability, Synaptic plasticity, Signal detection |
| ISSN | 1631918 |
| Link | Link |