OXRAM based ELM architecture for multi-class classification applications

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.