  In this paper, we present an efficient hardware mapping methodology for
realizing vector matrix multiplication (VMM) on resistive memory (RRAM) arrays.
Using the proposed VMM computation technique, we experimentally demonstrate a
binarized-ADALINE (Adaptive Linear) classifier on an OxRAM crossbar. An 8x8
OxRAM crossbar with Ni/3-nm HfO2/7 nm Al-doped-TiO2/TiN device stack is used.
Weight training for the binarized-ADALINE classifier is performed ex-situ on
UCI cancer dataset. Post weight generation the OxRAM array is carefully
programmed to binary weight-states using the proposed weight mapping technique
on a custom-built testbench. Our VMM powered binarized-ADALINE network achieves
a classification accuracy of 78% in simulation and 67% in experiments.
Experimental accuracy was found to drop mainly due to crossbar inherent
sneak-path issues and RRAM device programming variability.
