  Capsule Networks (CapsNets), recently proposed by the Google Brain team, have
superior learning capabilities in machine learning tasks, like image
classification, compared to the traditional CNNs. However, CapsNets require
extremely intense computations and are difficult to be deployed in their
original form at the resource-constrained edge devices. This paper makes the
first attempt to quantize CapsNet models, to enable their efficient edge
implementations, by developing a specialized quantization framework for
CapsNets. We evaluate our framework for several benchmarks. On a deep CapsNet
model for the CIFAR10 dataset, the framework reduces the memory footprint by
6.2x, with only 0.15% accuracy loss. We will open-source our framework at
https://git.io/JvDIF in August 2020.

Machine Learning;Machine Learning