  The analog nature of computing in Memristive crossbars poses significant
issues due to various non-idealities such as: parasitic resistances, non-linear
I-V characteristics of the device etc. The non-idealities can have a
detrimental impact on the functionality i.e. computational accuracy of
crossbars. Past works have explored modeling the non-idealities using
analytical techniques. However, several non-idealities have data dependent
behavior. This can not be captured using analytical (non data-dependent) models
thereby, limiting their suitability in predicting application accuracy.
  To address this, we propose a Generalized Approach to Emulating Non-Ideality
in Memristive Crossbars using Neural Networks (GENIEx), which accurately
captures the data-dependent nature of non-idealities. We perform extensive
HSPICE simulations of crossbars with different voltage and conductance
combinations. Following that, we train a neural network to learn the transfer
characteristics of the non-ideal crossbar. Next, we build a functional
simulator which includes key architectural facets such as \textit{tiling}, and
\textit{bit-slicing} to analyze the impact of non-idealities on the
classification accuracy of large-scale neural networks. We show that GENIEx
achieves \textit{low} root mean square errors (RMSE) of $0.25$ and $0.7$ for
low and high voltages, respectively, compared to HSPICE. Additionally, the
GENIEx errors are $7\times$ and $12.8\times$ better than an analytical model
which can only capture the linear non-idealities. Further, using the functional
simulator and GENIEx, we demonstrate that an analytical model can overestimate
the degradation in classification accuracy by $\ge 10\%$ on CIFAR-100 and
$3.7\%$ on ImageNet datasets compared to GENIEx.
