  Recently several structured pruning techniques have been introduced for
energy-efficient implementation of Deep Neural Networks (DNNs) with lesser
number of crossbars. Although, these techniques have claimed to preserve the
accuracy of the sparse DNNs on crossbars, none have studied the impact of the
inexorable crossbar non-idealities on the actual performance of the pruned
networks. To this end, we perform a comprehensive study to show how highly
sparse DNNs, that result in significant crossbar-compression-rate, can lead to
severe accuracy losses compared to unpruned DNNs mapped onto non-ideal
crossbars. We perform experiments with multiple structured-pruning approaches
(such as, C/F pruning, XCS and XRS) on VGG11 and VGG16 DNNs with benchmark
datasets (CIFAR10 and CIFAR100). We propose two mitigation approaches -
Crossbar column rearrangement and Weight-Constrained-Training (WCT) - that can
be integrated with the crossbar-mapping of the sparse DNNs to minimize accuracy
losses incurred by the pruned models. These help in mitigating non-idealities
by increasing the proportion of low conductance synapses on crossbars, thereby
improving their computational accuracies.
