  How to improve the efficiency of routing procedures in CapsNets has been
studied a lot. However, the efficiency of capsule convolutions has largely been
neglected. Capsule convolution, which uses capsules rather than neurons as the
basic computation unit, makes it incompatible with current deep learning
frameworks' optimization solution. As a result, capsule convolutions are
usually very slow with these frameworks. We observe that capsule convolutions
can be considered as the operations of `multiplication of multiple small
matrics' plus tensor-based combination. Based on this observation, we develop
two acceleration schemes with CUDA APIs and test them on a custom CapsNet. The
result shows that our solution achieves a 4X acceleration.
