Name
Abstract | ||
|---|---|---|
In this paper, we show how metal-oxide (OxRAM) based nanoscale memory devices can be exploited to design low-power Extreme Learning Machine (ELM) architectures. In particular we fabricated HfO2 and TiO2 based OxRAM devices, and exploited their intrinsic resistance spread characteristics to realize ELM hidden layer weights and neuron biases. To validate our proposed OxRAM-ELM architecture, full-scale learning and multi-class classification simulations were performed for two complex datasets: (i) Land Satellite images and (ii) Image segmentation. Dependence of classification performance on neuron gain parameter and OxRAM device properties was studied in detail. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | multi-class classification, OxRAM, memristive devices, extreme learning machine, nanoarchitecture |
Field | DocType | ISSN |
Gain parameter,Architecture,MATLAB,Pattern recognition,Computer science,Extreme learning machine,Device Properties,Image segmentation,Artificial intelligence,Machine learning,Multiclass classification | Conference | 2161-4393 |
Citations | PageRank | References |
2 | 0.42 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| manan suri | 1 | 10 | 7.84 |
| vivek parmar | 2 | 8 | 5.42 |
| Gilbert Sassine | 3 | 2 | 0.42 |
| Fabien Alibart | 4 | 169 | 14.94 |