  Hexagonal CNN models have shown superior performance in applications such as
IACT data analysis and aerial scene classification due to their better rotation
symmetry and reduced anisotropy. In order to realize hexagonal processing,
existing studies mainly use the ZeroOut method to imitate hexagonal processing,
which causes substantial memory and computation overheads. We address this
deficiency with a novel native hexagonal CNN framework named HexCNN. HexCNN
takes hexagon-shaped input and performs forward and backward propagation on the
original form of the input based on hexagon-shaped filters, hence avoiding
computation and memory overheads caused by imitation. For applications with
rectangle-shaped input but require hexagonal processing, HexCNN can be applied
by padding the input into hexagon-shape as preprocessing. In this case, we show
that the time and space efficiency of HexCNN still outperforms existing
hexagonal CNN methods substantially. Experimental results show that compared
with the state-of-the-art models, which imitate hexagonal processing but using
rectangle-shaped filters, HexCNN reduces the training time by up to 42.2%.
Meanwhile, HexCNN saves the memory space cost by up to 25% and 41.7% for
loading the input and performing convolution, respectively.
