  There is widespread interest in emerging technologies, especially resistive
crossbars for accelerating Deep Neural Networks (DNNs). Resistive crossbars
offer a highly-parallel and efficient matrix-vector-multiplication (MVM)
operation. MVM being the most dominant operation in DNNs makes crossbars
ideally suited. However, various sources of device and circuit non-idealities
lead to errors in the MVM output, thereby reducing DNN accuracy. Towards that
end, we propose crossbar re-mapping strategies to mitigate line-resistance
induced accuracy degradation in DNNs, without having to re-train the learned
weights, unlike most prior works. Line-resistances degrade the voltage levels
along the crossbar columns, thereby inducing more errors at the columns away
from the drivers. We rank the DNN weights and kernels based on a sensitivity
analysis, and re-arrange the columns such that the most sensitive kernels are
mapped closer to the drivers, thereby minimizing the impact of errors on the
overall accuracy. We propose two algorithms $-$ static remapping strategy (SRS)
and dynamic remapping strategy (DRS), to optimize the crossbar re-arrangement
of a pre-trained DNN. We demonstrate the benefits of our approach on a standard
VGG16 network trained using CIFAR10 dataset. Our results show that SRS and DRS
limit the accuracy degradation to 2.9\% and 2.1\%, respectively, compared to a
5.6\% drop from an as it is mapping of weights and kernels to crossbars. We
believe this work brings an additional aspect for optimization, which can be
used in tandem with existing mitigation techniques, such as in-situ
compensation, technology aware training and re-training approaches, to enhance
system performance.
