  We introduce Multi-Lane Capsule Networks (MLCN), which are a separable and
resource efficient organization of Capsule Networks (CapsNet) that allows
parallel processing, while achieving high accuracy at reduced cost. A MLCN is
composed of a number of (distinct) parallel lanes, each contributing to a
dimension of the result, trained using the routing-by-agreement organization of
CapsNet. Our results indicate similar accuracy with a much reduced cost in
number of parameters for the Fashion-MNIST and Cifar10 datsets. They also
indicate that the MLCN outperforms the original CapsNet when using a proposed
novel configuration for the lanes. MLCN also has faster training and inference
times, being more than two-fold faster than the original CapsNet in the same
accelerator.
