Convolutional Neural Networks (CNNs) have produced state-of-the-art results
for image classification tasks. However, they are limited in their ability to
handle rotational and viewpoint variations due to information loss in
max-pooling layers. Capsule Networks (CapsNets) employ a
computationally-expensive iterative process referred to as dynamic routing to
address these issues. CapsNets, however, often fall short on complex datasets
and require more computational resources than CNNs. To overcome these
challenges, we introduce the Parallel Dynamic Routing CapsNet (PDR-CapsNet), a
deeper and more energy-efficient alternative to CapsNet that offers superior
performance, less energy consumption, and lower overfitting rates. By
leveraging a parallelization strategy, PDR-CapsNet mitigates the computational
complexity of CapsNet and increases throughput, efficiently using hardware
resources. As a result, we achieve 83.55\% accuracy while requiring 87.26\%
fewer parameters, 32.27\% and 47.40\% fewer MACs, and Flops, achieving 3x
faster inference and 7.29J less energy consumption on a 2080Ti GPU with 11GB
VRAM compared to CapsNet and for the CIFAR-10 dataset.

通过并行动态路由 CapsNet（PDR-CapsNet）提出了一种比 CapsNet 更深，更节能，性能更好，能够更高效利用硬件资源的替代方法，通过减少参数，降低计算复杂度以及增加吞吐量，从而实现了对 CIFAR-10 数据集的 3 倍更快推理时间和 7.29J 更少的能量消耗。