Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks. (arXiv:2201.05229v1 [cs.LG])

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 …

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