  The quantization of weights to binary states in Deep Neural Networks (DNNs)
can replace resource-hungry multiply accumulate operations with simple
accumulations. Such Binarized Neural Networks (BNNs) exhibit greatly reduced
resource and power requirements. In addition, memristors have been shown as
promising synaptic weight elements in DNNs. In this paper, we propose and
simulate novel Binarized Memristive Convolutional Neural Network (BMCNN)
architectures employing hybrid weight and parameter representations. We train
the proposed architectures offline and then map the trained parameters to our
binarized memristive devices for inference. To take into account the variations
in memristive devices, and to study their effect on the performance, we
introduce variations in $R_{ON}$ and $R_{OFF}$. Moreover, we introduce means to
mitigate the adverse effect of memristive variations in our proposed networks.
Finally, we benchmark our BMCNNs and variation-aware BMCNNs using the MNIST
dataset.
