  In this paper, we firstly introduce a method to efficiently implement
large-scale high-dimensional convolution with realistic memristor-based circuit
components. An experiment verified simulator is adapted for accurate prediction
of analog crossbar behavior. An improved conversion algorithm is developed to
convert convolution kernels to memristor-based circuits, which minimizes the
error with consideration of the data and kernel patterns in CNNs. With circuit
simulation for all convolution layers in ResNet-20, we found that 8-bit ADC/DAC
is necessary to preserve software level classification accuracy.
