Hardware-aware Training Techniques for Improving Robustness of Ex-Situ
  Neural Network Transfer onto Passive TiO2 ReRAM Crossbars
Philippe Drolet Raphaël Dawant Victor Yon Pierre-Antoine Mouny Matthieu Valdenaire Javier Arias Zapata Pierre Gliech Sean U. N. Wood Serge Ecoffey Fabien Alibart Yann Beilliard Dominique Drouin
Passive resistive random access memory (ReRAM) crossbar arrays, a promising
emerging technology used for analog matrix-vector multiplications, are far
superior to their active (1T1R) counterparts in terms of the integration
density. However, current transfers of neural network weights into the
conductance state of the memory devices in the crossbar architecture are
accompanied by significant losses in precision due to hardware variabilities
such as sneak path currents, biasing scheme effects and conductance tuning
imprecision. In this work, training approaches that adapt techniques such as
dropout, the reparametrization trick and regularization to TiO2 crossbar
variabilities are proposed in order to generate models that are better adapted
to their hardware transfers. The viability of this approach is demonstrated by
comparing the outputs and precision of the proposed hardware-aware network with
those of a regular fully connected network over a few thousand weight transfers
using the half moons dataset in a simulation based on experimental data. For
the neural network trained using the proposed hardware-aware method, 79.5% of
the test set's data points can be classified with an accuracy of 95% or higher,
while only 18.5% of the test set's data points can be classified with this
accuracy by the regularly trained neural network.